To perform a systematic study of data, we use data science life cycle to perform testable methods to make predictions.
Before you apply science to data, you must be aware of the important steps. A data science life cycle will help you get a clear understanding of the end-to-end actions of a data scientist. It provides us with a framework to fulfill business requirements using data science tools and technologies.
Follow these steps to accomplish your data science life cycle
In this blog, we will study the iterative steps used to develop, deliver, and maintain any data science product.
6 steps of data science project life cycle – Data Science Dojo
1. Problem identification
Let us say you are going to work on a project in the healthcare industry. Your team has identified that there is a problem of patient data management in this industry, and this is affecting the quality of healthcare services provided to patients.
Before you start your data science project, you need to identify the problem and its effects on patients. You can do this by conducting research on various sources, including:
Online forums
Social media (Twitter and Facebook)
Company websites
Understanding the aim of analysis to extract data is mandatory. It sets the direction to use data science for the specific task. For instance, you need to know if the customer is willing to minimize savings loss or prefers to predict the rate of a commodity.
To be precise, in this step we answer the following questions:
Clearly state the problem to be solved
Reason to solve the problem
State the potential value of the project to motivate everyone
Identify the stakeholders and risks associated with the project
Perform high-level research with your data science team
To complete this step, you need to dive into the enterprise’s data collection methods and data repositories. It Is important to gather all the relevant and required data to maintain the quality of research. Data scientists contact the enterprise group to apprehend the available data.
Data study and data investigation – Data Science Dojo
In this step, we:
Describe the data
Define its structure
Figure out relevance of data and
Assess the type of data record
Here you need to intently explore the data to find any available information related to the problem. Because the historical data present in the archive contributes to better understanding of business.
In any business, data collection is a continual process. At various steps, information on key stakeholders is recorded in various software systems. To study that data to successfully conduct a data science project it is important to understand the process followed from product development to deployment and delivery.
Also, data scientists also use many statistical methods to extract critical data and derive meaningful insights from it.
3. Pre-processing of data
Organizing the scattered data of any business is a pre-requisite to data exploration. First, we gather data from multiple sources in various formats, then convert the data into a unified format for smooth data processing.
All the data processing happens in a data warehouse, in which data scientists together extract, transform and load (ETL) the data. Once the data is collected, and the ETL process is completed, data science operations are carried out.
It is important to realize the role of the ETL process in every data science project. Also, a data architect contributed widely at the stage of pre-processing data as they decide the structure of the data warehouse and perform the steps of ETL operations.
The actions to be performed at this stage of a data science project are:
Selection of the applicable data
Data integration by means of merging the data sets
Data cleaning and filtration of relevant information
Treating the lacking values through either eliminating them or imputing them
Treating inaccurate data through eliminating them
Additionally, test for outliers the use of box plots and cope with them
This step also emphasizes the importance of elements essential to constructing new data. Often, we are mistaken to start data research for a project from scratch. However, data pre-processing suggests us to construct new data by refining the existing information and eliminating undesirable columns and features.
Data preparation is the most time-consuming but the most essential step in the complete existence cycle. Your model will be as accurate as your data.
4. Exploratory data analysis
Applause to us! We now have the data ready to work on. At this stage make sure that you have the data in your hands in the required format. Data analysis is carried out by using various statistical tools. Support of data engineer is crucial in data analysis. They perform the following steps to conduct the Exploratory Data Analysis:
Examine the data by formulating the various statistical functions
Identify dependent and independent variables or features
Analyze key features of data to work on
Define the spread of data
Moreover, for thorough data analysis, various plots are utilized to visualize the data for better understanding for everyone. Data scientists explore the distribution of data inside distinctive variables of a character graphically by the usage of bar graphs. Not only this but relations between distinct aspects are captured via graphical representations like scatter plots and warmth maps.
The instruments like Tableau, PowerBI and so on are well known for performing Exploratory Data Analysis and Visualization. Information on Data Science with Python and R is significant for performing EDA on an information.
5. Data modeling
Data modeling refers to the process of converting raw data into a form that can be transverse into other applications as well. Mostly, this step is performed in spreadsheets, but data scientists also prefer to use statistical tools and databases for data modeling.
The following elements are required for data modeling:
Data dictionary: A list of all the properties describing your data that you want to maintain in your system, for example, spreadsheet, database, or statistical software.
Entity relationship diagram: This diagram shows the relationship between entities in your data model. It shows how each element is related to the other, as well as any constraints to that relationship
Data model: A set of classes representing each piece of information in your system, along with its attributes and relationships with other objects in the system.
The Machine Learning engineer applies different algorithms to the information and delivers the result. While demonstrating the information numerous multiple times, the models are first tried on fake information like genuine information.
6. Model evaluation/ Monitoring
Before we learn what, model evaluation is all about, we need to know that model evaluation can be done parallel to the other stages of the data science life cycle. It helps you to know at every step if your model is working as intended or if you need to make any changes. Alongside, eradicate any error at an early stage to avoid getting false predictions at the end of the project.
In case you fail to acquire a quality result in the evaluation, we must reiterate the complete modeling procedure until the preferred stage of metrics is achieved.
As we assess the model towards the end of project, there might be changes in the information, however, the result will change contingent upon changes in information. Thus, while assessing the model the following two stages are significant
Data drift analysis:
Data drift refers to the changes in the input information. Data drift analysis is a feature in data science that highlights the changes in the information along with the circumstance. Examination of this change is called Data Drift Analysis. The accuracy of the model relies heavily on how well it handles this information float. The progressions in information are significantly a direct result of progress in factual properties of information.
Model drift analysis
We use drift machine learning techniques to find the information. Additionally, more complex techniques like Adaptive Windowing, Page Hinkley, and so on are accessible for use. Demonstrating Drift Analysis is significant as we realize change is quick. Steady advancement likewise can be utilized where the model is presented to added information gradually.
Start your data science project today
Data science life cycle is a collection of individual steps that need to be taken to prepare for and execute a data science project. The steps include identifying the project goals, gathering relevant data, analyzing it using appropriate tools and techniques, and presenting results in a meaningful way. It is not an effortless process, but with some planning and preparation you can make it much easier on yourself.
6 steps of data science project life cycle – Data Science Dojo
Data analysis,data investigation,data modeling,Data processing,data science life cycle,data science phases,data science project,data science steps
Data Science Dojo is offering DBT for FREE on Azure Marketplace packaged with support for various data warehouses and data lakes to be configured from CLI.
What does DBT stands for?
Traditionally, data engineers had to process extensive data available at multiple data clouds in the same available cloud environments. The next task was to migrate the data and then transform it as per the requirements, but Data migration was a task not easy to do so. DBT short for Data Build Tool, allows the analysts and engineers to manipulate massive amounts of data from various significant cloud warehouses to be processed reliably at a single workstation using modular SQL.
It is basically the “T” in ELT for data transformation in diverse data warehouses.
ELT vs ETL – Insights of both terms
Now what do these two terms mean? Have a look at the table below:
ELT
ETL
1.
Stands for Extraction Load Transform
Stands for Extraction Transform Load
2.
Supports structured, unstructured, semi structured and raw type of data
Requires relational and structured dataset
3.
New technology, so it’s difficult to find experts or to create data pipelines
Old process, used for over 20 years now
4.
Dataset is extracted from sources and warehoused in the destination and then transformed
After extraction, data is brought into the staging area where’s its transformed and then loaded into target system
5.
Quick data loading time because data is integrated at target system once and then transformed
Takes more time as it’s a multistage process involving a staging area for transformation and twice loading operations
Use cases for ELT
Since dbt relates closely to ELT process, let’s discuss its use cases:
Associations with huge volumes of information: Meteorological frameworks like weather forecasters gather, examine and utilize a lot of information consistently. Organizations with enormous exchange volumes additionally fall into this classification. The ELT process considers faster exchange of data
Associations needing quick accessibility: Stock trades produce and utilize a lot of data continuously, where postponements can be destructive.
Challenges for Data Build Tool (DBT)
Data distributed across multiple data centers and the ability to transform those volumes at a single place was a big challenge.
Then testing and documenting the workflow was another problem.
Therefore, an engine that could cater to the multiple disjointed data warehouses for data transformation would be suitable for the data engineers. Additionally, testing the complex data pipeline with the same agent would do wonders.
Working of DBT
Data Build Tool is a partially open-source platform for transforming and modeling data obtained from your data warehouses all in one place. It allows the usage of simple SQL to manipulate data acquired from different sources. Users can document their files and can generate DAG diagrams thereby identifying the lineage of workflow using dbt docs. Automated tests can be run to detect flaws and missing entries in the data models as well. Ultimately, you can deploy the transformed data model to any other warehouse. DBT serves pleasantly in the cutting-edge information stack and is considered cloud agnostic meaning it operates with several significant cloud environments.
DBT enables data analysts with the feasibility to take over the task of data engineers. With modular SQL at hand, analysts can take ownership of data transformation and eventually create visualizations upon it
It’s cloud agnostic which means that DBT can handle multiple significant cloud environments with their warehouses such as BigQuery, Redshift, and Snowflake to process mission-critical data
Users can maintain a profile specifying connections to different data sources along with schema and threads
Users can document their work and can generate DAG diagrams to visualize their workflow
Through the snapshot feature, you can take a copy of your data at any point in time for a variety of reasons such as tracing changes, time intervals, etc.
What Data Science Dojo has for you
DBT instance packaged by Data Science Dojo comes with pre-installed plugins which are ready to use from CLI without the burden of installation. It provides the flexibility to connect with different warehouses, load the data, transform it using analysts’ favorite language – SQL and finally deploy it to the data warehouse again or export it to data analysis tools.
Ubuntu VM having dbt Core installed to be used from Command Line Interface (CLI)
Database: PostgreSQL
Support for BigQuery
Support for Redshift
Support for Snowflake
Robust integrations
A web interface at port 8080 is spun up by dbt docs to visualize the documentation and DAG workflow
Several data models as samples are provided after initiating a new project
This dbt offer is compatible with the following cloud providers:
GCP
Snowflake
AWS
Disclaimer: The service in consideration is the free open-source version which operates from CLI. The paid features as stated officially by DBT are not endorsed in this offer.
Conclusion
Incoherent sources, data consistency problems, and conflicting definitions for measurements and enterprise details lead to disarray, excess endeavors, and unfortunate data being dispersed for decision-making. DBT resolves all these issues. It was built with version control in mind. It has enabled data analysts to take on the role of data engineers. Any developer with good SQL skills is able to operate on the data – this is in fact the beauty of this tool.
At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. Therefore, to enhance your data engineering and analysis skills and make the most out of this tool, use the Data Science Bootcamp by Data Science Dojo, your ideal companion in your journey to learn data science!
Click on the button below to head over to the Azure Marketplace and deploy DBT for FREE by clicking on “Get it now”.
Note: You’ll have to sign up to Azure, for free, if you do not have an existing account.
data build tool,Data engineering,Data models,DBT,ELT
For a 21st-century professional, having proven analytical skills is increasingly important. Companies all over the world have started to push data scientists to participate in leading data science competitions. Businesses now emphasize all their employees to gain analytical skillsets, regardless of their department.
One of the best ways to prove that you have a strong grip on analytics/ data science skills is to take part in reputable competitions that test these to show your employer that you have the required skill set.
There are many competitions these days, so it can get overwhelming trying to figure out which ones are worth your time. If you are not sure where to begin, or which ones to take part in, here are a few notable ones to help you get started.
Participating in data science competitions – Data Science Dojo
1. Kaggle
Kaggle is the most popular platform for practicing data science skills. It hosts multiple popular datasets, and regularly has competitions where anyone can participate to build the best machine learning models and compete against others working on the same dataset.
The IBM call for code competition asks for contributions across several different areas in order to solve real world challenges. There are currently 4 areas in 2022 where you can get involved and build solutions:
Machine hack is a community that hosts competitions or hackathons for data science and AI enthusiasts. There are a wide variety of challenges available from the data science pipeline, from machine learning to data visualization. You can also win cash prizes for some of the challenges.
4. DataCamp:
DataCamp has weekly competitions on their website. Each competition has a cash prize associated with it as well. You can submit your solutions, and vote on the best solutions from other participants as well
5. DrivenData:
DrivenData provides a platform for data scientists who want to make a social impact with their work. The challenges on the platform focus on solving social issues through data science.
These challenges include things like predicting public health risks at restaurants, identifying endangered species in images, and matching students to schools where they are likely to succeed. The winning code gets a prize, and gets published under an open source license for others to benefit as well
Are you excited to participate in data science competitions?
All of the above-mentioned data science competitions allow you to gain hands-on learning of data science skills. It offers a platform to the learner for improving problem-solving skills and proving their abilities in the competitive market.
Not only participating in these competitions helps you stand out, but these also let you brainstorm innovative ideas for the future.
data science competitions,datacamp,drivendata,IBM call for code,Kaggle,Machine hack
Most people often think of JavaScript (JS) as just a programming language; however, JavaScript, as well as JavaScript frameworks, JavaScript code have multiple applications besides web applications. That includes mobile applications, desktop applications, backend development, and embedded systems.
Looking around, you might also discover that a growing number of developers are leveraging JavaScript frameworks to learn new machine learning (ML) applications. JS frameworks, like Node JS, are capable of developing and running various machine learning models and concepts.
BrainJS is a fast-running JavaScript-written library for neural networking and machine learning. Developers can use this library in both NodeJS and the web browser. BrainJS offers various kinds of networks for various tasks. It is fast and easy to use as it performs computations with the help of GPU.
If GPU isn’t available, BrainJS falls back to pure JS and continues computation. It offers numerous implementations on a neural network and encourages developing and building these neural nets on the server side with NodeJS. That is a major reason why a development agencyuses this library for the simple execution of their machine learning projects.
Pros:
BrainJS helps create interesting functionality using fewer code lines and a reliable dataset.
The library can also operate on client-side JavaScript.
It’s a great library for quick development of a simple NN (Neural Network) wherein you can reap the benefits of accessing the wide variety of open-source libraries.
Cons:
There is not much possibility for a softmax layer or other such structures.
It restricts the developer’s network architecture and only allows simple applications.
Cracking captcha with neural networks is a good example of a machine learning application that uses BrainJS.
TensorflowJS is a hardware-accelerated open-sourced cross platform to develop and implement deep learning and machine learning models. The library makes it easy for you to utilize flexible APIs for developing models with the help of high-level layer API or low-level JS linear algebra. That is what makes TensorflowJS a popular library for every JavaScript project that is based on ML.
There are an array of guides and tutorials on this library on its official website. It even offers model converters for running the pre-existing Tensorflow models under JavaScript or in the web browser directly. The developers also get the option to convert default Tensorflow models into certain Python models.
Pros:
TensorflowJS can be implemented on several hardware machines, from computers to cellular devices with complicated setups
It offers quick updates, frequent new features, releases, and seamless performance
Developed by MIT, Synaptic is another popular JavaScript-based library for machine learning. It is known for its pre-manufactured structure and general architecture-free algorithm. This feature makes it convenient for developers to train and build any kind of second or first-order neural net architecture.
Developers can use this library easily if they don’t know comprehensive details about machine learning techniques and neural networks. Synaptic also helps import and export ML models using JSON format. Besides, it comes with a few interesting pre-defined networks such as multi-layer perceptions, Hopfield networks, and LSTMs (long short-term memory networks).
Pros:
Synaptic can develop recurrent and second-order networks.
It features pre-defined networks.
There’s documentation available for layers, networks, neurons, architects, and trainers.
Cons:
Synaptic isn’t maintained actively anymore.
It has a slow runtime compared to the other libraries.
MLJS is a general-purpose, comprehensive JavaScript machine learning library that makes ML approachable for all target audiences. The library provides access to machine learning models and algorithms in web browsers. However, the developers who want to work with MLJS in the JS environment can add their dependencies.
MLJS offers mission-critical and straightforward utilities and models for unsupervised and supervised issues. It’s an easy-to-use, open-source library that can handle memory management in ML algorithms and GPU-based mathematical operations. The library supports other routines, too, like hash tables, arrays, statistics, cross-validation, linear algebra, etc.
Pros:
MLJS provides a routine for array manipulation, optimizations, and algebra
It facilitates BIT operations on hash tables, arrays, and sorting
MLJS extends support to cross-validation
Cons:
MLJS doesn’t offer default file system access in the host environment of the web browser
NeuroJS is another good JavaScript-based library to develop and train deep learning models majorly used in creating chatbots and AI technologies. Several developers leverage NeuroJS to create and train ML models and implement them in NodeJS or the web application.
A major advantage of the NeuroJS library is that it provides support for real-time classification, online learning, and classification of multi-label forms while developing machine learning projects. The simple and performance-driven nature of this library makes machine learning practical and accessible to those using it.
Pros:
NeuroJS offers support for online learning and reinforcement learning
High-performance
It also supports the classification of multi-label forms
Cons:
NeuroJS does not support backpropagation and LSTM through time
A good example of NeuroJS being used along with React can be discovered here.
Stdlib is a large JavaScript-based library used to create advanced mathematical models and ML libraries. Developers can also use this library to conduct graphics and plotting functionalities for data analysis and data visualization.
You can use this library to develop scalable, and modular APIs for other developers and yourself within minutes, sans having to tackle gateways, servers, domains, build SDKs, or write documentation.
Pros:
Stdlib offers robust, and rigorous statistical and mathematical functions
It comes with auto-generated documentation
The library offers easy-API access control and sharing
Cons:
Stdlib doesn’t support developing project builds that don’t feature runtime assertions.
It does not support computing inverse hyperbolic secant.
Main, mk-stack, and From the Farmer, are three companies that reportedly use Stdlib in their technology stack.
KerasJS is a renowned neural network JavaScript library used to develop and prepare profound deep learning and machine learning models. The models developed using Keras are mostly run in a web application. However, to run the models, you can only use CPU mode for it. There won’t be any GPU acceleration.
Keras is known as a JavaScript alternative for AI (Artificial Intelligence) library. Besides, as Keras uses numerous frameworks for backend, it allows you to train the models in TensorFlow, CNTK, and a few other frameworks.
Pros:
Using Keras, models can be trained in any backend
It can exploit GPU support offered by the API of WebGL 3D designs
The library is capable of running Keras models in programs
Cons:
Keras is not that useful if you wish to create your own abstract layer for research purposes
It can only run in CPU mode
A few well-known scientific organizations, likeCERN, andNASA, are using this library for their AI-related projects.
Wrapping up:
This article covers the top five NodeJS libraries you can leverage when exploring machine learning. JavaScript may not be that popular in machine learning and deep learning yet; however, the libraries listed in the article prove that it is not behind the times when it comes to progressing in the machine learning space.
Moreover, a developerhaving and utilizing the correct libraries and tools for machine learning can help you put up algorithms and solutions capable of tapping the various strengths of your machine learning project.
We hope this article helps you learn and use the different libraries listed above in your project.
In this tutorial, you will learn how to create an attractive voice-controlled chatbot application with a small amount of coding in python. To build our application we’ll first create a good-looking user interface through the built-in Tkinter library in Python and then we will create some small functions to achieve our task.
Here is a sneak peek of what we are going to create.
Voice controlled chatbot using coding in Python – Data Science Dojo
Before kicking off, I hope you already have a brief idea about web scraping, if not then read the following article talking about Python web scraping.
PRO-TIP: Join our 5-day instructor-led Python for Data Science training to enhance your deep learning
Pre-requirements for building a voice chatbot
Make sure that you are using Python 3.8+ and the following libraries are installed on it
Pyttsx3 (pyttsx3 is a text-to-speech conversion library in Python)
SpeechRecognition (Library for performing speech recognition)
Requests (The requests module allows you to send HTTP requests using Python)
Bs4 (Beautiful Soup is a library that is used to scrape information from web pages)
pyAudio (With PyAudio, you can easily use Python to play and record audio)
If you are still facing installation errors or incompatibility errors, then you can try downloading specific versions of the above libraries as they are tested and working currently in the application.
Python 3.10
pyttsx3==2.90
SpeechRecognition==3.8.1
requests==2.28.1
beautifulsoup4==4.11.1
beautifulsoup4==4.11.1
Now that we have set everything it is time to get started. Open a fresh new py file and name it VoiceChatbot.py. Import the following relevant libraries on the top of the file.
from tkinter import *
import time
import datetime
import pyttsx3
import speech_recognition as sr
from threading import Thread
import requests
from bs4 import BeautifulSoup
The code is divided into the GUI section, which uses the Tkinter library of python and 7 different functions. We will start by declaring some global variables and initializing instances for text-to-speech and Tkinter. Then we start creating the windows and frames of the user interface.
The user interface
This part of the code loads images initializes global variables, and instances and then it creates a root window that displays different frames. The program starts when the user clicks the first window bearing the background image.
This is the first function that is called inside a thread. It first calls the wishme function to wish the user. Then it checks whether the query variable is empty or not. If the query variable is empty, then it checks the contents of the query variable. If there is a shutdown or quit or stop word in query, then it calls the shutdown function, and the program exits. Else, it calls the web_scraping function. This function calls another function with the name wishme.
def main_window(): global query wishme() while True: if query != None: if 'shutdown' in query or 'quit' in query or 'stop' in query or 'goodbye' in query: shut_down() break else: web_scraping(query) query = None
The wish me function
This function checks the current time and greets users according to the hour of the day and it also updates the canvas. The contents in the text variable are passed to the ‘speak’ function. The ‘transition’ function is also invoked at the same time in order to show the movement effect of the bot image, while the bot is speaking. This synchronization is achieved through threads, which is why these functions are called inside threads.
def wishme(): hour = datetime.datetime.now().hour if 0 <= hour < 12: text = "Good Morning sir. I am Jarvis. How can I Serve you?" elif 12 <= hour < 18: text = "Good Afternoon sir. I am Jarvis. How can I Serve you?" else: text = "Good Evening sir. I am Jarvis. How can I Serve you?" canvas2.create_text(10,10,anchor =NW , text=text,font=('Candara Light', -25,'bold italic'), fill="white",width=350) p1=Thread(target=speak,args=(text,)) p1.start() p2 = Thread(target=transition) p2.start()
The speak function
This function converts text to speech using pyttsx3 engine.
def speak(text): global flag engine.say(text) engine.runAndWait() flag=False
The transition functions
The transition function is used to create the GIF image effect, by looping over images and updating them on canvas. The frames variable contains a list of ordered image names.
def transition(): global img1 global flag global flag2 global frames global canvas local_flag = False for k in range(0,5000): for frame in frames: if flag == False: canvas.create_image(0, 0, image=img1, anchor=NW) canvas.update() flag = True return else: canvas.create_image(0, 0, image=frame, anchor=NW) canvas.update() time.sleep(0.1)
The web scraping function
This function is the heart of this application. The question asked by the user is then searched on google using the ‘requests’ library of python. The ‘beautifulsoap’ library extracts the HTML content of the page and checks for answers in four particular divs. If the webpage does not contain any of the four divs, then it searches for answers on Wikipedia links, however, if that is also not successful, then the bot apologizes.
def web_scraping(qs): global flag2 global loading URL = 'https://www.google.com/search?q=' + qs print(URL) page = requests.get(URL) soup = BeautifulSoup(page.content, 'html.parser')div0 = soup.find_all('div',class_="kvKEAb") div1 = soup.find_all("div", class_="Ap5OSd") div2 = soup.find_all("div", class_="nGphre") div3 = soup.find_all("div", class_="BNeawe iBp4i AP7Wnd") links = soup.findAll("a") all_links = [] for link in links: link_href = link.get('href') if "url?q=" in link_href and not "webcache" in link_href: all_links.append((link.get('href').split("?q=")[1].split("&sa=U")[0])) flag= False for link in all_links: if 'https://en.wikipedia.org/wiki/' in link: wiki = link flag = True break if len(div0)!=0: answer = div0[0].text elif len(div1) != 0: answer = div1[0].text+"\n"+div1[0].find_next_sibling("div").text elif len(div2) != 0: answer = div2[0].find_next("span").text+"\n"+div2[0].find_next("div",class_="kCrYT").text elif len(div3)!=0: answer = div3[1].text elif flag==True: page2 = requests.get(wiki) soup = BeautifulSoup(page2.text, 'html.parser') title = soup.select("#firstHeading")[0].text paragraphs = soup.select("p") for para in paragraphs: if bool(para.text.strip()): answer = title + "\n" + para.text break else: answer = "Sorry. I could not find the desired results" canvas2.create_text(10, 225, anchor=NW, text=answer, font=('Candara Light', -25,'bold italic'),fill="white", width=350) flag2 = False loading.destroy() p1=Thread(target=speak,args=(answer,)) p1.start() p2 = Thread(target=transition) p2.start()
The take command function
This function is invoked when the user clicks the green button to ask any question. The speech recognition library listens for 5 seconds and converts the audio input to text using google recognize API.
def takecommand(): global loading global flag global flag2 global canvas2 global query global img4 if flag2 == False: canvas2.delete("all") canvas2.create_image(0,0, image=img4, anchor="nw") speak("I am listening.") flag= True r = sr.Recognizer() r.dynamic_energy_threshold = True r.dynamic_energy_adjustment_ratio = 1.5 #r.energy_threshold = 4000 with sr.Microphone() as source: print("Listening...") #r.pause_threshold = 1 audio = r.listen(source,timeout=5,phrase_time_limit=5) #audio = r.listen(source) try: print("Recognizing..") query = r.recognize_google(audio, language='en-in') print(f"user Said :{query}\n") query = query.lower() canvas2.create_text(490, 120, anchor=NE, justify = RIGHT ,text=query, font=('fixedsys', -30),fill="white", width=350) global img3 loading = Label(root, image=img3, bd=0) loading.place(x=900, y=622) except Exception as e: print(e) speak("Say that again please") return "None"
The shutdown function
This function farewells the user and destroys the root window in order to exit the program.
def shut_down():
p1=Thread(target=speak,args=("Shutting down. Thankyou For Using Our Sevice. Take Care, Good Bye.",))
p1.start()
p2 = Thread(target=transition)
p2.start()
time.sleep(7)
root.destroy()
Conclusion
It is time to wrap up, I hope you enjoyed our little application. This is the power of Python, you can create small attractive applications in no time with a little amount of code. Keep following us for more cool python projects!
AI bot,chatbot,Python,voice bots,voice chatbot,Web scraping
Marketing analytics tells you about the most profitable marketing activities of your business. The more effectively you target the right people with the right approach, the greater value you generate for your business.
However, it is not always clear which of your marketing activities are effective at bringing value to your business. This is where marketing analytics comes in. Marketing analytics is the use of data to evaluate your marketing campaign. It helps you identify which of your activities are effective in engaging with your audience, improving user experience, and driving conversions.
6 marketing analytics features by Data Science Dojo
Marketing analytics is imperative in optimizing your campaigns to generate a net positive value from all your marketing activities in real-time. Without analyzing your marketing data and customer journey, you cannot identify what you are doing right and what you are doing wrong when engaging with potential customers. The 6 features listed below can give you the start you need to get into analyzing and optimizing your marketing strategy using marketing analytics
1. Impressions
In digital marketing, impressions are the number of times any piece of your content has been shown on a person’s screen. It can be an ad, a social media post, video etc. However, it is important to remember that impressions do not mean views, a view is an engagement, anytime somebody sees your video that is a view, but an impression would also include anytime they see your video in the recommended videos on YouTube or in their newsfeed on Facebook. The impression will be counted regardless of whether they watch your video or not.
Learn more about impressions in this video
It is also important to distinguish between impressions and reach. Reach is the number of unique viewers, so for example if the same person views your ad three times, you will have three impressions but a reach of one.
Impressions and reach are important in understanding how effective your content was at gaining traction. However, these metrics alone are not enough to gauge how effective your digital marketing efforts have been, neither impressions nor reach tell you how many people engaged with your content. So, tracking impressions is important, but it does not specify whether you are reaching the right audience.
2. Engagement rate
In social media marketing, engagement rate is an important metric. Engagement is when a user comments, likes, clicks, or otherwise interacts with any of your content. Engagement rate is a metric that measures the amount of engagement of your marketing campaign relative to each of the following:
Reach
Post
Impressions
Days
Views
Engagement rate by reach is the percentage of people who chose to interact with the content after seeing it. It is calculated by the following formula. Reach is a more accurate measurement than follower count, because not all of your brands followers may see the content while those who do not follow your brand may still be exposed to your content.
Engagement rate by post is the rate at which followers engage with the content. This metric shows how engaged your followers are with your content. However, this metric does not account for organic reach and as your follower count goes up your engagement by post goes down.
Engagement rate by Impressions is the rate of engagement relative to the number of impressions. If you are running paid ads for your brand, engagement rate by impressions can be used to gauge your ads effectiveness.
Average Daily engagement rate tells you how much your followers are engaging with your content daily. This is suitable for specific use cases for instance, when you want to know how much your followers are commenting on your posts or other content.
Engagement rate by views gives the percentage of people who chose to engage with your video after watching them. This metric however does not use unique views so it may double or triple count views from a single user.
Learn more about engagement rate in this video
3. Sessions
Sessions are another especially important metric in marketing analytics that help you analyze engagement on your website. A session is a set of activities by a user within a certain period. For example, a user spent 10 minutes on your website, loading pages, interacting with your content and completed an interaction. All these activities will be recorded in the same 10-minute session.
In google analytics, you can use sessions to check how much time a user spent on your website (session length), how many times they returned to your website (number of sessions), and what interactions users had with your website. Tracking sessions can help you determine how effective your campaigns were in directing traffic towards your website.
If you have an E-commerce website another very helpful tool on Google Analytics is behavioral analytics. With behavioral analytics you see what key actions are driving purchases on your website. The sessions report can be accessed under conversions tab on google analytics. This report can help you understand user behaviors such as abandon carts. This allows you to target these users with targeted ads or offering incentives to complete their purchase.
Learn more about sessions in this video
4. Conversion rate
Once you have engaged your audience the next step in the customers’ journey is conversion. A conversion is when you make the customer or user complete a specific action. This desired action can be anything from a form submission, purchasing a product or subscribing to a service. The conversion rate is the percentage of visitors who completed the desired action.
So, if you have a form on your website and you want to find out what the conversion rate is. You would simply divide the number of form submissions by the number of visitors on that form’s page (Total conversions/total interactions).
Conversion rate is a very important metric that helps you assess the quality of your leads. While you may generate a large number of leads or visitors, if you cannot get them to perform the desired action you may be targeting the wrong audience. Conversion rate can also help you gauge how effective your conversion strategy is, if you aren’t converting visitors, it might indicate that your campaign needs optimization.
5. Attribution
Attribution is a sophisticated model that helps you measure which channels are generating the most sales opportunities or conversions. It helps you assign credit to specific touchpoints on the customers journey and understand which touchpoints are driving conversions the most. But how do you know which touchpoint to attribute to a specific conversion? Well, that depends on which attribution models you are using. There are four common attribution models.
First touch attribution models assign all the credit to the first touchpoint that drove the prospect to your website. It focuses on the top of the marketing efforts funnel and tells you what is attracting people to your brand
Last touch attribution models assign credit to the last touchpoint. It focuses on the last touchpoint the visitor interacted with before they converted.
Linear attribution model assigns an equal weight to all the touchpoints in the buyer’s journey.
Time decay attributions is based on how close the touchpoint is to the conversion, where a weighted percentage is assigned to the most recent touchpoints. This can be used when the buying cycle is relatively short.
What model you use is based on what product or subscription you are selling and what is the length of your buyer cycle. While attribution is very important in identifying the effectiveness of your channels, to get the complete picture you need to look at how each touchpoint drives conversion.
Learn more about attribution in this video
6. Customer lifetime value
Businesses prefer retaining customers over acquiring new ones, and one of the main reasons is that attracting new customers has a cost. The customer acquisition cost is the total cost that you incur as a business acquiring a customer. The customer acquisition cost is calculated by dividing the marketing and sales cost by the number of new customers.
Learn more about CLV in this video
So, as a business, you must weigh the value of each customer with the associated acquisition cost. This is where the customer lifetime value or CLV comes in. The Customer lifetime value is the total value of your customer to your business during the period of your relationship.
The CLV helps you forecast your revenue as well, the larger the average CLV you have the better your forecasted revenue will be. CLV is calculated by dividing the annual revenue generated from customers by the average retention period (in years). If your CAC is higher than your CLV, then you are on average losing money on every customer you make.
This presents a huge problem. Metrics like CAC and CLV are very important for driving revenue. They help you identify high-value customers and identify low value customers so you can understand how to serve these customers better. They help you make more informed decisions regarding your marketing effort and build a healthy customer base.
Integrate marketing analytics into your business
Marketing analytics is a vast field. There is no one method that suits the needs of all businesses. Using data to analyze and drive your marketing and sales effort is a continuous effort that you will find yourself constantly improving upon. Furthermore, finding the right metrics to track that have a genuine impact on your business activities is a difficult task.
So, this list is by no means exhaustive, however the features listed here can give you the start you need to analyze and understand what actions are important in driving engagement, conversions and eventually value for your business.
Data science tools are becoming increasingly popular as the demand for data scientists increases. However, with so many different tools, knowing which ones to learn can be challenging
In this blog post, we will discuss the top 7 data science tools that you must learn. These tools will help you analyze and understand data better, which is essential for any data scientist.
So, without further ado, let’s get started!
List of 7 data science tools
There are many tools a data scientist must learn, but these are the top 7:
Top 7 data science tools you must learn
Python
R Programming
SQL
Java
Apache Spark
Tensorflow
Git
And now, let me share about each of them in greater detail!
1. Python
Python is a popular programming language that is widely used in data science. It is easy to learn and has many libraries that can be used to analyze data, machine learning, and deep learning.
It has many features that make it attractive for data science: An intuitive syntax,rich libraries, and an active community.
Python is also one of the most popular languages on GitHub, a platform where developers share their code.
Therefore, if you want to learn data science, you must learn Python!
There are several ways you can learn Python:
Take an online course: There are many online courses that you can take to learn Python. I recommend taking several introductory courses to familiarize yourself with the basic concepts.
PRO TIP: Join our 5-day instructor-led Python for Data Science training to enhance your deep learning skills.
Read a book: You can also pick up a guidebook to learning data science. They’re usually highly condensed with all the information you need to get started with Python programming.
Join a Boot Camp: Boot camps are intense, immersive programs that will teach you Python in a short amount of time.
Whichever way you learn Python, make sure you make an effort to master the language. It will be one of the essential tools for your data science career.
2. R Programming
R is another popular programming language that is highly used among statisticians and data scientists. They typically use R for statistical analysis, data visualization, and machine learning.
R has many features that make it attractive for data science:
A wide range of packages
An active community
Great tools for data visualization (ggplot2)
These features make it perfect for scientific research!
In my experience with using R as ahealthcare data analyst and data scientist, I enjoyed using packages like ggplot2 and tidyverse to work on healthcare and biological data too!
If you’re going to learn data science with a strong focus on statistics, then you need to learn R.
SQL (Structured Query Language) is a database query language used to store, manipulate, and retrieve data from data sources. It is an essential tool for data scientists because it allows them to work with databases.
SQL has many features that make it attractive for data science: it is easy to learn, can be used to query large databases, and is widely used in industry.
If you want to learn data science involving big data sets, then you need to learn SQL. SQL is also commonly used among data analysts if that’s a career you’re also considering exploring.
There are several ways you can learn SQL:
Take an online course: There are plenty of SQL courses online. I’d pick one or two of them to start with
Java is another programming language to learn as a data scientist. Java can be used for data processing, analysis, and NLP (Natural Language Processing).
Java has many features that make it attractive for data science: it is easy to learn, can be used to develop scalable applications, and has a wide range of frameworks commonly used in data science. Some popular frameworks include Hadoop and Kafka.
Apache Spark is a powerful big data processing tool that is used for data analysis, machine learning, and streaming. It is an open-source project that was originally developed at UC Berkeley’s AMPLab.
Apache Spark is known for its uses in large-scale data analytics, where data scientists can run machine learning on single-node clusters and machines.
Spark has many features made for data science:
It can process large datasets quickly
It supports multiple programming languages
It has high scalability
It has a wide range of libraries
If you want to learn big data science, then Apache Spark is a must-learn. Consider taking an online course or watching awebinar on big data to get started.
6. Tensorflow
TensorFlow is a powerful toolkit for machine learning developed by Google. It allows you to build and train complex models quickly.
Some ways TensorFlow is useful for data science:
Provides a platform for data automation
Model monitoring
Model training
Many data scientists use TensorFlow with Python to develop machine learning models. TensorFlow helps them to build complex models quickly and easily.
If you’re interested to learn TensorFlow, do consider these ways:
Read the official documentation
Complete online courses
Attend a TensorFlow meetup
However, to learn and practice your Tensorflow skills, you’ll need to pick up decentdeep learning hardware to support the running of your algorithms.
7. Git
Git is a version control system used to track code changes. It is an essential tool for data scientists because it allows them to work on projects collaboratively and keep track of their work.
If you’re planning to enter data science, Git is a must-know tool! Since you’ll be coding a lot in Python/R/Java, you’ll want to master Git to work with your team well in a collaborative coding environment.
Git is also an essential part of using GitHub, a code repository platform used by many data scientists.
To learn Git, I’d recommend just watching simple tutorials on YouTube.
Final thoughts
And these are the top seven data science tools that you must learn!
The most important thing is to get started and keepupskilling yourself! There is no one-size-fits-all solution in data science, so find the tools that work best for you and your team and start learning.
I hope this blog post has been helpful in your journey to becoming a data scientist. Happy learning!
Data science tools,Git,Java,Programming,Python,R,SQL,Tensorflow
Learning Data Science with fun is the missing ingredient for diligent data scientists. This blog post collected the best data science jokes including statistics, artificial intelligence, and machine learning.
For Data Scientists
1. There are two kinds of data scientists. 1.) Those who can extrapolate from incomplete data.
2. Data science is 80% preparing data, and 20% complaining about preparing data.
3. There are 10 kinds of people in this world. Those who understand binary and those who don’t.
4. What’s the difference between an introverted data analyst & an extroverted one? Answer: the extrovert stares at YOUR shoes.
5. Why did the chicken cross the road? The answer is trivial and is left as an exercise for the reader.
6. The data science motto: If at first, you don’t succeed; call it version 1.0
7. What do you get when you cross a pirate with a data scientist? Answer: Someone who specializes in Rrrr
8. A SQL query walks into a bar, walks up to two tables, and asks, “Can I join you?”
9. Why should you take a data scientist with you into the jungle? Answer: They can take care of Python problems
10. Old data analysts never die – they just get broken down by age
11. I don’t know any programming, but I still use Excel in my field!
12. Data is like people – interrogate it hard enough and it will tell you whatever you want to hear.
13. Don’t get it? We can help. Check out our in-person data science Bootcamp or online data science certificate program.
For Statisticians
14. Statistics may be dull, but it has its moments.
15. You are so mean that your standard deviation is zero.
16. How did the random variable get into the club? By showing a fake i.d.
17. Did you hear the one about the statistician? Probably….
18. Three statisticians went out hunting and came across a large deer. The first statistician fired, but missed, by a meter to the left. The second statistician fired, but also missed, by a meter to the right. The third statistician didn’t fire, but shouted in triumph, “On average we got it!”
19. Two random variables were talking in a bar. They thought they were being discreet, but I heard their chatter continuously.
20. Statisticians love whoever they spend the most time with; that’s their statistically significant other.
21. Old age is statistically good for you – very few people die past the age of 100.
22. Statistics prove offspring’s an inherited trait. If your parents didn’t have kids, odds are you won’t either.
For Artificial Intelligence experts
23. Artificial intelligence is no match for natural stupidity
24. Do neural networks dream of strictly convex sheep?
25. What did one support vector say to another support-vector? Answer: I feel so marginalized
26. AI blogs are like philosophy majors. They’re always trying to explain “deep learning.”
27. How many support vectors does it take to change a light bulb? Answer: Very few, but they must be careful not to shatter* it.
28. Parent: If all your friends jumped off a bridge, would you follow them? Machine Learning Algorithm: yes.
29. They call me Dirichlet because all my potential is latent and awaiting allocation
30. Batch algorithms: YOLO (You Only Learn Once), Online algorithms: Keep Updates and Carry On
31. “This new display can recognize speech” “What?” “This nudist play can wreck a nice beach”
32. Why did the naive Bayesian suddenly feel patriotic when he heard fireworks? Answer: He assumed independence
33. Why did the programmer quit their job? Answer: Because they didn’t get arrays.
34. What do you call a program that identifies spa treatments? Facial recognition!
35. Human: What do we want!?
Computer: Natural language processing!
Human: When do we want it!?
Computer: When do we want what?
36. A statistician’s wife had twins. He was delighted. He rang the minister who was also delighted. “Bring them to church on Sunday and we’ll baptize them,” said the minister. “No,” replied the statistician. “Baptize one. We’ll keep the other as a control.”
For Machine Learning professionals
37. I have a joke about a data miner, but you probably won’t dig it. @KDnuggets:
38. I have a joke about deep learning, but I can’t explain it. Shamail Saeed, @hacklavya
39. I have a joke about deep learning, but it is shallow. Mehmet Suzen, @memosisland
40. I have a machine learning joke, but it is not performing as well on a new audience. @dbredesen
41. I have a new joke about Bayesian inference, but you’d probably like the prior more. @pauljmey
42. I have a joke about Markov models, but it’s hidden somewhere. @AmeyKUMAR1
43. I have a statistics joke, but it’s not significant. @micheleveldsman
44. I have a geography joke, but I don’t know where it is. @olimould
45. I have an object-oriented programming joke. But it has no class. Ayin Vala
46. I have a quantum mechanics joke. It’s both funny and not funny at the same time. Philip Welch
47. I have a good Bayesian laugh that came from a prior joke. Nikhil Kumar Mishra
48. I have a java joke, but it is too verbose! Avneesh Sharma
49. I have a regression joke, but it sounds quite mean. Gang Su
50. I have a machine learning joke, but I cannot explain it. Andriy Burkov
Did we miss your favorite Data Science joke?
Share your favorite data science jokes with us in the comments below. Let’s laugh together!
Data science jokes,fun data science,jokes,Machine Learning,statistics jokes
Data Science Dojo is offering Apache Zeppelin for FREE on Azure Marketplace packaged with pre-installed interpreters and backends to make Machine Learning easier than ever.
Introduction
How cumbersome and tiring it is to install different tools to perform your desired ML tasks and then look after the integration and dependency issues. Already getting headaches? Worry not, because Data Science Dojo’s Apache Zeppelin instance fixes all of that. But before we delve further into it, let’s get to know some basics.
What are Machine Learning Operations?
Machine Learning is a branch of Artificial Intelligence that deals with models that produce outcomes based on some learned pre-existing data. It provides automation and reduces the workload of users. ML converges with Data Science and Engineering and that gives birth to some necessary operations to be performed to acquire the output of any task.
These operations include ETL (Extraction, Transform, Load) or ELT, drawing interactive visualizations, running queries, training and testing ML models and several other functions.
Pro Tip: Join our 6-months instructor-led Data Science Bootcamp to master machine learning skills.
Challenges for individuals
Wanting to explore and visualize your data but not knowing the methodology of the new tool is not only a red flag but also demands extraneous skills to be learnt to proceed with your job. Or you would have to switch among different environments to achieve your goal which is again – time-consuming, and needless to say time is of the essence for data scientists and engineers when they must deliver a task.
In this scenario, switching from one tool to another which you may know how to use or may not, is time and cost intensive. What if a data driven interactive environment having several interpreters ready to be worked with in one place is provided and you just select your favorite language and break the ice?
ML Operations with Apache Zeppelin
Apache Zeppelin is an open-source tool that equips you with a web-based notebook that can be used for data processing and querying, handling big data, training and testing models, interactive data analytics, visualization, and exploration. Vibrant designs and pictures generated can save time for users in the identification of key patterns in data and ultimately accelerates the decision-making processes.
It contains different pre-installed interpreters but also allows you to plug in your own various language backends for desirability. Apache Zeppelin supports many data sources which allow you to synthesize your data to visualize into interactive plots and charts. You can also create dynamic forms in your notebook and can share your notebook with collaborators.
Zeppelin delivers an optimized and interactive UI that enhances the plots, charts, and other diagrams. You can also create dynamic forms in your notebook along with other markdowns to fancify your note
It’s open-source and allows vendors to make Zeppelin highly customized according to use-case requirements that vary from industry to industry
The choice to select a learned backend from a variety of pre-installed ones or the feasibility to add your own customizable language adds to the user-friendliness, flexibility, and adaptability
It supports Big Data databases like Hive and Spark. It also provides support for web sockets so you can share your web page by echoing the output of the browser and creating live reports
Zeppelin provides an in-build job manager who keeps track of the condition or status of various notebooks
What Data Science Dojo has for you
Our Zeppelin instance serves as a web-accessible programming environment with miscellaneous pre-installed interpreters. In our service users can switch between different interpreters like processing data with python and then visualizing it by querying with SQL. The pre-installed backends provide the feasibility to perform the task using your accustomed language instead of learning a new tool.
A web-accessible Zeppelin environment
Several pre-installed language-backends/interpreters
Various tutorial notebooks containing codes for understandability
A Job manager responsible for monitoring the status of the notebooks
A Notebook Repos feature to manage your notebook repositories’ settings
Ability to import notes from JSON file or URL
In-build functionality to add and modify your own customized interpreters
Efficient resource requirement for processing, visualizing, and training large data was one of the areas of concern when working on traditional desktop environments. The other area of concern includes the burden of working with non-familiar backends or switching among different accustomed environments. With our Zeppelin instance, both concerns are put to rest.
When coupled with Microsoft Azure services and processing speed, it outperforms the traditional counterparts because data-intensive computations aren’t performed locally, but in the cloud. You can collaborate and share notebooks with various stakeholders within and outside the company while monitoring the status of each
At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We are therefore adding a free Zeppelin Notebook Environment dedicated specifically for Machine Learning and Data Science operations on Azure Market Place. Don’t wait to install this offer by Data Science Dojo, your ideal companion in your journey to learn data science!
Click on the button below to head over to the Azure Marketplace and deploy Apache Zeppelin for FREE by clicking on “Get it now”.
Note: You’ll have to sign up to Azure, for free, if you do not have an existing account.
Data is growing at an exponential rate in the world. It is estimated that the world will generate 181 zettabytes of data by 2025. With this increase, we are also seeing an increase in demand for data-driven techniques and strategies.
According to Forbes, 95% of businesses expressed the need to manage unstructured data as a problem for their business. In fact, Business Analytics vs Data Science is one of the hottest debates among data professionals nowadays.
Many people might wonder – what is the difference between Business Analytics and Data Science? Or which one should they choose as a career path? If you are one of those keep reading to know more about both these fields!
Team working on Business Analytics
First, we need to understand what both these fields are. Let’s take a look.
What is Business Analytics?
Business Analytics is the process of deriving insights from business data to inform business decisions. It is the process of collecting data and doing analysis for the business to make better decisions. It provides a lot of insight that can be used to make better business decisions. It helps in optimizing processes and improving productivity.
It also helps in identifying potential risks, opportunities, and threats. Business Analytics is an important part of any organization’s decision-making process. It is a combination of different analytical activities like data exploration, data visualization, data transformation, data modeling, and model validation. All of this is done by using various tools and techniques like R programming, machine learning, artificial intelligence, data mining, etc.
Business analytics is a very diverse field that can be used in every industry. It can be used in areas like marketing, sales, supply chain, operations, finance, technology and many more.
Now that we have a good understanding of what Business Analytics is, let’s move on to Data Science.
What is Data Science?
Data science is the process of discovering new information, knowledge, and insights from data. They apply different machine-learning algorithms to any form of data from numbers to text, images, videos, and audio, to draw various understandings from them. Data science is all about exploring data to identify hidden patterns and make decisions based on them.
It involves implementing the right analytical techniques and tools to transform the data into something meaningful. It is not just about storing data in the database or creating reports about the same. Data scientists collect and clean the data, apply machine learning algorithms, create visualizations, and use data-driven decision-making tools to create an impact on the organization.
Data scientists use tools like programming languages, database management, artificial intelligence, and machine learning to clean, visualize, and explore the data.
What is the difference between Business Analytics and Data Science?
Technically, Business analytics is a subset of Data Science. But the two terms are often used interchangeably because of the lack of a clear understanding among people. Let’s discuss the key differences between Business Analytics and Data Science. Business Analytics focuses on creating insights from existing data for making better business decisions.
While Data Science focuses on creating insights from new data by applying the right analytical techniques. Business Analytics is a more established field. It combines several analytical activities like data transformation, modeling, and validation. Data Science is a relatively new field that is evolving every day. Business Analytics is more of a hands-on approach to manage the data whereas Data Science is more focused on the development of the data.
Both the fields also differ a bit in their required skills. Business Analysts mostly use Interpretation, Data visualization, analytical reasoning, statistics, and written communication skills to interpret and communicate their work. Whereas Data Scientists utilize statistical analysis, programming skills, machine learning, calculus and algebra, and data visualization to perform most of their work.
Which should one choose?
Business analytics is a well-established field, whereas data science is still evolving. If you are inclined towards decisive and logical skills with little or no programming knowledge or computer science skills, you can take up Business Analytics. It is a beginner friendly domain and is easy to catch on to.
But if you are interested in programming and are familiar with machine learning algorithms or even interested in data analysis, you can opt for Data Science. We hope this blog answers your questions about the differences between the two similar and somewhat overlapping fields and helps you make the right data-driven and informed decision for yourself!
Be it Netflix, Amazon, or another mega-giant, their success stands on the shoulders of experts, analysts are busy deploying machine learning through supervised, unsupervised, and reinforcement successfully.
The tremendous amount of data being generated via computers, smartphones, and other technologies can be overwhelming, especially for those who do not know what to make of it. To make the best use of data researchers and programmers often leverage machine learning for an engaging user experience.
There are many advanced techniques that are coming up every day for data scientists. Of all supervised, and unsupervised, reinforcement learning is leveraged often. In this article, we will briefly explain what supervised, unsupervised, and reinforcement learning is, how they are different, and the relevant uses of each by well-renowned companies.
Supervised machine learning is used for making predictions from data. To be able to do that, we need to know what to predict, which is also known as the target variable. The datasets where the target label is known are called labeled datasets to teach algorithms that can properly categorize data or predict outcomes. Therefore, for supervised learning:
We need to know the target value
Targets are known in labeled datasets
Let’s look at an example: If we want to predict the prices of houses, supervised learning can help us predict that. For this, we will train the model using characteristics of the houses, such as the area (sq ft.), the number of bedrooms, amenities nearby, and other similar characteristics, but most importantly the variable that needs to be predicted – the price of the house.
A supervised machine learning algorithm can make predictions such as predicting the different prices of the house using the features mentioned earlier, predicting trends of future sales, and many more.
Sometimes this information may be easily accessible while other times, it may prove to be costly, unavailable, or difficult to obtain, which is one of the main drawbacks of supervised learning.
Saniye Alabeyi, Senior Director Analyst at Garnet calls Supervised learning the backbone of today’s economy, stating:
“Through 2022, supervised learning will remain the type of ML utilized most by enterprise IT leaders” (Source).
Types of problems:
Supervised learning deals with two distinct kinds of problems:
Classification problems
Regression problems
Classification problem: In the case of classification problems, examples are classified into one or more classes/ categories.
For example, if we are trying to predict that a student will pass or fail based on their past profile, the prediction output will be “pass/fail.” Classification problems are often resolved using algorithms such as Naïve Bayes, Support Vector Machines, Logistic Regression, and many others.
Regression problem: A problem in which the output variable is either a real or continuous value, s is defined as a regression problem. Bringing back the student example, if we are trying to predict that a student will pass or fail based on their past profuse, the prediction output will be numeric, such as “68%” likely to score.
Predicting the prices of houses in an area is an example of a regression problem and can be solved using algorithms such as linear regression, non-linear regression, Bayesian linear regression, and many others.
Why Amazon, Netflix and YouTube are great fans of supervised learning
Recommender systems are a notable example of supervised learning. E-commerce companies such as Amazon, streaming sites like Netflix, social media platforms such as TikTok, Instagram, and even YouTube among many others make use of recommender systems to make appropriate recommendations to their target audience.
Unsupervised learning
Imagine receiving swathes of data with no obvious pattern in it. A dataset with no labels, or target values cannot come up with an answer to what to predict. Does that mean the data is all waste? Nope! Infact, it is likely that the dataset has many hidden patterns in it.
Unsupervised learning studies the underlying patterns and predicts the output. In simple terms, in unsupervised learning the model is only provided with the data in which it looks for hidden or underlying patterns.
Unsupervised learning is most helpful for projects where individuals are unsure of what they are looking for in data. It is used to search for unknown similarities and differences in data to create corresponding groups.
An application of unsupervised learning is the categorization of users based on their social media activities.
Commonly used unsupervised machine learning algorithms include K-means clustering, neural networks, principal component analysis, hierarchical clustering, and many more.
Reinforcement learning
Another type of machine learning is reinforcement learning.
In reinforcement learning, algorithms learn to an environment on their own. The field has gained quite some popularity over the years and has produced a variety of learning algorithms.
Reinforcement learning is neither supervised nor unsupervised as it does not require labelled data or a training set. It relies on the ability to monitor the response of the actions of the learning agent.
Most used in gaming, robotics, and many other fields, reinforcement learning makes use of a learning agent. A start state and an end state are involved. For the learning agent to reach the final or end stage, different paths may be involved.
An agent may also try to manipulate its environment and may travel from one state to another
On success, the agent is rewarded but does not receive any reward or appreciation on failure
Amazon has robots picking and moving goods in warehouses because of reinforcement learning
Numerous IT companies including Google, IBM, Sony, Microsoft, and many others have established research centers focused on projects related to reinforcement learning.
Social media platforms like Facebook have also started implementing reinforcement learning models that can consider different inputs such as languages, integrate real-world variables such as fairness, privacy, and security, and more to mimic human behavior and interactions. (Source)
Amazon also employs reinforcement learning to teach robots in its warehouses and factories how to pick up and move goods.
Comparison between supervised, unsupervised, and reinforcement learning
Caption: Differences between supervised, unsupervised, and reinforcement learning algorithms
Supervised learning
Unsupervised learning
Reinforcement learning
Definition
Makes predictions from data
Segments and groups data
Reward-punishment system and interactive environment
Types of data
Labelled data
Unlabeled data
Acts according to a policy with a final goal to reach (No or predefined data)
Commercial value
High commercial and business value
Medium commercial and business value
Little commercial use yet
Types of problems
Regression and classification
Association and Clustering
Exploitation or Exploration
Supervision
Extra supervision
No supervision
No supervision
Algorithms
Linear Regression, Logistic Regression, SVM, KNN and so forth
K – Means clustering,
C – Means, Apriori
Q – Learning,
SARSA
Aim
Calculate outcomes
Discover underlying patterns
Learn a series of action
Application
Risk Evaluation, Forecast Sales
Recommendation System, Anomaly Detection
Self-Driving Cars, Gaming, Healthcare
Which is better Machine Learning technique?
We learned about the three main members of the machine learning family essential for deep learning. Other kinds of learning are also available such as semi-supervised learning, or self-supervised learning.
Supervised, unsupervised, and reinforcement learning, are all used for different to complete diverse kinds of tasks. No single algorithm exists that can solve every problem, as problems of different natures require different approaches to resolve them.
Despite the many differences between the three types of learning, all of these can be used to build efficient and high-value machine learning and Artificial Intelligence applications. All techniques are used in different areas of research and development to help solve complex tasks and resolve challenges.
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Looking at the right event metrics not only helps us in gauging the success of the current event but also facilitates understanding the audience’s behavior and preferences for future events.
Creating, managing, and organizing an event seems like a lot of work and surely it is. The job of an event manager is no doubt a hectic one, and the job doesn’t end once the event is complete. After every event, analyzing it is a crucial task to continuously improve and enhance the experience for your audience and presenters.
In a world completely driven by data, if you are not measuring your events, you are surely missing out on a lot. The questions arise about how to get started and what metrics to look for.The post-Covid world has adopted the culture of virtual events which not only allows the organizers to gather audiences globally but also makes it easier for them to measure it.
There are several platforms and tools available for collecting the data, or if you are hosting it through social media then you can easily use the analytics tool of that channel. You can view our Marketing Analytics videos to better understand the analytical tools and features of each platform.
Successful event metrics
You can take the assistance of tools and platforms to collect the data but utilizing that data to come up with insightful findings and patterns is a critical task. You need to hear the story your data is trying to tell and understand the patterns in your events.
Event metrics that you should look at
1. RSVP to attendance rate
RSVP is the number of people who sign up for your event (through landing pages or social sites) while attendance rate is the number of people who show up.
You should expect at least 30% of your RSVPs to actually attend and if they don’t there is something wrong, the possible reasons could be:
The procedure for joining the event is not provided or clarified
They forgot about the event as they signed up long before
The information provided regarding the event day or date is wrong
Or it many other likely reasons. You need to dig into each channel to find out the reason because if a person signs up, it shows a clear intent to attend from their end.
2. Retention rate
There are a few channels as LinkedIn and YouTube that have inbuilt analytics to gauge retention rate, but you can always integrate third-party tools for other platforms. The retention rate depicts how long your audience stayed in your webinar and the points where they dropped off.
It is usually shown as a line graph with the duration of the webinar on the x-axis and the number of people on the y-axis, in this way you can view the number of people at a certain time in the webinar. Through this chart, you can look at points where you see a drop or rise in your views.
Graph representing retention rate
Use-case For instance, at Data Science Dojo our webinars experienced a huge drop in the audience during the initial 5 mins of the webinar. It was worrisome for the team, so we dug into it and conducted a critical analysis of our webinars. We realized this was happening because we usually spend our first 5 mins waiting for the audience to join in but that is where our existing audience started leaving.
We decided to bring in engaging activities as a poll in those 5 mins and initiated conversations with our audience directly through chats which improved our overall retention as our audience started feeling more connected which made them stay for a long time. You can explore our webinars here.
3. Demographics of audience
It is far-reaching to know where your audience belongs to. To take more targeted decisions in the future, every business must realize the audience demographics and what type of people find your events beneficial.
Once we work on the demographics, it will help us for future events. For example, you can select a time that would be viable in your audience’s time zone, and you can also select a topic that they would be more interested in.
Statistics showing demographic data
The demographics data opens many new avenues for your business, it introduces you to segments of your audience that you might not be targeting already, and you can expand your business. It shows the industries, locations, seniority, and many other crucial factors about your audience.
By analyzing this data, you can also understand whether your content is attracting the right target audience or not, if not then what kind of audience you are pulling in and whether that’s beneficial for your business or not.
4. Engagement rate
Your event might receive a large number of views but if that audience is not engaging with your content, then it is something you should be concerned about. The engagement rate depicts how involved your audience is. Today’s audience has a lot of distractions especially when it comes to online events, in that situation grasping your audience’s attention and keeping them involved is a major task.
Audience engagement shown by chat messages
The more engaged the audience is, the higher the chance that they will benefit from it and come back to you for other services. There are several techniques to keep your audience engaged, you can look up a few engagement activities to build connections.
Make your event a success with event metrics
On that note, if you have just hosted an event or have an event on your calendar, you know what you need to look at. These metrics will help you continuously improve your event’s quality to match the audience’s expectations and requirements. Planning your strategies based on data will help you stay relevant to your audience and trends.
In today’s blog, we will try to understand the working behind social media algorithms and focus on the top 6 social media platforms. Algorithms are a part of machine learning which has also become a key area to measure success of digital marketing; these are written by coders to learn human actions. It specifies the behavior of data by using a mathematical set of rules
According to the latest data for 2022, users worldwide spend 147 minutes, on average every day on social media. The use of social media is booming with every passing day. We get hooked up on the content of our interest. But you cannot deny that it is often surprising to experience the content we just discussed with our friends or family.
Social media algorithms sort posts on a user’s feed based on their interest rather than the publishing time. Every content creator desires to get the maximum impressions on their social media postings or their marketing campaigns. That’s where the need to develop quality content comes in. Social media users only experience the content that the algorithms figure out to be most relevant for them.
1. Insights into Facebook algorithm
Facebook had 2.934 billion monthly active users in July 2022.
Anna Stepanov, Head of Facebook App Integrity said “News Feed uses personalized ranking, which considers thousands of unique signals to understand what’s most meaningful to you. Our aim isn’t to keep you scrolling on Facebook for hours on end, but to give you an enjoyable experience that you want to return to.”
On Facebook, which means that the average reach for an organic post is down over 5 percent while the engagement rate is just 0.25 percent which drops to 0.08 percent if you have over 100k followers.
Facebook’s algorithm is not static, it has evolved over the years with the objective to keep its users engaged with the platform. In 2022, Facebook adopted the idea of showing stories to users instead of news, like before. So, what we see on Facebook is no longer a newsfeed but “feed” only.
Further, it works mainly on 3 ranking signals:
Interactivity:
The more you interact with the posts from one of your friends or family members, Facebook is going to show you their activities relatively more on your feed.
Interest:
If you like content about cars or automobiles, there’s a high chance Facebook algorithm will push relevant posts to your feed. This happens because we search, like, interact or spend most of our time seeing the content we like.
Impressions:
Viral or popular content becomes a part of everyone’s Facebook. That’s because the Facebook algorithm promotes content that is in general liked by its users. So, you’re also more likely to see what’s everyone talking about today.
2. How does YouTube algorithm work
There are 2.1 billion monthly active YouTube users worldwide. When you open YouTube, you see multiple streaming options. YouTube says that in 2022, homepages and suggested videos are usually the top sources of traffic for most channels.
The broad selection is narrowed on the user homepage on the basis of two main types of ranking signals.
Performance:
When a video is uploaded on YouTube, the algorithm evaluates it on the basis of a few key metrics:
Click-through rate
Average view duration
Average percentage viewed
Likes and dislikes
Viewer surveys
If a video gains good viewership and engagement by the regular followers of the channel, then the YouTube algorithm will offer that video to more users on YouTube.
Personalization:
The second-ranking signal for YouTube is personalization. In case you love watching DIY videos, YouTube algorithm processes to keep you hooked on the platform by suggesting interesting DIY videos to you.
Personalization works based on a user’s watch history or the channels you subscribed to lately. It tracks your past behavior and figures out your most preferred streaming options.
Lastly, you must not forget that YouTube acts as a search engine too. So, what you type in the search bar plays a major role in shortlisting the top videos for you.
3. Instagram algorithm explained
In July 2022, Instagram reached 1.440 billion users around the world according to the global advertising audience reach numbers.
The main content on Instagram revolves around posts, stories, and reels. Instagram CEO Adam Mosseri said, “We want to make the most of your time, and we believe that using technology [the Instagram algorithm] to personalize your experience is the best way to do that.”
Let’s shed some light to the Instagram’s top 3 ranking factors for year 2022:
Interactivity:
Every account holder or influencer on Instagram runs after followers. Because that’s the core to getting your content viewed by the users. To get something on our Instagram feed we need to follow other accounts. As much as our interaction with someone’s content occurs, we will be able to see more of their postings.
Interest:
This ranking factor has more influence on reels feed and explore page. The more you show interest in watching a specific type of content and tap on it, the more of that category will be shown to you. And it’s not essential to follow someone to see their postings on reels and explore the page.
Information:
How relevant is the content uploaded on Instagram? This highlights the value of content posted by anyone. If people are talking about it, engaging with it, and sharing it on their stories, you are also going to see it on your feed.
4. Guide to Pinterest algorithm
Being the 15th most active social media platform, Pinterest had 433 million monthly active users in July 2022.
Pinterest is popular amongst audiences who are more likely interested in home décor, aesthetics, food, and style inspirations. This platform carries a slightly different purpose of use than the above-mentioned social media platforms. Therefore, the algorithm works with distinct ranking factors for Pinterest.
Pinterest algorithm promotes pins having:
High-quality images and visually appealing designs
Proper use of keywords in the pin descriptions so that pins come up in search results.
Increased activity on Pinterest and engagement with other users.
Needless to mention, the algorithm weighs more for the pins that are similar to a user’s past pins and search activities.
5. Working process behind LinkedIn algorithm
There are 849.6 million users with LinkedIn in July 2022. LinkedIn is a platform for professionals. People use it to build their social networks and have the right connections that can help them succeed in their careers.
To maintain the authenticity and relevance of connections for professionals, the LinkedIn algorithm processes billions of posts per day to keep the platform valuable for its users. LinkedIn’s ranking factors are mainly these:
Spam:
LinkedIn considers post as spam if it contains a lot of links, has multiple grammatical errors, and consists of bad vocabulary. Also, avoid using hashtags like #comment, #like, or #follow can flag the system, too.
Low-quality posts:
There are billions of posts uploaded on LinkedIn every day. The algorithm works to filter out the best for users to engage with. Low-quality posts are not spam but they lack value as compared to other posts. It is evaluated based on the engagement a post receives.
High-quality content:
You wonder what’s the criteria to create high-quality posts on LinkedIn? Here are some tips to remember:
Easy to read posts
Encourages responses with a question
Uses three or fewer hashtags
Incorporates strong keywords
Tag responsive people to the post
Moreover, LinkedIn appreciates consistency in posts, so it’s recommended to keep your followers engaged not only with informative posts but also conversing with users in the comments section.
6. A sneak peek at the TikTok algorithm
TikTok will have 750 million monthly users worldwide in 2022. In the past couple of years, this social media platform has gained popularity for all the right reasons. The TikTok algorithm is considered as a recommendation system for its users.
We have found one great explanation of TikTok “For You” page algorithm by the platform itself:
“A stream of videos curated to your interests, making it easy to find content and creators you love … powered by a recommendation system that delivers content to each user that is likely to be of interest to that particular user.”
Key ranking factors for the TikTok algorithm are:
User interactions:
This factor is like the Instagram algorithm, but mainly concerns the following actions of users:
Which accounts do you follow
Comments you’ve posted
Videos you’ve reported as inappropriate
Longer videos you watch all the way to the end (aka video completion rate)
Content you create on your own account
Creators you’ve hidden
Videos you’ve liked or shared on the app
Videos you’ve added to your favorites
Videos you’ve marked as “Not Interested”
Interests you’ve expressed by interacting with organic content and ads
Video information:
Videos with missing information, incorrect captions, titles, and tags are buried under hundreds of videos being uploaded on TikTok every minute. On the discover tab, your video information signals tend to seek for:
Captions
Sounds
Hashtags*
Effects
Trending topics
TikTok account settings:
TikTok algorithm optimizes the audience for your video based on the options you selected while creating your account. Some of the device and account settings that decide audience for your videos are:
Language preference
Country setting (you may be more likely to see content from people in your own country)
Type of mobile device
Categories of interest you selected as a new user
Social media algorithms relation with content quality
Apart from all the key ranking factors for each platform, we discussed in this blog, one thing remains ascertain for all i.e., maintain content quality. Every social media platform is algorithm bsed which means it only filters out the best quality content for visitors.
No matter which platform you focus on growing your business or your social network, it highly relies on the meaningful content you provide your connections.
If we missed your favorite social media platform, don’t worry, let us know in the comments and we will share its algorithm in the next blog.
The Monte Carlo method is a technique for solving complex problems using probability and random numbers. Through repeated random sampling, Monte Carlo calculates the probabilities of multiple possible outcomes occurring in an uncertain process.
Whenever you try to solve problems in the future, you make certain assumptions. For example, forecasting problems make certain assumptions like the cost of a particular item, the value of stocks, or electricity units used in the future. Since these problems try to predict an estimate of an unknown value based on historical data, there always exists inherent risk and uncertainty.
The Monte Carlo simulation allows us to see all the possible outcomes of our decisions and assess risk, consequently allowing for better decision-making under uncertainty.
This blog will walk through the famous Monty Hall problem, and how it can be solved using the Monte Carlo method using Python.
Monty Hall problem
In the Monty Hall problem, the TV show host Monty presents three doors to the participant. Behind one of the doors is a valuable prize like a car, while behind the others is a less valuable prize like a goat.
Consider yourself to be one of the participants in the show. You choose one out of the three doors. Before opening your chosen door, Monty opens another door behind which would be one of the goats. Now you are left with two doors, behind one could be the car, and behind the other would be the other goat.
Monty then gives you the option to either switch your answer to the other unopened door or stick to the original one.
Is it in your favor to switch your answer to the other door? Well, probability says it is!
Let’s see how:
Initially, there are three unopen doors in front of you. The probability of the car being behind any of these doors is 1/3.
Let’s say you decide to pick door #1 as the probability is the same (1/3) for each of these doors. In other words, the probability that the car is behind door #1 is 1/3, and the probability that it will be behind either door #2 or door #3 is 2/3.
Monty is aware of the prize behind each door. He chooses to open door #3 and reveal a goat. He then asks you if you would like to either switch to door #2 or stick with door #1.
To solve the problem, let’s switch to Python and apply the Monte Carlo simulation.
Solving with Python
Initialize the 3 prizes
Create python lists to store the probabilities after each game. We will play as many games as iterations input.
Monte Carlo simulation
Before starting the game, we randomize the prizes behind each door. One of the doors will have a car behind it, while the other two will have a goat each. When we play a large number of games, all possible permutations get covered of prize distributions, and door choices get covered.
Below is the code that decides if your choice was correct or not, and if switching would’ve been the correct move.
After playing each game, the winning probabilities are updated and stored in the lists. When all games have been played, we return the final values of each of the lists, i.e., winning by switching your choice and winning by sticking to your choice.
Get results
Enter your desired number of iterations (the higher the number, the more numbers of games will be played to approximate the probabilities). In the final step, plot your results.
After running the simulation 1000 times, the probability that we win by always switching is 67.7%, and the probability that we win by always sticking to our choice is 32.3%. In other words, you will win approximately 2/3 times if you switch your door, and only 1/3 times if you stick to the original door.
Therefore, according to the Monte Carlo simulation, we are confident that it works to our advantage to switch the door in this tricky game.
In this blog, we will introduce you to the highly rated data science statistics books on Amazon. As you read the blog, you will find 5 books for beginners and 5 books for advanced-level experts. We will discuss what’s covered in each book and how it helps you to scale up your data science career.
Advanced statistics books for data science
1. Naked Statistics: Stripping the Dread from the Data – By Charles Wheelan
The book unfolds the underlying impact of statistics on our everyday life. It walks the readers through the power of data behind the news.
Mr. Wheelan begins the book with the classic Monty Hall problem. It is a famous, seemingly paradoxical problem using Bayes’ theorem in conditional probability. Moving on, the book separates the important ideas from the arcane technical details that can get in the way. The second part of the book interprets the role of descriptive statistics in crafting a meaningful summary of the underlying phenomenon of data.
Wheelan highlights the Gini Index to show how it represents the income distribution of the nation’s residents and is mostly used to measure inequality. The later part of the book clarifies key concepts such as correlation, inference, and regression analysis explaining how data is being manipulated in order to tackle thorny questions. Wheelan’s concluding chapter is all about the amazing contribution that statistics will continue to make to solving the world’s most pressing problems, rather than a more reflective assessment of its strengths and weaknesses.
2. Bayesian Methods For Hackers – Probabilistic Programming and Bayesian Inference, By Cameron Davidson-Pilon
We mostly learn Bayesian inference through intensely complex mathematical analyses that are also supported by artificial examples. This book comprehends Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib.
Davidson-Pilon focused on improving learners’ understanding of the motivations, applications, and challenges in Bayesian statistics and probabilistic programming. Moreover, this book brings a much-needed introduction to Bayesian methods targeted at practitioners. Therefore, you can reap the most benefit from this book if you have a prior sound understanding of statistics. Knowing about prior and posterior probabilities will give an added advantage to the reader in building and training the first Bayesian model.
The second part of the book introduces the probabilistic programming library for Python through a series of detailed examples and intuitive explanations, with recent core developments and the popularity of the scientific stack in Python, PyMC is likely to become a core component soon enough. PyMC does have dependencies to run, namely NumPy and (optionally) SciPy. To not limit the user, the examples in this book will rely only on PyMC, NumPy, SciPy, and Matplotlib. This book is filled with examples, figures, and Python code that make it easy to get started solving actual problems.
3. Practical Statistics for Data Scientists – By Peter Bruce and Andrew Bruce
This book is most beneficial for readers that have some basic understanding of R programming language and statistics.
The authors penned the important concepts to teach practical statistics in data science and covered data structures, datasets, random sampling, regression, descriptive statistics, probability, statistical experiments, and machine learning. The code is available in both Python and R. If an example code is offered with this book, you may use it in your programs and documentation.
The book defines the first step in any data science project that is exploring the data or data exploration. Exploratory data analysis is a comparatively new area of statistics. Classical statistics focused almost exclusively on inference, a sometimes-complex set of procedures for drawing conclusions about large populations based on small samples.
To apply the statistical concepts covered in this book, unstructured raw data must be processed and manipulated into a structured form—as it might emerge from a relational database—or be collected for a study.
4. Advanced Engineering Mathematics by Erwin Kreyszig
Advanced Engineering Mathematics is a textbook for advanced engineering and applied mathematics students. The book deals with calculus of vector, tensor and differential equations, partial differential equations, linear elasticity, nonlinear dynamics, chaos theory and applications in engineering.
Advanced Engineering Mathematics is a textbook that focuses on the practical aspects of mathematics. It is an excellent book for those who are interested in learning about engineering and its role in society. The book is divided into five sections: Differential Equations, Integral Equations, Differential Mathematics, Calculus and Probability Theory. It also provides a basic introduction to linear algebra and matrix theory. This book can be used by students who want to study at the graduate level or for those who want to become engineers or scientists.
The text provides a self-contained introduction to advanced mathematical concepts and methods in applied mathematics. It covers topics such as integral calculus, partial differentiation, vector calculus and its applications to physics, Hamiltonian systems and their stability analysis, functional analysis, classical mechanics and its applications to engineering problems.
The book includes a large number of problems at the end of each chapter that helps students develop their understanding of the material covered in the chapter.
5. Computer Age Statistical Inference by Bradley Efron and Trevor Hastie
Computer Age Statistical Inference is a book aimed at data scientists who are looking to learn about the theory behind machine learning and statistical inference. The authors have taken a unique approach in this book, as they have not only introduced many different topics, but they have also included a few examples of how these ideas can be applied in practice.
The book starts off with an introduction to statistical inference and then progresses through chapters on linear regression models, logistic regression models, statistical model selection, and variable selection. There are several appendices that provide additional information on topics such as confidence intervals and variable importance. This book is great for anyone looking for an introduction to machine learning or statistics.
Computer Age Statistical Inference is a book that introduces students to the field of statistical inference in a modern computational setting. It covers topics such as Bayesian inference and nonparametric methods, which are essential for data science. In particular, this book focuses on Bayesian classification methods and their application to real world problems. It discusses how to develop models for continuous and discrete data, how to evaluate model performance, how to choose between parametric and nonparametric methods, how to incorporate prior distributions into your model, and much more.
5 Beginner level statistics books for data science
6. How to Lie with Statistics by Darrell Huff
How to Lie with Statistics is one of the most influential books about statistical inference. It was first published in 1954 and has been translated into many languages. The book describes how to use statistics to make your most important decisions, like whether to buy a house, how much money to give to charity, and what kind of mortgage you should take out. The book is intended for laymen, as it includes illustrations and some mathematical formulas. It’s full of interesting insights into how people can manipulate data to support their own agendas.
The book is still relevant today because it describes how people use statistics in their daily lives. It gives an understanding of the types of questions that are asked and how they are answered by statistical methods. The book also explains why some results seem more reliable than others.
The first half of the book discusses methods of making statistical claims (including how to make improper ones) and illustrates these using examples from real life. The second half provides a detailed explanation of the mathematics behind probability theory and statistics.
A common criticism of the book is that it focuses too much on what statisticians do rather than why they do it. This is true — but that’s part of its appeal!
7. Head first Statistics: A Brain-Friendly Guide Book by Dawn Griffiths
If you are looking for a book that will help you understand the basics of statistics, then this is the perfect book for you. In this book, you will learn how to use data and make informed decisions based on your findings. You will also learn how to analyze data and draw conclusions from it.
This book is ideal for those who have already completed a course in statistics or have studied it in college. Griffiths has given an overview of the different types of statistical tests used in everyday life and provides examples of how to use them effectively.
The book starts off with an explanation of statistics, which includes topics such as sampling, probability, population and sample size, normal distribution and variation, confidence intervals, tests of hypotheses and correlation.
After this section, the book goes into more advanced topics such as regression analysis, hypothesis testing etc. There are also some chapters on data mining techniques like clustering and classification etc.
The author has explained each topic in detail for the readers who have little knowledge about statistics so they can follow along easily. The language used throughout this book is very clear and simple which makes it easy to understand even for beginners.
8. Think Stats By Allen B. Downey
Think Stats is a great book for students who want to learn more about statistics. The author, Allen Downey, uses simple examples and diagrams to explain the concepts behind each topic. This book is especially helpful for those who are new to mathematics or statistics because it is written in an easy-to-understand manner that even those with a high school degree can understand.
The book begins with an introduction to basic counting, addition, subtraction, multiplication and division. It then moves on to finding averages and making predictions about what will happen if one number changes. It also covers topics like randomness, sampling techniques, sampling distributions and probability theory.
The author uses real-world examples throughout the book so that readers can see how these concepts apply in their own lives. He also includes exercises at the end of each chapter so that readers can practice what they’ve learned before moving on to the next section of the book. This makes Think Stats an excellent resource for anyone looking for tips on improving their math skills or just wanting to brush up on some statistical basics!
9. An Introduction To Statistical Learning With Applications In R By Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
Statistical learning with applications in R is a guide to advanced statistical learning. It introduces modern machine learning techniques and their applications, including sequential decision-making, Gaussian mixture models, boosting, and genetic programming. The book covers methods for supervised and unsupervised learning, as well as neural networks. The book also includes chapters on Bayesian statistics and deep learning.
It begins with a discussion of correlation and regression analysis, followed by Bayesian inference using Markov chain Monte Carlo methods. The authors then discuss regularization techniques for regression models and introduce boosting algorithms. This section concludes with an overview of neural networks and convolutional neural networks (CNNs). The remainder of the book deals with topics such as kernel methods, support vector machines (SVMs), regression trees (RTs), naive Bayes classifiers, Gaussian processes (GP), gradient ascent methods, and more.
This statistics book is recommended to researchers willing to learn about statistical machine learning but do not have the necessary expertise in mathematics or programming languages
10. Statistics in Plain English By Timothy C. Urdan
Statistics in Plain English is a writing guide for students of statistics. Timothy in his book covered basic concepts with examples and guidance for using statistical techniques in the real world. The book includes a glossary of terms, exercises (with solutions), and web resources.
The book begins by explaining the difference between descriptive statistics and inferential statistics, which are used to draw conclusions about data. It then covers basic vocabulary such as mean, median, mode, standard deviation, and range.
In Chapter 2, the author explains how to calculate sample sizes that are large enough to make accurate estimates. In Chapters 3–5 he gives examples of how to use various kinds of data: census data on population density; survey data on attitudes toward various products; weather reports on temperature fluctuations; and sports scores from games played by teams over time periods ranging from minutes to seasons. He also shows how to use these data to estimate the parameters for models that explain behavior in these situations.
The last 3 chapters define the use of frequency distributions to answer questions about probability distributions such as whether there’s a significant difference between two possible outcomes or whether there’s a trend in a set of numbers over time or space
Which data science statistics books are you planning to get?
Build upon your statistical concepts and successfully step into the world of data science. Analyze your knowledge and choose the most suitable book for your career to enhance your data science skills. If you have any more suggestions for statistics books for data science, please share them with us in the comments below.
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This blog will discuss the different Natural Language Processing applications. We will see the applications and what problems they solve in our daily life.
Introduction
One of the essential things in the life of a human being is communication. We need to communicate with other human beings to deliver information, express our emotions, present ideas, and much more. The key to communication is language. We need a common language to communicate, which both ends of the conversation can understand. Doing this is possible for humans, but it might seem a bit difficult if we talk about communicating with a computer system or the computer system communicating with us.
But we have a solution for that, Artificial Intelligence, or more specifically, a branch of Artificial Intelligence known as Natural Language Processing (NLP). Natural Language Processing enables the computer system to understand and comprehend information the same way humans do. It helps the computer system understand the literal meaning and recognize the sentiments, tone, opinions, thoughts, and other components that construct a proper conversation.
Applications of Natural Language Processing
After making the computer understand human language, a question arises in our minds, how can we utilize this ability of a computer to benefit humankind?
Natural Language Processing Applications:
Let’s answer this question by going over some Natural Language Processing applications and understanding how they decrease our workload and help us complete many time-taking tasks more quickly and efficiently.
1. Email filtering
Email is a part of our everyday life. Whether it is related to work or studies or many other things, we find ourselves plunged into the pile of emails. We receive all kinds of emails from various sources; some are work-related or from our dream school or university, while others are spam or promotional emails. Here Natural Language Processing comes to work. It identifies and filters incoming emails into “important” or “spam” and places them into their respective designations.
2. Language translation
There are as many languages in this world as there are cultures, but not everyone understands all these languages. As our world is now a global village owing to the dawn of technology, we need to communicate with other people who speak a language that might be foreign to us. Natural Language processing helps us by translating the language with all its sentiments.
3. Smart assistants
In today’s world, every new day brings in a new smart device, making this world smarter and smarter by the day. And this advancement is not just limited to machines. We have advanced enough technology to have smart assistants, such as Siri, Alexa, and Cortana. We can talk to them like we talk to normal human beings, and they even respond to us in the same way.
All of this is possible because of Natural Language Processing. It helps the computer system understand our language by breaking it into parts of speech, root stem, and other linguistic features. It not only helps them understand the language but also in processing its meaning and sentiments and answering back in the same way humans do.
4. Document analysis
Another one of NLP’s applications is document analysis. Companies, colleges, schools, and other such places are always filled to the brim with data, which needs to be sorted out properly, maintained, and searched for. All this could be done using NLP. It not only searches a keyword but also categorizes it according to the instructions and saves us from the long and hectic work of searching for a single person’s information from a pile of files. It is not only limited to this but also helps its user to inform decision-making on claims and risk management.
5. Online searches
In this world full of challenges and puzzles, we must constantly find our way by getting the required information from available sources. One of the most extensive information sources is the internet. We type what we want to search and checkmate! We have got what we wanted. But have you ever thought about how you get these results even when you do not know the exact keywords you need to search for the needed information? Well, the answer is obvious.
It is again Natural Language Processing. It helps search engines understand what is asked of them by comprehending the literal meaning of words and the intent behind writing that word, hence giving us the results, we want.
6. Predictive text
A similar application to online searches is predictive text. It is something we use whenever we type anything on our smartphones. Whenever we type a few letters on the screen, the keyboard gives us suggestions about what that word might be and when we have written a few words, it starts suggesting what the next word could be. These predictive texts might be a little off in the beginning.
Still, as time passes, it gets trained according to our texts and starts to suggest the next word correctly even when we have not written a single letter of the next word. All this is done using NLP by making our smartphones intelligent enough to suggest words and learn from our texting habits.
7. Automatic summarization
With the increasing inventions and innovations, data has also increased. This increase in data has also expanded the scope of data processing. Still, manual data processing is time taking and is prone to error. NLP has a solution for that, too, it can not only summarize the meaning of information, but it can also understand the emotional meaning hidden in the information. Thus, making the summarization process quick and impeccable.
8. Sentiment analysis
The daily conversations, the posted content and comments, book, restaurant, and product reviews, hence almost all the conversations and texts are full of emotions. Understanding these emotions is as important as understanding the word-to-word meaning. We as humans can interpret emotional sentiments in writings and conversations, but with the help of natural language processing, computer systems can also understand the sentiments of a text along with its literal meaning.
9. Chatbots
With the increase in technology, everything has been digitalized, from studying to shopping, booking tickets, and customer service. Instead of waiting a long time to get some short and instant answers, the chatbot replies instantly and accurately. NLP gives these chatbots conversational capabilities, which help them respond appropriately to the customer’s needs instead of just bare-bones replies.
Chatbots also help in places where human power is less or is not available round the clock. Chatbots operating on NLP also have emotional intelligence, which helps them understand the customer’s emotional sentiments and respond to them effectively.
10. Social media monitoring
Nowadays, every other person has a social media account where they share their thoughts, likes, dislikes, experiences, etc., which tells a lot about the individuals. We do not only find information about individuals but also about the products and services. The relevant companies can process this data to get information about their products and services to improve or amend them. NLP comes into play here. It enables the computer system to understand unstructured social media data, analyze it and produce the required results in a valuable form for companies.
Conclusion:
We now understand that NLP has many applications, spreading its wings in almost every field. Help decrease manual labor and do the tasks accurately and efficiently.
50 self-explanatory data science quotes by thought leaders you need to read if you’re a Data Scientist, – covering the four core components of data science landscape.
Data science for anyone can seem scary. This made me think of developing a simpler approach to it. To reinforce a complicated idea, quotes can do wonders. Also, they are a sneak peek into the window of the author’s experience. With precise phrasing with chosen words, it reinstates a concept in your mind and offers a second thought to your beliefs and understandings.
In this article, we jot down 51 data science quotes that were once shared by experts. So, before you let the fear of data scienceget to you, browse through the wise words of industry experts divided into four major components to get inspired.
Data strategy
If you successfully devise a data strategy with the information available, then it will help you to debug a business problem. It builds a connection to the data you gather and the goals you aim to achieve with it. Here are fiveinspiring and famous data strategy quotes by Bernard Marr from his book, “Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things”
“Those companies that view data as a strategic asset are the ones that will survive and thrive.”
“Doesn’t matter how much data you have, it’s whether you use it successfully that counts.”
“If every business, regardless of size, is now a data business, every business, therefore, needs a robust data strategy.”
“They need to develop a smart strategy that focuses on the data they really need to achieve their goals.”
“Data has become one of the most important business assets, and a company without a data strategy is unlikely to get the most out of their data resources.”
Some other influential data strategy quotes are as follows:
6. “Big data is at the foundation of all of the megatrends that are happening today, from social to mobile to the cloud to gaming.” – Chris Lynch, Former CEO, Vertica
7. “You can’t run a business today without data. But you also can’t let the numbers drive the car. No matter how big your company is or how far along you are, there’s an art to company-building that won’t fit in any spreadsheet.” Chris Savage, CEO, Wistia
8. “Data science is a combination of three things: quantitative analysis (for the rigor required to understand your data), programming (to process your data and act on your insights), and narrative (to help people comprehend what the data means).” — Darshan Somashekar, Co-founder,at Unwind media
9. “In the next two to three years, consumer data will be the most important differentiator. Whoever is able to unlock the reams of data and strategically use it will win.” — Eric McGee, VP Data and Analytics
10. “Data science isn’t about the quantity of data but rather the quality.” — Joo Ann Lee, Data Scientist, Witmer Group
11. “If someone reports close to a 100% accuracy, they are either lying to you, made a mistake, forecasting the future with the future, predicting something with the same thing, or rigged the problem.” — Matthew Schneider, Former United States Attorney
12. “Executive management is more likely to invest in data initiatives when they understand the ‘why.’” — Della Shea, Vice President of Privacy and Data Governance, Symcor
13. “If you want people to make the right decisions with data, you have to get in their head in a way they understand.” — Miro Kazakoff, Senior Lecturer, MIT Sloan
14. “Everyone has the right to use company data to grow the business. Everyone has the responsibility to safeguard the data and protect the business.” — Travis James Fell, CSPO, CDMP,– Product Manager
15. “For predictive analytics, we need an infrastructure that’s much more responsive to human-scale interactivity. The more real-time and granular we can get, the more responsive, and more competitive, we can be.” Peter Levine, VC and General Partner ,Andreessen Horowitz
Data engineering
Without a sophisticated system or technology to access, organize, and use the data, data science is no less than a bird without wings. Data engineering builds data pipelines and endpoints to utilize the flow of data. Check out these top quotes on data engineering by thought leaders:
16. “Defining success with metrics that were further downstream was more effective.” John Egan, Head of Growth Engineer, Pinterest
17. ” Wrangling data is like interrogating a prisoner. Just because you wrangled a confession doesn’t mean you wrangled the answer.” — Brad Schneider – Politician
18. “If you have your engineering team agree to measure the output of features quarter over quarter, you will get more features built. It’s just a fact.” Jason Lemkin, Founder, SaaStr Fund
19. “Data isn’t useful without the product context. Conversely, having only product context is not very useful without objective metrics…” Jonathan Hsu, CFO, and COO, AppNexus & Head of Data Science,at Social Capital
20. “I think you can have a ridiculously enormous and complex data set, but if you have the right tools and methodology, then it’s not a problem.” Aaron Koblin, Entrepreneur in Data and Digital Technologies
21. “Many people think of data science as a job, but it’s more accurate to think of it as a way of thinking, a means of extracting insights through the scientific method.” — Thilo Huellmann, Co-fFounder,at Levity
22. “You want everyone to be able to look at the data and make sense out of it. It should be a value everyone has at your company, especially people interacting directly with customers. There shouldn’t be any silos where engineers translate the data before handing it over to sales or customer service. That wastes precious time.” Ben Porterfield, Founder and VP of Engineering,at Looker
23. “Of course, hard numbers tell an important story; user stats and sales numbers will always be key metrics. But every day, your users are sharing a huge amount of qualitative data, too — and a lot of companies either don’t know how or forget to act on it.” Stewart Butterfield, CEO, Slack
Data analysis and models
Every business is bombarded with a plethora of data every day. When you get tons of data, analyze it and make impactful decisions. Data analysis usesstatistical and logical techniques to model the use of data:.
24. “In most cases, you can’t build high-quality predictive models with just internal data.” — Asif Syed, Vice President of Data Strategy, Hartford Steam Boiler
25. “Since most of the world’s data is unstructured, an ability to analyze and act on it presents a big opportunity.” — Michael Shulman, Head of Machine Learning, Kensho
26. “It’s easy to lie with statistics. It’s hard to tell the truth without statistics.” — Andrejs Dunkels, Mathematician, and Writer
27. “Information is the oil of the 21st century, and analytics is the combustion engine.” Peter Sondergaard, Senior Vice President, Gartner Research
28. “Use analytics to make decisions. I always thought you needed a clear answer before you made a decision and the thing that he taught me was [that] you’ve got to use analytics directionally…and never worry whether they are 100% sure. Just try to get them to point you in the right direction.” Mitch Lowe, Co-founder of Netflix
29. “Your metrics influence each other. You need to monitor how. Don’t just measure which clicks generate orders. Back it up and break it down. Follow users from their very first point of contact with you to their behavior on your site and the actual transaction. You have to make the linkage all the way through.” Lloyd Tabb, Founder, Looker
30. “Don’t let shallow analysis of data that happens to be cheap/easy/fast to collect nudge you off-course in your entrepreneurial pursuits.” Andrew Chen, Partner at Andreessen Horowitz,
31. “Our real job with data is to better understand these very human stories, so we can better serve these people. Every goal your business has is directly tied to your success in understanding and serving people.” — Daniel Burstein, Senior Director, Content & Marketing, MarketingSherpa
32. “A data scientist combines hacking, statistics, and machine learning to collect, scrub, examine, model, and understand data. Data scientists are not only skilled at working with data, but they also value data as a premium product.” — Erwin Caniba, Founder and Owner,Digitacular Marketing Solutions
33. “It has therefore become a strategic priority for visionary business leaders to unlock data and integrate it with cloud-based BI and analytic tools.” — Gil Peleg, Founder , Model 9 – Crunchbase
34. “The role of data analytics in an organization is to provide a greater level of specificity to discussion.” — Jeff Zeanah, Analytics Consultant
35. “Data is the nutrition of artificial intelligence. When an AI eats junk food, it’s not going to perform very well.” — Matthew Emerick, Data Quality Analyst
36. “Analytics software is uniquely leveraged. Most software can optimize existing processes, but analytics (done right) should generate insights that bring to life whole new initiatives. It should change what you do, not just how you do it.” Matin Movassate, Founder, Heap Analytics
37. “No major multinational organization can ever expect to clean up all of its data – it’s a never-ending journey. Instead, knowing which data sources feed your BI apps, and the accuracy of data coming from each source, is critical.” — Mike Dragan, COO, – Oveit
38. “All analytics models do well at what they are biased to look for.” — Matthew Schneider, Strategic Adviser
39. “Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” Geoffrey Moore, Author and Consultant
Data visualization and operationalization
When you plan to take action with your data, you visualize it on a very large canvas. For an actionable insight, you must squeeze the meaning out of all the analysis performed on that data, this is data visualization. Some data visualization quotes that might interest you are:
40. “Companies have tons and tons of data, but [success] isn’t about data collection, it’s about data management and insight.” — Prashanth Southekal, Business Analytics Author
41. “Without clean data, or clean enough data, your data science is worthless.” — Michael Stonebraker, Adjunct Professor, MIT
42. “The skill of data storytelling is removing the noise and focusing people’s attention on the key insights.” — Brent Dykes, Author, “Effective Data Storytelling”
43. “In a world of more data, the companies with more data-literate people are the ones that are going to win.” — Miro Kazakoff, Senior Lecturer, MIT Sloan
44. The goal is to turn data into information and information into insight. Carly Fiorina, Former CEO, Hewlett Packard
45. “Data reveals impact, and with data, you can bring more science to your decisions.” Matt Trifiro, CMO, at Vapor IO
46. “The skill of data storytelling is removing the noise and focusing people’s attention on the key insights.” — Brent Dykes, data strategy consultant and author, “Effective Data Storytelling”
47. “In a world of more data, the companies with more data-literate people are the ones that are going to win.” — Miro Kazakoff, Senior Lecturer, MIT Sloan
48. “One cannot create a mosaic without the hard small marble bits known as ‘facts’ or ‘data’; what matters, however, is not so much the individual bits as the sequential patterns into which you organize them, then break them up and reorganize them'” — Timothy Robinson, Physician Scientist
49. “Data are just summaries of thousands of stories–tell a few of those stories to help make the data meaningful.” Chip and Dan Heath, Authors of Made to Stick and Switch
Parting thoughts on amazing data science quotes
Each quote by industry experts or experienced professionals provides us with insights to better understand the subject. Here are the final quotes for both aspiring and existing data scientists:
50. “The self-taught, un-credentialed, data-passionate people—will come to play a significant role in many organizations’ data science initiatives.” – Neil Raden, Founder, and Principal Analyst, Hired Brains Research.
51. “Data scientists are involved with gathering data, massaging it into a tractable form, making it tell its story, and presenting that story to others.” – Mike Loukides, Editor, O’Reilly Media.
Have we missed any of your favorite quotes on data? Or do you have any thoughts on the data quotes shared above? Let us know in the comments.
Data engineering,Data science quote,Data strategy,Data visualization
The current world relies on data for things to run smoothly. There have been multiple research projects on nonverbal communication and many researchers came to comparable results that 93% of all communication is nonverbal. Whether you are scrolling on social media or watching television, you are consuming data. Data professionals strongly believe that data can create or break your business brand.
The concept of content marketing strategy requires you to have a unique operating model to attain your business objective. Remember that everybody is busy, and no one has time to read dull content on the internet. This is where the art of data visualization comes in to help the dreams of many digital marketers come true. Below are some practical data visualization tips that you can use to supercharge your content strategy!
1. Invest in accurate data
Everybody loves to read the information they can rely on and use in decision-making. When you present data to your audience in form of visualization make sure the data is accurate and mention it’s source to gain the trust of your audience. You need to ensure that all the information you have is highly accurate and can be utilized in decision-making.
If your business brand presents inaccurate data, you are likely to lose many potential clients who depend on your Company. Obviously, customers are likely to come and view your visual content, but they won’t be happy because your data is inaccurate. Remember that there is no harm in gathering information from a third-party source. You only need to ensure that the information is accurate.
According to the ERP-information data can never be 100% accurate but it can be more or less accurate depending on how close it adheres to reality. The closer that data sticks to reality, the higher its accuracy.
2. Use real-time data to be unique
Posting real-time data is an excellent way of attracting a significant number of potential customers. Many people opt for brands that present data on time, depending on the market situation. This strategy proved to be efficient during the black Friday season, whereby companies recorded a significant number of sales within the shortest time.
In addition, real-time data plays a critical role in building trust between a brand and its customers. When customers realize that you are posting things that are just happening, their level of true skyrockets.
3. Create a story
Once you have decided about including visual content in your content strategy, you also need to find out an exciting story that the visual will present to the audience. Before you start authoring the story, think about the ins and outs of your content to ensure that you have nailed everything in your head.
You can check out the types of visual content that have been created by some of the big brands on the internet. Try to mimic how these brands present their stories to the audience.
4. Promote visualizations perfectly
Promoting Visualizations does not mean that you need to spend the whole day working on a single visualization. Create simpler and more interactive excel charts (Bar chart, Line chart, Sankey diagram, and Box and Whisker Plot, etc.) to encourage your audience. This is not what promoting means! It means that you need to communicate to your audience directly through different social media platforms.
Also, you can opt to send direct emails, given the fact that you have their contact details. The ultimate goal of this campaign is to make your visual go viral across the internet and reach as many people as possible. Ensure that you know your target audience to make your efforts yield profit.
5. Gather and present unique data
Data visualization plays a fundamental role when developing a unique identity for your brand. You have the power to use visualization to make your brand stand out from your competitors. Collecting and presenting unique data gives you an added advantage in business that makes you unique.
To achieve this level of data, you need to conduct in-depth research and dig down across different variables to find unique data. Even though it may sound simple, this is not the case. Also, selecting a massive set of data is simple, but the complexity comes with when selecting the most appropriate data points.
6. Know your audience
Getting to know your audience is a fundamental aspect that you should always consider. It gives you detailed insights not about understanding the nature of your content but also about promoting your visualization. To be able to encourage your visualization ideally, you need to understand your audience.
When designing different visualization types, you should also channel all your eye to the platform you are targeting. Decide on the media where you are sharing various types of content depending on the nature of the audience available on the respective platforms.
7. Understand your craft
Conduct in-depth research to understand what works for you and what doesn’t work. For instance, one of the benefits of data visualization is that it reduces the time it takes to read through loads of content. If you are mainly writing content for your readers to share across the market audience, a maximum of two hundred and thirty words is enough.
Data visualization is an art and science that requires you to conduct remarkable research to uncover essential information. Once you uncover the necessary information, you will definitely get to know your craft.
8. Learn from the best
The digital marketing world involves continuous learning to remain at the top of the game. The best way to learn in business is to monitor what the developed brands are doing to succeed. You can learn the content strategy used by international companies such as Netflix to get a test of what it means to promote your brand across its target market.
9. Gather the respective data visualization tool
After conducting your research and settling on a story that reciprocates your brand, you have to gather the Respective tools necessary to generate the story you need. You would acquire creative tools with a successful track record of developing quality output.
There are multiple data visualization tools on the web that you can choose and use. However, some people recommend starting from scratch, depending on the nature of the output they want. Some famous data visualization tools are Tableau, Microsoft Excel, Power BI, ChartExpo, and Plotly.
10. Research and testing
Do not forget about the power of research and testing. Acquire different tools to help you conduct research and test different elements to check if they can work and generate the desired results. You should be keen to analyze what can work for your business and what cannot.
Need for data visualization
The business world is in dire need of data visualization to enhance competitive content strategies. A study done by the Wharton School of Business has revealed that data visualization can shorten a business meeting by 24% since all the essential elements are outlined clearly. However, to grab the attention of your target market, you need to come up with something unique to be successful.
Are you interested in learning more about IoT? Do you want to network with people working in IoT? Here is a list of 10 IoT conferences and events that can help you learn more about the new research and developments, and help you network and meet recruiters or project owners.
The ongoing development of the Internet of Things (IoT) is a major driver of digital transformation.
Data processing, data visualization, and many other techniques are just a few of the innovative technologies that may be combined to create new possibilities and solutions that demand improved integration and collaboration.
1. 4th Asia IoT Technologies Conference– Beijing, China
Scheduled to be held in Beijing, China, from 6th to 8th January 2023, the Asia IoT Technologies Conference, sponsored by Beijing Huaxia Rongzhi Institute of Blockchain (BJIB), co-sponsored by Beijing University of Technology (China) and the Faculty of Information Technology (BJUT, China).
The conference will focus on core technologies and IoT applications to promote the integration of IoT and the economy for industrial and economic purposes.
The conference features a broad range of programs and talks on the latest developments in the field. The main aim of the conference is to deepen the understanding of the masses and take the necessary actions to accelerate the adoption of IoT with an emphasis on diverse topics across the IoT landscape.
More details regarding the conference can be found here.
2. International Conference on Innovations in Data Analytics ICIDA – Kolkata, India
Taking place on November 29-30, 2022, the International Conference on Innovations in Data Analytics ICIDA will be organized by International Knowledge Research Foundation in collaboration with Eminent College of Management and Technology (ECMT), West Bengal, India.
The main aim of the conference is to bring together innovators, academics, and business specialists in the fields of Computing and Communication at one place. The conference also aims to inspire young scholars to learn newly created avenues of research at an international academic forum.
More details regarding the conference can be found here.
3. Asia IoT Business Platform – Southeast Asia
Taking place in different cities in Southeast Asia from October to December, the Asia IoT Business Platform aims to serve public and private organizations to enable their access and exchange of knowledge on development and innovation in the B2B sector.
The conferences help create partnerships within the tech and IoT sectors and help provide better collaborations between public and private organizations.
The AIBP conferences and exhibitions also promote market research and access gained via the creation and implementation of business growth strategies.
The IoT India Expo will be held from the 27th to the 29th of March 2023 and will feature numerous companies working in IT, enabling them to enter a new market more quickly and with more accurate data through the adoption of modern technologies.
For anyone in the IT sector, the event is a good place to network and talk about the future of technology because it is the premier enterprise event for IoT, Blockchain, AI, Big Data, Cyber Security, and Cloud.
More details regarding the expo can be found here.
5. Cloud Expo Asia – Marina Bay Sands, Singapore
One of the leading IoT events in Asia, Cloud Expo Asia Is expected to be held from 12th to 13th October 2022.
The main aim of the event is to connect people from academia and professionals with experts in the field to find sustainable solutions and services that can help accelerate digital transformation.
With multiple conferences, shows, speaker sessions, and much more lined up, the event focuses on a large variety of topics.
6. IEEE 8th World Forum on Internet of Things (WF-IoT) – Yokohama, Japan
One of the events organized by the Multi-Society IEEE IoT Initiative WF-IOT will take place from the 26th of October till the 11th of November.
The conference highlights the latest developments in IoT, business, and private and public sectors.
The main aim of the forum is to promote the development and promotion of IoT for society’s and humanity’s benefit, as well as to promote the ethical and responsible use of IoT applications and solutions to improve human lives.
The theme of the event this year is ‘Sustainability and the Internet of Things.’
7. Internet of Things World, Asia – Marina Bay Sands, Singapore
From edge computing to digital transformations, the Internet of Things World covers all aspects of IoT. The event is expected to be held tentatively in October.
The main aim of the event is always to teach professionals, industrialists, and academics, the importance of monetizing IoT and how it can effectively impact business models.
8. EAI International Conference on Industrial Networks and Intelligent Systems – Vietnam
The International Conference on Industrial Networks and Intelligent Systems focuses on the current state of AI and 6G Convergence in Models, Technologies, and Applications related to IoT.
The conference also highlights any issues pertaining to IoT Networks for Smart Cities, Next Generation Networks Infrastructures, and Optical Spectrum–LiFi.
9. International Conference on Internet of Medical Things (ICIMT) – UAE
The International Conference on the Internet of Medical Things (ICIMT) focuses on exchanging experiences and research findings within the Internet of Medical Things.
The conference gives researchers, practitioners, and educators a world-class interdisciplinary forum on which to present and debate the most recent advancements, concerns, and trends on the Internet of Medical Things.
10. IEEE International Conference on IoT and Blockchain Technology – India
The conference that led to revolutionizing the IoT industry, reevaluating business structures, and IoT, covers interoperability, data, and service mashups.
Moreover, the development of open platforms and standardization across technological levels is also focused on throughout the conference.
More details regarding the conference can be found here.
Find similar IoT conferences
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If you are interested in learning more about machine learning and data science, click here.
A data science portfolio is a great way to show off your skills and talents to potential employers. It can be difficult to stand out in the competitive data science job market, but with a strong data science portfolio, you will have an edge over the competition.
In this post, we will discuss three easy ways to make your data science portfolio stand out. Let’s get started!
What does a data science portfolio include?
Adata science portfolio is a collection of your work that demonstrates your skills and abilities in data science. These portfolios typically include a mix of scripts of code fromdata science projects you’ve worked on, data visualizations you made, and write-ups on personal projects you’ve completed.
When applying for data science positions, your potential employer will want to see your data science portfolio. Employers use portfolios as a way to evaluate candidates, so it is important that your data science portfolio is well-crafted and showcases your best work.
Why is it important for your data science portfolio to stand out?
Withdata scientist jobs being highly favored among the Gen Z workforce, the competition for such data science roles is starting to heat up. With many pursuing careers in data science, you’ll need to find ways to stand out among the crowd.
Having an excellent data science portfolio is important for 3 main reasons:
It acts as an extension of your resume
It shows expertise in using certain tools
It demonstrates your problem-solving approaches
Now let me go through some ways you can make your data science portfolio stronger than most others.
What are 3 easy ways for your data science portfolio to stand out?
Your data science portfolio should be a reflection of your skills and experience.
With that said, here are three easy ways to make your data science portfolio stand out:
Much like your resume, employers are likely to spend less than one minute looking at your data science portfolio. To make a lasting impression, your data science portfolio should be heavily focused on visuals.
Some data science portfolios I love are those that use data visualizations to tell a story. A great data visualization can communicate complex information in an easily digestible format.
Here are some guidelines you can follow:
Ensure it is visually appealing and easy to navigate
Include screenshots, graphs, and charts to make your data science portfolio pop
Explain any insights found in the visualizations
Including data visualizations in your data science portfolio will help you stand out from the competition and communicate your skills effectively.
Since data visualizations are a big part of data science work, I’d recommend showing off some charts and dashboards you’ve created. If you’ve used Python in any of yourdata analytics certificates, do include any line charts, bar graphs, and plots you have created using Plotly/Seaborn in your data science portfolio.
If you’ve created some dashboards inTableau, do publish them on Tableau Public and link that up to your portfolio site. Or if you’re aPower BI user, do take screenshots/GIFs of the dashboard in use and include them in your portfolio.
Having visuals to represent your work can make a huge impact and will help you stand out from the rest. This is just one example of how you can make your data science portfolio stand out with visuals.
Let’s move on.
2. Include links to popular data science platforms
A strong data science portfolio should include links to popular data science platforms as well. By having links of popular data science tools in your portfolio, your employers would perceive you as having higher credibility.
This credibility comes from the demonstration of your experience and skills since many data science hiring managers use these platforms often themselves.
Some common platforms to link and display your work include:
GitHub
Kaggle
Stack Overflow
RPubs
Tableau Public
If you’re someone who has had several machine learning projects done in Python, do upload them to your personal GitHub account so others can read your code. By linking your GitHub repo links to your portfolio, employers can take a glimpse at your coding quality and proficiency!
One tip I’d recommend is to include a README file for your GitHub profile and customize it to showcase the data science skills and programming languages you’ve learned.
3. Write blog posts to complement your projects
The last way to create an outstanding data science portfolio is to document your projects in writing – via blog posts!
Having comprehensive and concise blog posts on your data science portfolio shows employers your thought process and how you approached each project. This is a great way to demonstrate your problem-solving skills and how you can solve business problems through analytics for your employer.
For example, if you’ve written some scripts in R for yourdata mining project and would like to help your employers understand the steps you took, writing an accompanying blog post would be perfect. In this case, I’d recommend trying to document everything in Rmarkdown as I didhere.
If you’re interested to publish more data science content to further boost your data science portfolio, do consider these platforms:
Medium
TowardsDataScience
WordPress (your own blog site)
By writing blog posts, you’re able to provide more context and explanation for eachdata science project in your portfolio. As a result, employers would be able to appreciate your work even more.
By following these three easy tips, you can make your data science portfolio stand out from the competition. I hope these tips will help you in perfecting your portfolio and I wish you all the best in your data science career.
Thanks for reading!
Author bio
Austin Chia is the Founder of Any Instructor, where he writes about tech, analytics & software. After breaking into data science without a degree, he seeks to help others learn about all things data science and tech. He has previously worked as a data scientist at a healthcare research institute and a data analyst at a health-tech startup.
Data science,data science portfolio,Data visualizations
From managing your cash flow to making lending decisions for you, here is a list of 15 fintech startups using Artificial Intelligence to enhance your experience.
1. Affirm is changing the way people buy stuff
Affirm is a consumer application that grants loans for purchases at various retailers. The startup makes use of multiple machine learning algorithms for credit underwriting and happens to be the exclusive buy now, pay later partner for Amazon.
Max Levchin, the co-founder of PayPal, along with Nathan Gettings, Jeffrey Kaditz, and Alex Rampell introduced Affirm in 2012 to the world.
Affirm also partnered with Walmart in 2019, allowing customers to access the app in-store and on Walmart’s website.
Fintech startup, HighRadius provides a Software-as-a-Service company (SaaS). The startup makes use of AI-based autonomous systems to help automate Accounts Receivable and Treasury processes.
HighRadius provides high operational efficiency, accurate cash flow forecasting, and much more to help companies achieve strong ROI.
3. SparkCognition is building smarter, safer, and sustainable solutions for the future
SparkCognition focuses on creating AI-powered cyber-physical software for the safety, security, and reliability of IT, OT, and the IoT. The startup builds artificial intelligence solutions for applications in energy, oil and gas, manufacturing, finance, aerospace, defense, and security.
The startup’s work in the financial sector enables businesses to improve analytical accuracy, minimize risks, accelerate reaction time to fluctuating market conditions, and sustain a competitive advantage.
Previously, SparkCognition enabled a fintech startup to use a machine learning model to detect fraud with 90% accuracy, saving the company over $450K each year.
4. ZestFinance helps cut losses and increase revenue
ZestFinance Logo
Another popular name in the financial AI industry, ZestFinance enables companies by helping them increase approval rates, cut credit losses, and improve underwriting using machine learning.
Moreover, the startup helps lenders predict credit risk so they can increase revenues, reduce risk & ensure compliance.
The main aim of the startup is to grant fair and transparent credit access to everyone and build an equitable financial system.
5. Upstart investigates the financial background and gives you a lower rate of lending
Based on a very cool concept, Upstart first checks your education and job history, then helps understand more about your future potential to eventually get the user a lower rate for lending.
According to the startup itself, they look beyond a person’s credit score for personal loans, car loan refinance, and small business loans.
6. Vise AI is the financial advisor of the future.
An AI-driven asset management platform, Vise AI is built and designed specifically as a financial advisory platform.
The startup builds hyper-personalized portfolios and automates portfolio management. Moreover, they aim to enable financial advisory across businesses so they can focus on developing their clients and growing their businesses.
7. Cape Analytics helps avail accurate insurance quotes
Cape Analytics combines machine learning and geospatial imagery to help identify property attributes that allow insurance companies to provide clients with accurate quotes.
The main aim of the startup is to provide property details to combat any risks associated with climate, insurance, and real estate.
Founded: 2014
Headquarters: Mountain View, California, United States
8. Clinc is revolutionizing conversational AI, one bank at a time.
Clinc develops intelligent personal financial assistants. The platform enables personal and instant answers to any common or complex questions.
Inspired by conversational AI, Clinc focuses on revolutionizing conversational AI at some of the biggest banks in the world. The startup utilizes NLP which understands how people talk, powering exceptional customer experiences that build loyalty and generate ROI.
To learn more about Conversational AI, click here.
9. Sentieo is centralizing financial research tools into a single platform
Sentieo is an AI-powered financial research startup that develops and distributes a range of systems across the financial world.
Sentieo is a financial intelligence platform that aims to centralize multiple financial research tools into a single innovative Ai-powered platform. Sentieo helps analysts save time but also discover alpha-driving insights.
10. CognitiveScale is industrializing scalable Enterprise AI
Pioneering the concept of ‘AI engineering,’ CognitiveScale aims to industrialize scalable Enterprise AI development and deployment.
The startup makes use of its award-winning Cortex AI Platform to empower businesses. The startup helps implement trusted decision intelligence into business processes and applications for better customer experience and operational efficiency.
11. Kyndi is building the world’s first Explainable AI platform
AI company Kyndi is trying to build the world’s first Explainable AI platform for governments and commercial institutions.
The startup hopes to transform business processes by offering auditable AI solutions across various platforms. It is built on the simple policy that higher-performing teams can produce trusted results and better business outcomes.
12. NumerAI is bridging the gap between the stock market and Data Scientists
Another startup transforming the financial sector is NumerAI. The startup aims to transform and regularize financial data into machine learning problems for a global network of Data Scientists.
Given the inefficiency of the stock market concerning developments in machine learning and artificial intelligence, the startup recognized that only a fraction of the world’s Data Scientists have access to its data and create solutions to combat that.
13. Merlon Intelligence is one of the startups that provide financial security through AI
Fintech startup, Merlon Intelligence, helps banks by mitigating potential risks and controlling money laundering across multiple platforms.
The startup makes use of AI to automate adverse media screening. This helps business and financial analysts focus on quicker, more accurate, real-time decisions.
14. Trade Ideas’ virtual research analyst helps with smarter trading
Trade Ideas built a virtual research analyst that can sift through multiple aspects of business and finances, including technical, fundamental, social, and much more. The virtual assistant sifts through thousands of trades every day to help find the highest probability.
The startup makes use of thousands of data centers and makes them play with different trading scenarios every single day.
15. Datrics is democratizing self-service financial analytics using data science
Fintech startup, Datrics helps democratize self-service analytics as well as machine learning solutions by providing an easy-to-use drag-and-drop interface.
Datrics provides a no-code platform that can easily generate analytics and data science. The startup makes use of data-driven decision-making that allows enterprises to make better use of their financial service analytics.
If you would like to learn more about Artificial Intelligence, click here.
Is there any other AI-based fintech startup that you would like us to talk about? Let us know in the comments below. For similar listicles, click here.
Healthcare is a necessity for human life, yet many do not have access to it. Here are 10 startups that are using AI to change healthcare.
Healthcare is a necessity that is inaccessible to many across the world. Despite rapid developments and improvements in medical research, healthcare systems have become increasingly unaffordable.
However, multiple startups and tech companies have been trying their best to integrate AI and machine learning for improvements in this sector.
As the population of the planet increases along with life expectancy due to advancements in agriculture, science, medicine, and more, the demand for functioning healthcare systems also rises.
According to McKinsey & Co., by the year 2050, in Europe and North America, 1 in 4 people will be over the age of 65 Source). Healthcare systems by that time will have to manage numerous patients with complex needs.
Here is a list of a few Artificial Intelligence (AI) startups that are trying their best to revolutionize the healthcare industry as we know it today and help their fellow human beings:
1. Owkin aims to find the right drug for every patient.
Originating in Paris, France, Owkin was launched in 2016 and develops a federated learning AI platform, that helps pharmaceutical companies discover new drugs, enhance the drug development process, and identify the best drug for the ‘right patient.’ Pretty cool, right?
Owkin makes use of different machine learning models to test AI models on distributed data.
The startup also aims to empower researchers across hospitals, educational institutes, and pharmaceutical companies to understand why drug efficacy varies from patient to patient.
2. Overjet is providing accurate data for better patient care and disease management.
Founded by PhDs from the Massachusetts Institute of Technology and dentists from Harvard School of Dental Medicine in 2018, Overjet is changing the playground in dental AI.
Overjet makes use of AI to make use of dentist-level understanding of the subject for the identification of diseases and their progression into software.
Overjet aims to provide effective and accurate data to dentists, dental groups, and insurance companies so that they can provide the best patient care and disease management.
3. From the mid-Atlantic health system to an enterprise-wide AI workforce, Olive AI is improving operational healthcare efficiency.
Founded in 2012, Olive AI is the only known AI as a Service (AIaaS) built for the healthcare sector. The premier AI startup utilizes the power of cloud computing by implementing Amazon Web Services (AWS) and automating systems that accelerate time to care.
With more than 200 enterprise customers such as health systems, insurance companies, and a growing number of healthcare companies. Olive AI assists healthcare workers with time-consuming tasks like prior authorizations and patient verifications.
Want to learn more about AI as a Service? Click here.
4. Insitro provides better medicines for patients with the overlap of biology and machine learning.
The perfect cross between biology and machine learning, Insitro aims to support pharmaceutical research and development, and improve healthcare services. Founded in 2018, Insitro promotes Machine Learning-Based Drug Discovery for which it has raised a substantial amount of funding over the years.
According to a recent Forbes ranking of the top 50 AI businesses, the HealthTech startup is ranked at 35 for having the most promising AI-based medication development process.
5. Caption Health makes early disease detection easier.
Founded in 2013, Caption Health has since been a top provider of medical artificial intelligence. The startup is responsible for the early identification of illnesses.
Caption Health was the first to provide the FDA-approved AI imaging and guiding software for cardiac ultrasonography. The startup has helped remove numerous barriers to treatment and enabled a wide range of people to perform heart scans of diagnostic quality.
6. InformAI is trying to transform the way healthcare is delivered and improve patient outcomes.
Founded in 2017, InformAI expedites medical diagnosis while increasing the productivity of medical professionals.
Focusing on AI and deep learning, as well as business analytics solutions for hospitals and medical companies, InformAI was built for AI-enabled medical image classification, healthcare operations, patient outcome predictors, and much more.
InformAI not only has top-tier medical professionals at its disposal, but also has 10 times more access to proprietary medical datasets, as well as numerous AI toolsets for data augmentation, model optimization, and 3D neural networks.
The startup’s incredible work can be further explored here.
7. Recursion is decoding biology to improve lives across the globe.
A biotechnology startup, Recursion was founded in 2013 and focuses on multiple disciplines, ranging from biology, chemistry, automation, and data science, to even engineering.
Recursion focuses on creating one of the largest and fastest-growing proprietary biological and chemical datasets in the world.
8. Remedy Health provides information and insights for better navigation of the healthcare industry.
As AI advances, so does the technology that powers it. Another marvelous startup known as Remedy Health is allowing people to conduct phone screening interviews with clinically skilled professionals to help identify hidden chronic conditions.
The startup makes use of virtual consultations, allowing low-cost, non-physician employees to proactively screen patients.
Founded in 2013, Sensely is an avatar and chatbot-based platform that aids insurance plan members and patients.
The startup provides virtual assistance solutions to different enterprises including insurance and pharmaceutical companies, as well as hospitals to help them converse better with their members.
Sensely’s business ideology can further be explored here.
10. Oncora Medical provides a one-stop solution for oncologists.
Another digital health company, founded in 2014, Oncora Medical focuses on creating a crossover between data and machine learning for radiation oncology.
The main aim of the startup was to create a centralized platform for better collection and application of real-world data that can in some way help patients.
Other details on Oncora Medical can be found here.
With the international AI in the healthcare market expected to reach over USD 36B by the year 2025, it is only accurate to expect that this market and specific niche will continue to grow even further.
If you would like to learn more about Artificial Intelligence, click here.
Was there any AI-based healthcare startup that we missed? Let us know in the comments below. For similar listicles, click here.
Data science is an interdisciplinary field that encompasses the scientific processes used to build predictive models. In turn, enabling data science to kickstart business decision-making through interpreting, modeling, and deployment.
Data science lifecycle steps
Now what is Data Science?
Data science is a combination of various tools and algorithms which are used to discover hidden patterns within raw data. Data science is different from other techniques in the way that it enables the predictive capabilities of data. A Data Analyst mainly focuses on the visualizations and the history of the data whereas a Data Scientist not only works on the exploratory analysis but also works on extracting useful insights using several kinds of machine learning algorithms.
Why do we need Data Science?
Some time ago, there were only a few sources from which data came. Also, the data then was much smaller in size, hence, we could easily make use of simple tools to identify trends and analyze them. Today, data comes from many sources and has mostly become unstructured so it cannot be so easily analyzed. The data sources can be sensors, social media, sales, marketing, and much more. With this, we need techniques to gain useful insights so companies can make positive impact, take bold steps, and achieve more.
Who is a data scientist?
Data scientists are professionals who use a variety of specialized tools and programs that are specifically designed for data cleaning, analysis and modelling. Amongst the numerous tools, the most widely used is Python, as cited by data scientists themselves.
