Dashboarding has become an increasingly popular tool for sales teams, and good reason. A well-designed dashboard can help sales teams to track key performance indicators (KPIs) in real-time, which can provide valuable insights into sales performance and help teams to make data-driven decisions.
In this blog post, we’ll explore the importance of dashboarding for sales teams, and highlight five KPIs that every sales team should track.
Sales revenue:
This is the most basic KPI for a sales team, and it simply represents the total amount of money generated from sales. Tracking sales revenue can help teams to identify trends in sales performance and can be used to set and track sales goals. It’s also important to track sales revenue by individual product, category, or sales rep to understand the performance of different areas of the business.
Sales quota attainment:
Sales quota attainment measures how well a sales team performs against its goals. It is typically expressed as a percentage and is calculated by dividing the total sales by the sales quota. Tracking this KPI can help sales teams to understand how they are performing against their goals and can identify areas that need improvement.
The lead conversion rate is a measure of how effectively a sales team is converting leads into paying customers. It is calculated by dividing the number of leads that are converted into sales by the total number of leads generated. Tracking this KPI can help sales teams to understand how well their lead generation efforts are working and can identify areas where improvements can be made.
Customer retention rate:
The customer retention rate is a measure of how well a company is retaining its customers over time. It is calculated by dividing the number of customers at the end of a given period by the number of customers at the beginning of that period, multiplied by 100. By tracking customer retention rate over time, sales teams can identify patterns in customer behavior, and use that data to develop strategies for improving retention.
Average order value:
Average order value (AOV) is a measure of the amount of money a customer spends on each purchase. It is calculated by dividing the total revenue by the total number of orders. AOV can be used to identify trends in customer buying behavior and can help sales teams to identify which products or services are most popular among customers.
All these KPIs are important for a sales team as they allow them to measure their performance and how they are doing against the set goals.
Sales revenue is important to understand the total money generated from sales, sales quota attainment gives a measure of how well the team is doing against their set targets, lead conversion rate helps understand the effectiveness of lead generation, the customer retention rate is important to understand the patterns of customer behavior and the average order value helps understand which products are most popular among the customers.
All of these KPIs can provide valuable insights into sales performance and can help sales teams to make data-driven decisions. By tracking these KPIs, sales teams can identify areas that need improvement, and develop strategies for increasing sales, improving lead conversion, and retaining customers.
A dashboard can be a great way to visualize this data, providing an easy-to-use interface for tracking and analyzing KPIs. By integrating these KPIs into a sales dashboard, teams can see a clear picture of performance in real time and make more informed decisions.
Take data-driven decisions today with creative dashboards!
In conclusion, dashboarding is an essential tool for sales teams as it allows them to track key performance indicators and provides a clear picture of their performance in real time. It can help them identify areas of improvement and make data-driven decisions. Sales revenue, sales quota attainment, lead conversion rate, customer retention rate,
Data Science Dojo is offering Airbyte for FREE on Azure Marketplace packaged with a pre-configured web environment enabling you to quickly start the ELT process rather than spending time setting up the environment.
What is an ELT pipeline?
An ELT pipeline is a data pipeline that extracts (E) data from a source, loads (L) the data into a destination, and then transforms (T) data after it has been stored in the destination. The ELT process that is executed by an ELT pipeline is often used by the modern data stack to move data from across the enterprise into analytics systems.
ELT process
In other words, in the ELT approach, the transformation (T) of the data is done at the destination after the data has been loaded. The raw data that contains the data from a source record is stored in the destination as a JSON blob.
Airbyte’s architecture:
Airbyte is conceptually composed of two parts: platform and connectors.
The platform provides all the horizontal services required to configure and run data movement operations, for example, the UI, configuration API, job scheduling, logging, alerting, etc., and is structured as a set of microservices.
Connectors are independent modules that push/pull data to/from sources and destinations. Connectors are built under the Airbyte specification, which describes the interface with which data can be moved between a source and a destination using Airbyte. Connectors are packaged as Docker images, which allows total flexibility over the technologies used to implement them.
Obstacles for data engineers & developers
Collection and maintenance of data from different sources is itself a hectic task for data engineers and developers. Building a custom ELT pipeline for all of the data sources is a nightmare on top that not only consumes a lot of time for the engineers but also costs a lot.
In this scenario, a unified environment to deal with the quick data ingestions from various sources to various destinations would be great to tackle the mentioned challenges.
Methodology of Airbyte
Airbyte leverages DBT (data build tool) to manage and create SQL code that is used for transforming raw data in the destination. This step is sometimes referred to as normalization. An abstracted view of the data processing flow is given in the following figure:
Airbyte methodology
It is worth noting that the above illustration displays a core tenet of ELT philosophy, which is that data should be untouched as it moves through the extracting and loading stages so that the raw data is always available at the destination. Since an unmodified version of the data exists in the destination, it can be re-transformed in the future without the need for a resync of data from source systems.
Major features
Airbyte supports hundreds of data sources and destinations including:
Apache Kafka
Azure Event Hub
Paste Data
Other custom sources
By specifying credentials and adding extensions you can also ingest from and dump to:
Azure Data Lake
Google Cloud Storage
Amazon S3 & Kinesis
Other major features that Airbyte offers:
High extensibility: Use existing connectors to your needs or build a new one with ease.
Customization: Entirely customizable, starting with raw data or from some suggestion of normalized data.
Full-grade scheduler: Automate your replications with the frequency you need.
Real-time monitoring: Logs all the errors in full detail to help you understand better.
Incremental updates: Automated replications are based on incremental updates to reduce your data transfer costs.
Manual full refresh: Re-syncs all your data to start again whenever you want.
Debugging: Debug and Modify pipelines as you see fit, without waiting.
What does Data Science Dojo provide?
Airbyte instance packaged by Data Science Dojo serves as a pre-configured ELT pipeline that makes data integration pipelines a commodity without the burden of installation. It offers efficient data migration and supports a variety of data sources and destinations to ingest and dump data.
Features included in this offer:
Airbyte service that is easily accessible from the web and has a rich user interface.
Easy to operate and user-friendly.
Strong community support due to the open-source platform.
Free to use.
Conclusion
There are a ton of small services that aren’t supported on traditional data pipeline platforms. If you can’t import all your data, you may only have a partial picture of your business. Airbyte solves this problem through custom connectors that you can build for any platform and make them run quickly.
Install the Airbyte offer now from the Azure Marketplace 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 Airbyte for FREE by clicking below:
Google OR-Tools is a software suite for optimization and constraint programming. It includes several optimization algorithms such as linear programming, mixed-integer programming, and constraint programming. These algorithms can be used to solve a wide range of problems, including scheduling problems, such as nurse scheduling.
Machine learning is the way of the future. Discover the importance of data collection, finding the right skill sets, performance evaluation, and security measures to optimize your next machine learning project.
In this blog post, the author introduces the new blog series about the titular three main disciplines or knowledge domains of software development, project management, and data science. Amidst the mercurial evolving global digital economy, how can job-seekers harness the lucrative value of those fields–esp. data science, vis-a-vis improving their employability?
Introduction/Overview:
To help us launch this blog series, I will gladly divulge two embarrassing truths. These are:
Despite my marked love of LinkedIn, and despite my decent / above-average levels of general knowledge, I cannot keep up with the ever-changing statistics or news reports vis-a-vis whether–at any given time, the global economy is favorable to job-seekers, or to employers, or is at equilibrium for all parties–i.e., governments, employers, and workers.
Despite having rightfully earned those fancy three letters after my name, as well as a post-graduate certificate from the U. New Mexico & DS-Dojo, I (used to think I) hate math, or I (used to think I) cannot learn math; not even if my life depended on it!
Background:
Following my undergraduate years of college algebra and basic discrete math–and despite my hatred of mathematics since 2nd grade (chief culprit: multiplication tables!), I had fallen in love (head-over-heels indeed!) with the interdisciplinary field of research methods. And sure, I had lucked out in my Masters (of Arts in Communication Studies) program, as I only had to take the qualitative methods course.
A Venn-diagram depicting the disciplines/knowledge-domains of the new blog series.
But our instructor couldn’t really teach us about interpretive methods, ethnography, and qualitative interviewing etc., without at least “touching” on quantitative interviewing/surveys, quantitative data-analysis–e.g. via word counts, content-analysis, etc.
Fast-forward; year: 2012. Place: Drexel University–in Philadelphia, for my Ph.D. program (in Communication, Culture, and Media). This time, I had to face the dreaded mathematics/statistics monster. And I did, but grudgingly.
Let’s just get this over with, I naively thought; after all, besides passing this pesky required pre-qualifying exam course, who needs stats?!
About software development:
Fast-forward again; year: 2020. Place(s): Union, NJ and Wenzhou, Zhejiang Province; Hays, KS; and Philadelphia all over again. Five years after earning the Ph.D., I had to reckon with an unfair job loss, and chaotic seesaw-moves between China and the USA, and Philadelphia and Kansas, etc.
Thus, one thing led to another, and soon enough, I was practicing algorithms and data-structures, learning about the basic “trouble-trio” of web-development–i.e., HTML, CSS, and JavaScript, etc.!
But like many other folks who try this route, I soon came face-to-face with that oh-so-debilitative monster: self-doubt! No way, I thought. I’m NOT cut out to be a software-engineer! I thus dropped out of the bootcamp I had enrolled in and continued my search for a suitable “plan-B” career.
About project management:
Eventually (around mid/late-2021), I discovered the interdisciplinary field of project management. Simply defined (e.g. by Te Wu, 2020; link), project management is
“A time-limited, purpose-driven, and often unique endeavor to create an outcome, service, product, or deliverable.”
One can also break down the constituent conceptual parts of the field (e.g. as defined by Belinda Goodrich, 2021; link) as:
Project life cycle,
Integration,
Scope,
Schedule,
Cost,
Quality,
Resources,
Communications,
Risk,
Procurement,
Stakeholders, and
Professional responsibility / ethics.
Ah…yes! I had found my sweet spot, indeed. or, so I thought.
Hard truths:
Eventually, I experienced a series of events that can be termed “slow-motion epiphanies” and hard truths. Among many, below are three prime examples.
Hard Truth 1: The quantifiability of life:
For instance, among other “random” models: one can generally presume–with about 95% certainty (ahem!)–that most of the phenomena we experience in life can be categorized under three broad classes:
Phenomena we can easily describe and order, using names (nominal variables);
Phenomena we can easily group or measure in discrete and evenly-spaced amounts (ordinal variables);
And phenomena that we can measure more accurately, and which: i)–is characterized by trait number two above, and ii)–has a true 0 (e.g., Wrench et Al; link).
Hard Truth 2: The probabilistic essence of life:
Regardless of our spiritual beliefs, or whether or not we hate math/science, etc., we can safely presume that the universe we live in is more or less a result of probabilistic processes (e.g., Feynman, 2013).
Hard truth 3: What was that? “Show you the money (!),” you demanded? Sure! But first, show me your quantitative literacy, and critical-thinking skills!
And finally, related to both the above realizations: while it is true indeed that there are no guarantees in life, we can nonetheless safely presume that professionals can improve their marketability by demonstrating their critical-thinking-, as well as quantitative literacy skills.
Bottomline; The value of data science:
Overall, the above three hard truths are prototypical examples of the underlying rationale(s) for this blog series. Each week, DS-Dojo will present our readers with some “food for thought” vis-a-vis how to harness the priceless value of data science and various other software-development and project-management skills / (sub-)topics.
No, dear reader; please do not be fooled by that “OmG, AI is replacing us (!)” fallacy. Regardless of how “awesome” all these new fancy AI tools are, the human touch is indispensable!
In this blog, we will discuss exploratory data analysis, also known as EDA, and why it is important. We will also be sharing code snippets so you can try out different analysis techniques yourself. So, without any further ado let’s dive right in.
What is Exploratory Data Analysis (EDA)?
“The greatest value of a picture is when it forces us to notice what we never expected to see.”John Tukey, American Mathematician
A core skill to possess for someone who aims to pursue data science, data analysis or affiliated fields as a career is exploratory data analysis (EDA). To put it simply, the goal of EDA is to discover underlying patterns, structures, and trends in the datasets and drive meaningful insights from them that would help in driving important business decisions.
The data analysis process enables analysts to gain insights into the data that can inform further analysis, modeling, and hypothesis testing.
EDA is an iterative process of conglomerative activities which include data cleaning, manipulation and visualization. These activities together help in generating hypotheses, identifying potential data cleaning issues, and informing the choice of models or modeling techniques for further analysis. The results of EDA can be used to improve the quality of the data, to gain a deeper understanding of the data, and to make informed decisions about which techniques or models to use for the next steps in the data analysis process.
Often it is assumed that EDA is to be performed only at the start of the data analysis process, however the reality is in contrast to this popular misconception, as stated EDA is an iterative process and can be revisited numerous times throughout the analysis life cycle if need may arise.
In this blog while highlighting the importance and different renowned techniques of EDA we will also show you examples with code so you can try them out yourselves and better comprehend what this interesting skill is all about.
Want to see some exciting visuals that we can create from this dataset? DSD got you covered! Visit the link
Importance of EDA:
One of the key advantages of EDA is that it allows you to develop a deeper understanding of your data before you begin modelling or building more formal, inferential models. This can help you identify
Important variables,
Understand the relationships between variables, and
Identify potential issues with the data, such as missing values, outliers, or other problems that might affect the accuracy of your models.
Another advantage of EDA is that it helps in generating new insights which may incur associated hypotheses, those hypotheses then can be tested and explored to gain a better understanding of the dataset.
Finally, EDA helps you uncover hidden patterns in a dataset that were not comprehensible to the naked eye, these patterns often lead to interesting factors that one couldn’t even think would affect the target variable.
Want to start your EDA journey, well you can always get yourself registered at Data Science Bootcamp.
Common EDA techniques:
The technique you employ for EDA is intertwined with the task at hand, many times you would not require implementing all the techniques, on the other hand there would be times that you’ll need accumulation of the techniques to gain valuable insights. To familiarize you with a few we have listed some of the popular techniques that would help you in EDA.
Visualization:
One of the most popular and effective ways to explore data is through visualization. Some popular types of visualizations include histograms, pie charts, scatter plots, box plots and much more. These can help you understand the distribution of your data, identify patterns, and detect outliers.
Below are a few examples on how you can use visualization aspect of EDA to your advantage:
Histogram:
The histogram is a kind of visualization that shows the frequencies of each category in a dataset.
Histogram
The above graph shows us the number of responses belonging to different age groups and they have been partitioned based on how many came to the appointment and how many did not show up.
Pie Chart:
A pie chart is a circular image, it is usually used for a single feature to indicate how the data of that feature are distributed, commonly represented in percentages.
Pie Chart
The pie chart shows the distribution that 20.2% of the total data comprises of individuals who did not show up for the appointment while 79.8% of individuals did show up.
Box Plot:
Box plot is also an important kind of visualization that is used to check how the data is distributed, it shows the five number summary of the dataset, which is quite useful in many aspects such as checking if the data is skewed, or detecting the outliers etc.
Box Plot
The box plot shows the distribution of the Age column, segregated on the basis of individuals who showed and did not show up for the appointments.
Descriptive statistics:
Descriptive statistics are a set of tools for summarizing data in a way that is easy to understand. Some common descriptive statistics include mean, median, mode, standard deviation, and quartiles. These can provide a quick overview of the data and can help identify the central tendency and spread of the data.
Descriptive statistics
Grouping and aggregating:
One way to explore a dataset is by grouping the data by one or more variables, and then aggregating the data by calculating summary statistics. This can be useful for identifying patterns and trends in the data.
Grouping and Aggregation of Data
Data cleaning:
Exploratory data analysis also includes cleaning data, it may be necessary to handle missing values, outliers, or other data issues before proceeding with further analysis.
As you can see, fortunately this dataset did not have any missing value.
Correlation analysis:
Correlation analysis is a technique for understanding the relationship between two or more variables. You can use correlation analysis to determine the degree of association between variables, and whether the relationship is positive or negative.
Correlation Analysis
The heatmap indicates to what extent different features are correlated to each other, with 1 being highly correlated and 0 being no correlation at all.
Types of EDA:
There are a few different types of exploratory data analysis (EDA) that are commonly used, depending on the nature of the data and the goals of the analysis. Here are a few examples:
Univariate EDA:
Univariate EDA, short for univariate exploratory data analysis, examines the properties of a single variable by techniques such as histograms, statistics of central tendency and dispersion, and outliers detection. This approach helps understand the basic features of the variable and uncover patterns or trends in the data.
Alcoholism – Pie Chart
The pie chart indicates what percentage of individuals from the total data are identified as alcoholic.
Alcoholism data
Bivariate EDA:
This type of EDA is used to analyse the relationship between two variables. It includes techniques such as creating scatter plots and calculating correlation coefficients and can help you understand how two variables are related to each other.
Bivariate data chart
The bar chart shows what percentage of individuals are alcoholic or not and whether they showed up for the appointment or not.
Multivariate EDA:
This type of EDA is used to analyze the relationships between three or more variables. It can include techniques such as creating multivariate plots, running factor analysis, or using dimensionality reduction techniques such as PCA to identify patterns and structure in the data.
Multivariate data chart
The above visualization is distplot of kind, bar, it shows what percentage of individuals belong to one of the possible four combinations diabetes and hypertension, moreover they are segregated on the basis of gender and whether they showed up for appointment or not.
Time-series EDA:
This type of EDA is used to understand patterns and trends in data that are collected over time, such as stock prices or weather patterns. It may include techniques such as line plots, decomposition, and forecasting.
Time Series Data Chart
This kind of chart helps us gain insight of the time when most appointments were scheduled to happen, as you can see around 80k appointments were made for the month of May.
Spatial EDA:
This type of EDA deals with data that have a geographic component, such as data from GPS or satellite imagery. It can include techniques such as creating choropleth maps, density maps, and heat maps to visualize patterns and relationships in the data.
Spatial data chart
In the above map, the size of the bubble indicates the number of appointments booked in a particular neighborhood while the hue indicates the percentage of individuals who did not show up for the appointment.
Popular libraries for EDA:
Following is a list of popular libraries that python has to offer which you can use for Exploratory Data Analysis.
Pandas: This library offers efficient, adaptable, and clear data structures meant to simplify handling “relational” or “labelled” data. It is a useful tool for manipulating and organizing data.
NumPy: This library provides functionality for handling large, multi-dimensional arrays and matrices of numerical data. It also offers a comprehensive set of high-level mathematical operations that can be applied to these arrays. It is a dependency for various other libraries, including Pandas, and is considered a foundational package for scientific computing using Python.
Matplotlib: Matplotlib is a Python library used for creating plots and visualizations, utilizing NumPy. It offers an object-oriented interface for integrating plots into applications using various GUI toolkits such as Tkinter, wxPython, Qt, and GTK. It has a diverse range of options for creating static, animated, and interactive plots.
Seaborn: This library is built on top of Matplotlib and provides a high-level interface for drawing statistical graphics. It’s designed to make it easy to create beautiful and informative visualizations, with a focus on making it easy to understand complex datasets.
Plotly: This library is a data visualization tool that creates interactive, web-based plots. It works well with the pandas library and it’s easy to create interactive plots with zoom, hover, and other features.
Altair: is a declarative statistical visualization library for Python. It allows you to quickly and easily create statistical graphics in a simple, human-readable format.
Conclusion:
In conclusion, Exploratory Data Analysis (EDA) is a crucial skill for data scientists and analysts, which includes data cleaning, manipulation, and visualization to discover underlying patterns and trends in the data. It helps in generating new insights, identifying potential issues and informing the choice of models or techniques for further analysis.
It is an iterative process that can be revisited throughout the data analysis life cycle. Overall, EDA is an important skill that can inform important business decisions and generate valuable insights from data.
Bellevue, Washington (January 11, 2023) – The following statement was released today by Data Science Dojo, through its Marketing Manager Nathan Piccini, in response to questions about future in-person bootcamps:
In this blog, we will explore some of the difficulties you may face while animating data science and machine learning videos in Adobe After Effects and how to overcome them.
Despite major layoffs in 2022, there are many optimistic fintech trends to look out for in 2023. Every crisis bespells new opportunities. In this blog, let’s see what the future holds for fintech trends in 2023.(more…)
An overview of data analysis, the data analysis process, its various methods, and implications for modern corporations.
Studies show that 73% of corporate executives believe that companies failing to use data analysis on big data lack long-term sustainability. While data analysis can guide enterprises to make smart decisions, it can also be useful for individual decision-making.
Let’s consider an example of using data analysis at an intuitive individual level. As consumers, we are always choosing between products offered by multiple companies. These decisions, in turn, are guided by individual past experiences. Every individual analysis the data obtained via their experience to generate a final decision.
Put more concretely, data analysis involves sifting through data, modeling it, and transforming it to yield information that guides strategic decision-making. For businesses, data analytics can provide highly impactful decisions with long-term yield.
Data analysis methods and data analysis processes – Data Science Dojo
So, let’s dive deep and look at how data analytics tools can help businesses make smarter decisions.
The data analysis process
The process includes five key steps:
1. Identify the need
Companies use data analytics for strategic decision-making regarding a specific issue. The first step, therefore, is to identify the particular problem. For example, a company decides it wants to reduce its production costs while maintaining product quality. To do so effectively, the company would need to identify step(s) of the workflow pipeline it should implement cost cuts.
Similarly, the company might also have a hypothetical solution to its question. Data analytics can be used to judge the falsifiability of the hypothesis, allowing the decision-maker to reach the optimized solution.
A specific question or hypothesis determines the subsequent steps of the process. Hence, this must be as clear and specific as possible.
2. Collect the data
Once the data analysis need is identified, the subsequent kind of data is also determined. Data collection can involve data entered in different types and formats. One broad classification is based on structure and includes structured and unstructured data.
Structured data, for example, is the data a company obtains from its users via internal data acquisition methods such as marketing automation tools. More importantly, it follows the usual row-column database and is suited to the company’s exact needs.
Unstructured data, on the other hand, need not follow any such formatting. It is obtained via third parties such as Google trends, census bureaus, world health bureaus, and so on. Structured data is easier to work with as it’s already tailored to the company’s needs. However, unstructured data can provide a significantly larger data volume.
There are many other data types to consider as well. For example, meta data, big data, real-time data, and machine data.
3. Clean the data
The third step, data cleaning, ensures that error-free data is used for the data analysis. This step includes procedures such as formatting data correctly and consistently, removing any duplicate or anomalous entries, dealing with missing data, fixing cross-set data errors.
Performing these tasks manually is tedious and hence, various tools exist to smoothen the data cleaning process. These include open-source data tools such as OpenRefine, desktop applications like Trifacta Wrangler, cloud-based software as a service (SaaS) like TIBCO Clarity, and other data management tools such as IBM Infosphere quality stage especially used for big data.
4. Perform data analysis
Data analysis includes several methods as described earlier. The method to be implemented depends closely on the research question to be investigated. Data analysis methods are discussed in detail later in this blog.
5. Present the results
Presentation of results defines how well the results are to be communicated. Visualization tools such as charts, images, and graphs effectively convey findings, establishing visual connections in the viewer’s mind. These tools emphasize patterns discovered in existing data and shed light on predicted patterns, assisting the results’ interpretation.
Listen to the Data Analysis challenges in cybersecurity
Methods for data analysis
Data analysts use a variety of approaches, methods, and tools to deal with data. Let’s sift through these methods from an approach-based perspective:
1. Descriptive analysis
Descriptive analysis involves categorizing and presenting broader datasets in a way that allows emergent patterns to be observed from them to see if there are any obvious patterns. Data aggregation techniques are one way of performing descriptive analysis. This involves first collecting the data and then sorting it to ease manageability.
This can also involve performing statistical analysis on the data to determine, say, the measures of frequency, dispersion, and central tendencies that provide a mathematical description for the data.
2. Exploratory analysis
Exploratory analysis involves consulting various data sets to see how certain variables may be related, or how certain patterns may be driving others. This analytic approach is crucial in framing potential hypotheses and research questions that can be investigated using data analytic techniques.
Data mining, for example, requires data analysts to use exploratory analysis to sift through big data and generate hypotheses to be tested out.
3. Diagnostic analysis
Diagnostic analysis is used to answer why a particular pattern exists in the first place. For example, this kind of analysis can assist a company in understanding why its product is performing in a certain way in the market.
Diagnostic analytics includes methods such as hypothesis testing, determining a correlations v/s causation, and diagnostic regression analysis.
4. Predictive analysis
Predictive analysis answers the question of what will happen. This type of analysis is key for companies in deciding new features or updates on existing products, and in determining what products will perform well in the market.
For predictive analysis, data analysts use existing results from the earlier described analyses while also using results from machine learning and artificial intelligence to determine precise predictions for future performance.
5. Prescriptive analysis
Prescriptive analysis involves determining the most effective strategy for implementing the decision arrived at. For example, an organization can use prescriptive analysis to sift through the best way to unroll a new feature. This component of data analytics actively deals with the consumer end, requiring one to work with marketing, human resources, and so on.
Prescriptive analysis makes use of machine learning algorithms to analyze large amounts of big data for business intelligence. These algorithms are able to asses large amounts of data by working through them via “if” and “else” statements and making recommendations accordingly.
6. Quantitative and qualitative analysis
Quantitative analysis computationally implements algorithms testing out a mathematical fit to describe correlation or causation observed within datasets. This includes regression analysis, null analysis, hypothesis analysis, etc.
Qualitative analysis, on the other hand, involves non-numerical data such as interviews and pertains to answering broader social questions. It involves working closely with textual data to derive explanations.
7. Statistical analysis
Statistical techniques provide answers to essential decision challenges. For example, they can accurately quantify risk probabilities, predict product performance, establish relationships between variables, and so on. These techniques are used by both qualitative and quantitative analysis methods. Some of the invaluable statistical techniques for data analysts include linear regression, classification, resampling methods, subset selection.
Statistical analysis, more importantly, lies at the heart of data analysis, providing the essential mathematical framework via which analysis is conducted.
Data-driven businesses
Data-driven businesses use the data analysis methods described above. As a result, they offer many advantages and are particularly suited to modern needs. Their credibility relies on them being evidence-based and using precise mathematical models to determine decisions. Some of these advantages include stronger customer needs, precise identification of business needs, devising effective strategy decisions, and performing well in a competitive market. Data-driven businesses are the way forward.
In this blog, we will share the list of leading data science conferences across the world to be held in 2023. This will help you to learn and grow your career in data science, AI, and machine learning.
Top data science conferences 2023 in different regions of the world
1. Future of Data & AI | Online conference (FREE)
The Future of Data and AI conference hosted by Data Science Dojo is an upcoming event aimed at exploring the advancements and innovations in the field of artificial intelligence and data. The conference is scheduled on March 1st-2nd, 2023and it is expected to bring together experts from the industry, academia, and government to share their insights and perspectives on the future direction of AI and data technologies.
Attendees can expect to learn about the latest trends and advancements in AI and data, such as machine learning, deep learning, big data, and cloud computing. They will also have the opportunity to hear from leading experts in the field and engage in discussions and debates on the ethical, social, and economic implications of these technologies.
In addition to the keynote speeches and panel discussions, the conference will also feature hands-on workshops and tutorials, where attendees can learn and apply new skills and techniques related to AI and data. The conference is an excellent opportunity for professionals, researchers, students, and anyone interested in the future of AI and data to network, exchange ideas, and build relationships with others in the field.
2. AAAI Conference on Artificial Intelligence – Washington DC, United States
The AAAI Conference on Artificial Intelligence (AAAI) is a leading conference in the field of artificial intelligence research. It is held annually in Washington, DC and attracts researchers, practitioners, and students from around the world to present and discuss their latest work.
The conference features a wide range of topics within AI, including machine learning, natural language processing, computer vision, and robotics, as well as interdisciplinary areas such as AI and law, AI and education, and AI and the arts. It also includes tutorials, workshops, and invited talks by leading experts in the field. The conference is organized by the Association for the Advancement of Artificial Intelligence (AAAI), which is a non-profit organization dedicated to advancing AI research and education.
3. Women in Data Science (WiDS) – California, United States
Women in Data Science (WiDS) is an annual conference held at Stanford University, California, United States and other locations worldwide. The conference is focused on the representation, education, and achievements of women in the field of data science. WiDS is designed to inspire and educate data scientists worldwide, regardless of gender, and support women in the field.
The conference is a one-day technical conference that provides an opportunity to hear about the latest data science related research, and applications in various industries, as well as to network with other professionals in the field.
The conference features keynote speakers, panel discussions, and technical presentations from prominent women in the field of data science. WiDS aims to promote gender diversity in the tech industry, and to support the career development of women in data science.
4. Gartner Data and Analytics Summit – Florida, United States
The Gartner Data and Analytics Summit is an annual conference that is held in Florida, United States. The conference is organized by Gartner, a leading research and advisory company, and is focused on the latest trends, strategies, and technologies in data and analytics.
The conference brings together business leaders, data analysts, and technology professionals to discuss the latest trends and innovations in data and analytics, and how they can be applied to drive business success.
The conference features keynote presentations, panel discussions, and breakout sessions on topics such as big data, data governance, data visualization, artificial intelligence, and machine learning. Attendees also have the opportunity to meet with leading vendors and solutions providers in the data and analytics space, and network with peers in the industry.
The Gartner Data and Analytics Summit is considered as a leading event for professionals in the data and analytics field.
5. ODSC East – Boston, United States
ODSC East is a conference on open-source data science and machine learning held annually in Boston, United States. The conference features keynote speeches, tutorials, and training sessions by leading experts in the field, as well as networking opportunities for attendees.
The conference covers a wide range of topics in data science, including machine learning, deep learning, big data, data visualization, and more. It is designed for data scientists, developers, researchers, and practitioners looking to stay up-to-date on the latest advancements in the field and learn new skills.
6. AI and Big Data Expo North America – California, United States
AI and Big Data Expo North America is a technology event that focuses on artificial intelligence (AI) and big data. The conference takes place annually in Santa Clara, California, United States. The event is for enterprise technology professionals seeking to explore the latest innovations, implementations, and strategies in AI and big data.
The event features keynote speeches, panel discussions, and networking opportunities for attendees to connect with leading experts and industry professionals. The conference covers a wide range of topics, including machine learning, deep learning, big data, data visualization, and more.
7. The Data Science Conference – Chicago, United States
The Data Science Conference is an annual data science conference held in Chicago, United States. The conference focuses on providing a space for analytics professionals to network and learn from one another without being prospected by vendors, sponsors, or recruiters.
The conference is by professionals for professionals and the material presented is substantial and relevant to the data science practitioner. It is the only sponsor-free, vendor-free, and recruiter-free data science conference℠. The conference covers a wide range of topics in data science, including artificial intelligence, machine learning, predictive modeling, data mining, data analytics and more.
8. Machine Learning Week – Las Vegas, United States
Machine Learning Week is a large conference that focuses on the commercial deployment of machine learning. It is set to take place in Las Vegas, United States, with the venue being the Red Rock Casino Resort Spa. The conference will have seven tracks of sessions, with six co-located conferences that attendees can register to attend: PAW Business, PAW Financial, PAW Healthcare, PAW Industry 4.0, PAW Climate and Deep Learning World.
9. International Conference on Mass Data Analysis of Images and Signals – New York, United States
The International Conference on Mass Data Analysis of Images and Signals (MDA) is a yearly conference that focuses on various applications of Artificial Intelligence and Pattern Recognition in fields such as Medicine, Biotechnology, Food Industries and Dietetics, Biometry, Agriculture, Drug Discovery, and System Biology.
The conference is not limited to these specific topics and welcomes research from other related fields as well. The conference has been held on a yearly basis
10. International Conference on Data Mining (ICDM) – New York, United States
The International Conference on Data Mining (ICDM) is an annual conference held in New York, United States that focuses on the latest research and developments in the field of data mining. The conference brings together researchers and practitioners from academia, industry, and government to present and discuss their latest research findings, ideas, and applications in data mining. The conference covers a wide range of topics, including machine learning, data mining, big data, data visualization, and more.
11. International Conference on Machine Learning and Data Mining (MLDM) – New York, United States
International Conference on Machine Learning and Data Mining (MLDM) is an annual conference held in New York, United States. The conference focuses on the latest research and developments in the field of machine learning and data mining. The conference brings together researchers and practitioners from academia, industry, and government to present and discuss their latest research findings, ideas, and applications in machine learning and data mining.
The conference covers a wide range of topics, including machine learning, data mining, big data, data visualization, and more. The conference is considered a premier forum for researchers and practitioners to share their latest research, ideas and development in machine learning and data mining and related areas.
12. AI in Healthcare Summit – Boston, United States
AI in Healthcare Summit is an annual event that takes place in Boston, United States. The summit focuses on showcasing the opportunities of advancing methods in AI and machine learning (ML) and their impact across healthcare and medicine.
The event features a global line-up of experts who will present the latest ML tools and techniques that are set to revolutionize healthcare applications, medicine and diagnostics. Attendees will have the opportunity to discover the AI methods and tools that are set to revolutionize healthcare, medicine and diagnostics, as well as industry applications and key insights.
13. Big Data and Analytics Summit – Ontario, Canada
The Big Data and Analytics Summit is an annual conference held in Ontario, Canada. The conference focuses on connecting analytics leaders to the latest innovations in big data and analytics as the world adapts to new business realities after the global pandemic. Businesses need to innovate in products, sales, marketing and operations and big data is now more critical than ever to make this happen and help organizations thrive in the future. The conference features leading industry experts who will discuss the latest trends exploding across the big data landscape, including security, architecture and transformation, cloud migration, governance, storage, AI and ML and so much more.
14. Deep Learning Summit – Montreal, Canada
The Deep Learning Summit is an annual conference held in Montreal, Canada. The conference focuses on providing attendees access to multiple stages to optimize cross-industry learnings and collaboration.
Attendees can solve shared problems with like-minded attendees during round table discussions, Q&A sessions with speakers or schedule 1:1 meeting. The conference also provides an opportunity for attendees to connect with other attendees during and after the summit and build new collaborations through interactive networking sessions.
15. Enterprise AI Summit – Montreal, Canada
The Enterprise AI Summit is an annual conference that takes place in Montreal, Canada. The conference is organized by RE-WORK LTD, and it is scheduled for November 1-2, 2023. The conference will feature the Deep Learning Summit and Enterprise AI Summit as part of the Montreal AI Summit.
The conference is an opportunity for attendees to learn about the latest advancements in AI and Machine Learning and how it can be applied in the enterprise. The conference is a 2-day event that features leading industry experts who will share their insights and experiences on AI and ML in the enterprise
16. Extraction and Knowledge Management Conference (EGC) – Lyon, France
The Extraction and Knowledge Management Conference (EGC) is an annual event that brings together researchers and practitioners from various disciplines related to data science and knowledge management. The conference will be held on the Berges du Rhône campus of the Université Lumière Lyon 2, from January 16 to 20, 2023. The conference provides a forum for researchers, students, and professionals to present their research results and exchange ideas and discuss future challenges in knowledge extraction and management.
17. Women in AI and Data Reception – London, United Kingdom
The Women in AI and Data Reception is an event organized by RE•WORK in London, United Kingdom that takes place on January 24th, 2023. The conference aims to bring together leading female experts in the field of artificial intelligence and machine learning to discuss the impact of this rapidly advancing technology on various sectors such as finance, retail, manufacturing, transport, healthcare and security. Attendees will have the opportunity to hear from these experts, establish new connections and network with peers
18. Chief Data and Analytics Officers (CDAO) – London, United Kingdom
The Chief Data and Analytics Officers (CDAO) conference is an annual event organized by Corinium Global Intelligence, which brings together senior leaders from the data and analytics space. The conference is focused on the acceleration of the adoption of data, analytics and AI in order to generate decision advantages across various industries.
The conference will take place on September 13-14, 2023, in Washington D.C. and will include sessions on latest trends, strategies, and best practices for data and analytics, as well as networking opportunities for attendees.
19. International Conference on Pattern Recognition Applications and Methods (ICPRAM) – Lisbon, Portugal
The International Conference on Pattern Recognition Applications and Methods (ICPRAM) is a major point of contact between researchers, engineers and practitioners on the areas of Pattern Recognition and Machine Learning. It will be held in Lisbon, Portugal and submissions for abstracts and doctoral consortium papers are due on January 2, 2023.
Registration to ICPRAM also allows free access to the ICAART conference as a non-speaker. It is a annual event where researchers can exchange ideas and discuss future challenges in pattern recognition and machine learning
20. AI in Finance Summit – London, United Kingdom
The AI in Finance Summit, taking place in London, United Kingdom, is an event that brings together leaders in the financial industry to discuss the latest advancements and innovations in artificial intelligence and its applications in finance. Attendees will have the opportunity to hear from experts in the field, network with peers, and learn about the latest trends and technologies in AI and finance. The summit will cover topics such as investment, risk management, fraud detection, and more
21. The Martech Summit – Hong Kong
The Martech Summit is an event that brings together the best minds in marketing technology from a range of industries through a number of diverse formats and engaging events. The conference aims to bring together people in senior leadership roles, such as C-suites, Heads, and Directors, to learn and network with industry experts.
The MarTech Summit series includes various formats such as The MarTech Summit, The Virtual MarTech Summit, Virtual MarTech Spotlight, and The MarTech Roundtable.
22. AI and Big Data Expo Europe – Amsterdam, Netherlands
The AI and Big Data Expo Europe is an event that takes place in Amsterdam, Netherlands. The event is scheduled to take place on September 26-27, 2023, at the RAI, Amsterdam. It is organized by Encore Media.
The event will explore the latest innovations within AI and Big Data in 2023 and beyond and covers the impact AI and Big Data technologies have on many industries including manufacturing, transport, supply chain, government, legal and more. The conference will also showcase next generation technologies and strategies from the world of Artificial Intelligence.
23. International Symposium on Artificial Intelligence and Robotics (ISAIR) – Beijing, China
The International Symposium on Artificial Intelligence and Robotics (ISAIR) is a platform for young researchers to share up-to-date scientific achievements in the field of Artificial Intelligence and Robotics. The conference is organized by the International Society for Artificial Intelligence and Robotics (ISAIR), IEEE Big Data TC, and SPIE. It aims to provide a comprehensive conference focused on the latest research in Artificial Intelligence, Robotics and Automation in Space.
24. The Martech Summit – Jakarta, Indonesia
The Martech Summit – Jakarta, Indonesia is a conference organized by BEETC Ltd that brings together the best minds in marketing technology from a range of industries through a number of diverse formats and engaging events. The conference aims to provide a platform for attendees to learn about the latest trends and innovations in marketing technology, with an agenda that includes panel discussions, keynote presentations, fireside chats, and more.
25. Web Search and Data Mining (WSDM) – Singapore
The 16th ACM International WSDM Conference will be held in Singapore on February 27 to March 3, 2023. The conference is a highly selective event that includes invited talks and refereed full papers. The conference focuses on publishing original and high-quality papers related to search and data mining on the Web. The conference is organized by the WSDM conference series and is a platform for researchers to share their latest scientific achievements in this field.
26. Machine Learning Developers Summit – Bangalore, India
The Machine Learning Developers Summit (MLDS) is a 2-day conference that focuses on machine learning innovation. Attendees will have direct access to top innovators from leading tech companies who will share their knowledge on the software architecture of ML systems, how to produce and deploy the latest ML frameworks, and solutions for business use cases. The conference is an opportunity for attendees to learn how machine learning can add potential to their business and gain best practices from cutting-edge presentations
CISO Malaysia 2023 is a conference designed for Chief Information Security Officers (CISOs), Chief Security Officers (CSOs), Directors, Heads, Managers of Cyber and Information Security, and cybersecurity practitioners from across sectors in Malaysia. The conference will be held on February 14, 2023, in Kuala Lumpur, Malaysia. It aims to provide a platform for attendees to get inspired, make new contacts and learn how to uplift their organization’s security program to meet the requirements set by the government and citizens.
Which data science conferences would you like to participate in?
In conclusion, data science and AI conferences are an invaluable opportunity to stay up to date with the latest developments in the field, network with industry leaders and experts, and gain valuable insights and knowledge. These are some of the top conferences in the field and offer a wide range of topics and perspectives. Whether you are a researcher, practitioner, or student, these conferences are a valuable opportunity to further your understanding of data science and AI and advance your career.
Additionally, there are many other conferences out there that might be specific to a certain industry or region, it’s important to research and find the one that fits your interest and needs. Attending these conferences is a great way to stay ahead of the curve and make meaningful connections within the data science and AI community.
In this blog, we will discuss the latest 6 projects that can escalate your data science career and boost your data science portfolio in a competitive era.
Data science myths are one of the main obstacles preventing newcomers from joining the field. In this blog, we bust some of the biggest myths shrouding the field.
The US Bureau of Labor Statistics predicts that data science jobs will grow up to 36% by 2031. There’s a clear market need for the field and its popularity only increases by the day. Despite the overwhelming interest data science has generated, there are many myths preventing new entry into the field.
Top 7 data science myths
Data science myths, at their heart, follow misconceptions about the field at large. So, let’s dive into unveiling these myths.
1. All data roles are identical
It’s a common data science myth that all data roles are the same. So, let’s distinguish between some common data roles – data engineer, data scientist, and data analyst. A data engineer focuses on implementing infrastructure for data acquisition and data transformation to ensure data availability to other roles.
A data analyst, however, uses data to report any observed trends and patterns to report. Using both the data and the analysis provided by a data engineer and a data analyst, a data scientist works on predictive modeling, distinguishing signals from noise, and deciphering causation from correlation.
Finally, these are not the only data roles. Other specialized roles such as data architects and business analysts also exist in the field. Hence, a variety of roles exist under the umbrella of data science, catering to a variety of individual skill sets and market needs.
2. Graduate studies are essential
Another myth preventing entry into the data science field is that you need a master’s or Ph.D. degree. This is also completely untrue.
In busting the last myth, we saw how data science is a diverse field welcoming various backgrounds and skill sets. As such, a Ph.D. or master’s degree is only valuable for specific data science roles. For instance, higher education is useful in pursuing research in data science.
However, if you’re interested in working on real-life complex data problems using data analytics methods such as deep learning, only knowledge of those methods is necessary. And so, rather than a master’s or Ph.D. degree, acquiring specific valuable skills can come in handier in kickstarting your data science career.
3. Data scientists will be replaced by artificial intelligence
As artificial intelligence advances, a common misconception arises that AI will replace all human intelligent labor. This misconception has also found its way into data science forming one of the most popular myths that AI will replace data scientists.
This is far from the truth because. Today’s AI systems, even the most advanced ones, require human guidance to work. Moreover, the results produced by them are only useful when analyzed and interpreted in the context of real-world phenomena, which requires human input.
So, even as data science methods head towards automation, it’s data scientists who shape the research questions, devise the analytic procedures to be followed, and lastly, interpret the results.
Being a data scientist does not translate into being an expert programmer! Programming tasks are only one component of the data science field, and these too, vary from one data science subfield to another.
For example, a business analyst would require a strong understanding of business, and familiarity with visualization tools, while minimal coding knowledge would suffice. At the same time, a machine learning engineer would require extensive knowledge of Python.
In conclusion, the extent of programming knowledge depends on where you want to work across the broad spectrum of the data science field.
5. Learning a tool is enough to become a data scientist
Knowing a particular programming language, or a data visualization tool is not all you need to become a data scientist. While familiarity with tools and programming languages certainly helps, this is not the foundation of what makes a data scientist.
So, what makes a good data science profile? That, really, is a combination of various skills, both technical and non-technical. On the technical end, there are mathematical concepts, algorithms, data structures, etc. While on the non-technical end there are business skills and understanding of various stakeholders in a particular situation.
To conclude, a tool can be an excellent way to implement data science skills. However, it isn’t what will teach you the foundations or the problem-solving aspect of data science.
6. Data scientists only work on predictive modeling
Another myth! Very few people would know that data scientists spend nearly 80% of their time on data cleaning and transforming before working on data modeling. In fact, bad data is the major cause of productivity levels not being up to par in data science companies. This requires significant focus on producing good quality data in the first place.
This is especially true when data scientists work on problems involving big data. These problems involve multiple steps of which data cleaning and transformations are key. Similarly, data from multiple sources and raw data can contain junk that needs to be carefully removed so that the model runs smoothly.
So, unless we find a quick-fix solution to data cleaning and transformation, it’s a total myth that data scientists only work on predictive modeling.
7. Transitioning to data science is impossible
Data science is a diverse and versatile field welcoming a multitude of background skill sets. While technical knowledge of algorithms, probability, calculus, and machine learning can be great, non-technical knowledge such as business skills or social sciences can also be useful for a data science career.
At its heart, data science involves complex problem solving involving multiple stakeholders. For a data-driven company, a data scientist from a purely technical background could be valuable but so could one from a business background who can better interpret results or shape research questions.
And so, it’s a total myth that transitioning to data science from another field is impossible.
It is no surprise that the demand for a skilled data analyst grows across the globe. In this blog, we will explore eight key competencies that aspiring data analysts should focus on developing.
Data analysis is a crucial skill in today’s data-driven business world. Companies rely on data analysts to help them make informed decisions, improve their operations, and stay competitive. And so, all healthy businesses actively seek skilled data analysts.
Technical skills and non-technical skills for data analyst
Becoming a skilled data analyst does not just mean that you acquire important technical skills. Rather, certain soft skills such as creative storytelling or effective communication can mean a more all-rounded profile. Additionally, these non-technical skills can be key in shaping how you make use of your data analytics skills.
Technical skills to practice as a data analyst:
Technical skills are an important aspect of being a data analyst. Data analysts are responsible for collecting, cleaning, and analyzing large sets of data, so a strong foundation in technical skills is necessary for them to be able to do their job effectively.
Some of the key technical skills that are important for a data analyst include:
1. Probability and statistics:
A solid foundation in probability and statistics ensures your ability to identify patterns in data, prevent any biases and logical errors in the analysis, and lastly, provide accurate results. All these abilities are critical to becoming a skilled data analyst.
Consider, for example, how various kinds of probabilistic distributions are used in machine learning. Other than a strong understanding of these distributions, you will need to be able to apply statistical techniques, such as hypothesis testing and regression analysis, to understand and interpret data.
2. Programming:
As a data analyst, you will need to know how to code in at least one programming language, such as Python, R, or SQL. These languages are the essential tools via which you will be able to clean and manipulate data, implement algorithms and build models.
Moreover, statistical programing languages like Python and R allow advanced analysis that interfaces like Excel cannot provide. Additionally, both Python and R are open source.
3. Data visualization:
A crucial part of a data analyst’s job is effective communication both within and outside the data analytics community. This requires the ability to create clear and compelling data visualizations. You will need to know how to use tools like Tableau, Power BI, and D3.js to create interactive charts, graphs, and maps that help others understand your data.
The progression of the Datasaurus Dozen dataset through all of the target shapes – Source
4. Database management:
Managing and working with large and complex datasets means having a solid understanding of database management. This includes everything from methods of collecting, arranging, and storing data in a secure and efficient way. Moreover, you will also need to know how to design and maintain databases, as well as how to query and manipulate data within them.
Certain companies may have roles particularly suited to this task such as data architects. However, most will require data analysts to perform these duties as data analysts responsible for collecting, organizing, and analyzing data to help inform business decisions.
Organizations use different data management systems. Hence, it helps to gain a general understanding of database operations so that you can later specialize them to a particular management system.
Non-technical skills to adopt as a data analyst:
Data analysts work with various members of the community ranging from business leaders to social scientists. This implies effective communication of ideas to a non-technical audience in a way that drives informed, data-driven decisions. This makes certain soft skills like communication essential.
Similarly, there are other non-technical skills that you may have acquired outside a formal data analytics education. These skills such as problem-solving and time management are transferable skills that are particularly suited to the everyday work life of a data analyst.
1. Communication:
As a data analyst, you will need to be able to communicate your findings to a wide range of stakeholders. This includes being able to explain technical concepts concisely and presenting data in a visually compelling way.
Writing skills can help you communicate your results to wider members of population via blogs and opinion pieces. Moreover, speaking and presentation skills are also invaluable in this regard.
Problem-solving is a skill that individuals pick from working in different fields ranging from research to mathematics, and much more. This, too, is a transferable skill and not unique to formal data analytics training. This also involves a dash of creativity and thinking of problems outside the box to come up with unique solutions.
Data analysis often involves solving complex problems, so you should be a skilled problem-solver who can think critically and creatively.
3. Attention to detail:
Working with data requires attention to detail and an elevated level of accuracy. You should be able to identify patterns and anomalies in data and be meticulous in your work.
4. Time management:
Data analysis projects can be time-consuming, so you should be able to manage your time effectively and prioritize tasks to meet deadlines. Time management can also be implemented by tracking your daily work using time management tools.
Final word
Overall, being a data analyst requires a combination of technical and non-technical skills. By mastering these skills, you can become an invaluable member of any team and make a real impact with your data analysis.
