data science

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. 

 

Note: the dataset used for this purpose can be found at: https://www.kaggle.com/datasets/raniahelmy/no-show-investigate-dataset  

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. 

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. 

 

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.  

 

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.

 

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. 

 

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. 

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. 

 

The pie chart indicates what percentage of individuals from the total data are identified as alcoholic. 

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.

 

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.

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. 

 

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.

 

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.   

1. 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. 
2. 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. 
3. 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. 
4. 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. 
5. 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. 
6. 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 data science bootcamp: 

“They’re back.” 

-DSD- 

Nothing can compare to Michael Jordan’s announcement in 1995 that he was returning to the NBA, but for Data Science Dojo (DSD), this comes close.  

In 2020, we had to move our in-person Data Science Bootcamp curriculum to an online format. Doing this allowed us to continue teaching and helping working professionals grow their skill sets and careers. We will continue to provide all our courses in part-time, online formats, but we’re bringing back an old friend.  

We are excited to announce that we will be hosting our first in-person data science bootcamp (since 2020) this March in Seattle! If you joined Data Science Dojo’s community during or after the COVID pandemic, you may have some questions about how it works, whether can really learn data science in 5 days, why DSD is comparing itself to MJ…I can’t explain the part about MJ other than that I thought it would be fun, but I can explain how in-person bootcamps work at DSD.  

How it works  

In-person bootcamps at Data Science Dojo are a little different than what you’ve seen on the market. Typically, in-person data science bootcamps are full-time, multiple weeks (I’ve seen as many as 24), and cost you an arm and a leg.

Our in-person bootcamp cuts through the fluff so that you’re applying concepts and techniques back at work in only five days, rather than weeks, without sacrificing any limbs.  

• 5 days  
• 10 hours per day 
• Industry expert instructors 
• Hands-on, practical exercises 
• Post-bootcamp supplemental learning  

 

 

Similar to our online format, we provide pre-bootcamp coursework to help our students prepare. These tutorials include topics like R & Python programming, data mining, and Azure ML (Machine Learning). These are important for our students to complete to be successful during the bootcamp.  

 

Learn Data Science with a “Think-Business-First” Approach: Hands-on Activities and Real-World Applications in our Bootcamp Class

When the bootcamp starts, you’re in class! You’ll have live instructors and TAs working with you to help you learn these complex topics. During class, we use a mix of conceptual learning and hands-on activities to drive a “think-business-first” approach to data science and instill a foundation for critical thinking.

Our goal is that our students can immediately start applying what they learn in the real world, and we have a plethora of use cases, extra practice material, and live coding notebooks to ramp up our students’ abilities.  

After each class period, you will have homework to reinforce your learning and prepare you for the next day. You will also work on an in-class Kaggle competition to compete with your peers for prizes, but more importantly, bragging rights.  

At the end of the 5th day, you’ll graduate from the program and become a Data Science Dojo alum. You’ll receive a verified certificate in association with the University of New Mexico, be invited to join DSD’s alumni group and take your lessons back to work to start solving problems with a new data science skillset.

Just because the bootcamp ends, doesn’t mean your education does. We provide post-bootcamp tutorials for our alumni to continue their data science education.  These include topics on NLP (Natural Language Processing), neural networks, and other more advanced techniques we don’t have time to cover during the bootcamp.  

Get more information on our in-person data science bootcamp

This is a lot to learn in one blog post, and I’ve done my best to try to make it as simple as possible. If you’re interested in solving problems with data and want to attend a fast-paced, in-person program, I encourage you to schedule a call with one of Data Science Dojo’s advisors.

With our expert instructors, hands-on practical exercises, and post-bootcamp tutorials, you’ll be on your way to becoming a data science pro in no time. Don’t miss this opportunity to take your career to the next level! 

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. 

Animating data science and machine learning videos can be a challenging task, especially if you are using Adobe After Effects. While this software is a powerful tool for creating visual effects, it can be difficult to use if you are not familiar with its features and capabilities. 

Let’s have a look at some of the most common challenges associated with the animation of complex data science videos: 

 

1. Declutter massive amount of data 

 

Challenge: 

One of the main challenges of animating data science and machine learning videos is the amount of data you have to work with. Data science and machine learning involve large sets of data that can be difficult to visualize concisely. Creating a compelling and informative video that tells a story with your data can make it difficult. 

Solution:  

One way to overcome this challenge is to focus on a few key data points and build your animation around them. This will allow you to highlight the most important aspects of your data and make it easier for your audience to understand. You can also use visualization tools like graphs and charts to help illustrate your data in a more effective way. 

 

Learn about 33 data visualization ways to improve your visual communication

 

2. Simplified presentation of complex ideas 

 

Challenge: 

Another challenge you may face when animating data science and machine learning videos is the complexity of the concepts you are trying to convey. Data science and machine learning are complex fields that can be difficult to explain to a general audience. This can make it challenging to create an animation that is both informative and easy to understand. 

Solution: 

One way to overcome this challenge is to break down complex concepts into smaller, more manageable chunks. You can do this by using analogies and examples to help illustrate the concepts in a more relatable way. You can also use animation techniques like motion graphics and character animation to help make the concepts more engaging and interactive. 

 

3. Achieving target in a short time 

 

Challenge: 

One of the most common challenges experienced by animators is the time it takes to create them. It gets difficult to achieve the best outcome in a limited time. Data science and machine learning videos often involve a lot of data and complex concepts, which can make them time-consuming to create. This can be frustrating for animators who are working on tight deadlines or who have limited resources. 

Solution: 

To overcome this challenge, it’s important to plan ahead and prioritize your tasks. This can help you stay on track and avoid last-minute rush jobs. You should also consider outsourcing some of the work if you don’t have the time or resources to handle it all yourself. This can help you get the job done faster and more efficiently. 

 

Key steps involved in data science video animation: 

 

The process of creating a data science and machine learning animated video using After Effects can be a challenging but rewarding experience. Here are the steps involved in the process: 

 

1. Gather data:

The first step in creating a data science and machine learning animated video is to gather relevant data that you want to showcase. This could be data from a recent study or research project, or it could be data from a company or organization that you want to highlight. 

 

2. Clean and organize the data:

Once you have gathered the data, you need to clean and organize it in a way that makes it easy to understand and visualize. This might involve sorting the data, eliminating outliers, and formatting it in a way that is easy to read and interpret. 

 

3. Create a script:

Next, you will need to write a script for your video that explains the data and its significance. This script should be clear and concise, and it should be written in a way that is easy for viewers to understand. 

 

4. Design the visual elements:

After you have a script, you can begin designing the visual elements of your video. This might include creating charts and graphs, selecting colors and fonts, and choosing other design elements that will help bring your data to life. 

 

5. Import the data into After Effects:

Once you have designed the visual elements, you can import your data into After Effects. This software allows you to create sophisticated animations and visual effects, so you can use it to bring your data to life in a dynamic and engaging way. 

 

6. Animating data:

With your data imported into After Effects, you can begin animating it. This might involve creating simple transitions between different data points, or it might involve more complex animations that highlight trends and patterns in the data. 

 

7. Add audio and other elements:

As you animate your data, you can also add audio elements such as music, voiceovers, and sound effects. These elements can help to enhance the impact of your video and make it more engaging for viewers. 

 

8. Render and export the video:

Once you have completed your animation, you can render and export your video. This involves saving the final version of your video in a format that can be easily shared with others. 

Develop a visual understanding of complex concepts 

Creating a data science and machine learning animated video can be a time-consuming process, but it is a great way to bring data to life and share it with others in an engaging and visually appealing way.  

With the right tools and techniques, you can create professional-quality videos that showcase your data in a dynamic and impactful way. 

Visit our YouTube channel to learn simply explained data science and machine learning concepts  

 

 

Written by Shahid Jamil

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.  

 

 

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 for other roles. 

A data analyst, however, uses data to report any observed trends and patterns. 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.  

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 handy 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 the field, 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.  

Read about: 2023 AI and Machine Learning trends

 

4. Data scientists are expert coders 

 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 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. On the non-technical end, there are business skills and understandings of various stakeholders in a particular situation.  

To conclude, a tool can be an excellent way to implement data 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 career. 

Any data science myths we missed?

 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. 

 

Get a behind-the-scenes look at Data Science Dojo’s intensive data science Bootcamp. Learn about the course curriculum, instructor quality, and overall experience in our comprehensive review.

“The more I learn, the more I realize what I don’t know”

(A quote by Raja Iqbal, CEO of DS-Dojo)

In our current era, the terms “AI”, “ML”, “analytics”–etc., are indeed THE “buzzwords” du jour. And yes, these interdisciplinary subjects/topics are **very** important, given our ever-increasing computing capabilities, big-data systems, etc. 

The problem, however, is that **very few** folks know how to teach these concepts! But to be fair, teaching in general–even for the easiest subjects–is hard. In any case, **this**–the ability to effectively teach the concepts of data-science–is the genius of DS-Dojo. Raja and his team make these concepts considerably easy to grasp and practice, giving students both a “big picture-,” as well as a minutiae-level understanding of many of the necessary details. 

Learn more about the Data Science Bootcamp course offered by Data Science Dojo

Still, a leery prospective student might wonder if the program is worth their time, effort, and financial resources. In the sections below, I attempt to address this concern, elaborating on some of the unique value propositions of DS-Dojo’s pedagogical methods.

The More Things Change…

Data Science enthusiasts today might not realize it, but many of the techniques–in their basic or other forms–have been around for decades. Thus, before diving into the details of data-science processes, students are reminded that long before the terms “big data,” AI/ML, and others became popularized, various industries had all utilized techniques similar to many of today’s data-science models. These include (among others): insurance, search engines, online shopping portals, and social networks. 

This exposure helps Data-Science Dojo students consider the numerous creative ways of gathering and using big data from various sources–i.e. directly from human activities or information, or from digital footprints or byproducts of our use of online technologies.

 

The Big Picture of the Data Science Bootcamp

As for the main curriculum contents, first, DS-Dojo students learn the basics of data exploration, processing/cleaning, and engineering. Students are also taught how to tell stories with data. After all, without predictive or prescriptive–and other–insights, big data is useless.

The bootcamp also stresses the importance of domain knowledge, and relatedly, an awareness of what precise data points should be sought and analyzed. DS-Dojo also trains students to critically assess: why, and how should we classify data. Students also learn the typical data-collection, processing, and analysis pipeline, i.e.:

1. Influx
2. Collection
3. Preprocessing
4. Transformation
5. Data-mining
6. And finally, interpretation and evaluation.

However, any aspiring (good) data scientist should disabuse themselves of the notion that the process doesn’t present challenges. Au contraire, there are numerous challenges; e.g. (among others):

1. Scalability
2. Dimensionality
3. Complex and heterogeneous data
4. Data quality
5. Data ownership and distribution, 
6. Privacy, 
7. Reaction time.

 

Deep dives

Following the above coverage of the craft’s introductory processes and challenges, DS-Dojo students are then led earnestly into the deeper ends of data-science characteristics and features. For instance, vis-a-vis predictive analytics, how should a data-scientist decide when to use unsupervised learning, versus supervised learning? Among other considerations, practitioners can decide using the criteria listed below.

 

Unsupervised Learning…Vs. … >>	<< …Vs. …Supervised Learning
>> Target values unknown	>> Targets known
>> Training data unlabeled	>> Data labeled
>> Goal: discover information hidden in the data	>> Goal: Find a way to map attributes to target value(s)
>> Clustering	>> Classification and regression

 

Read more about the supervised and unsupervised learning

 

Overall, the main domains covered by DS-Dojo’s data-science bootcamp curriculum are:

• An introduction/overview of the field, including the above-described “big picture,” as well as visualization, and an emphasis on story-telling–or, stated differently, the retrieval of actual/real insights from data;
• Overview of classification processes and tools
•  Applications of classification
• Unsupervised learning; 
• Regression;
• Special topics–e.g., text-analysis
• And “last but [certainly] not least,” big-data engineering and distribution systems. 

 

Method-/Tool-Abstraction

In addition to the above-described advantageous traits, data-science enthusiasts, aspirants, and practitioners who join this program will be pleasantly surprised with the bootcamp’s de-emphasis on specific tools/approaches.  In other words, instead of using doctrinaire approaches that favor only Python, R, Azure, etc., DS-Dojo emphasizes the need for pragmatism; practitioners should embrace the variety of tools at their disposal.

“Whoo-Hoo! Yes, I’m a Data Scientist!”

By the end of the bootcamp, students might be tempted to adopt the above stance–i.e., as stated above (as this section’s title/subheading). But as a proud alumnus of the program, I would cautiously respond: “Maybe!” And if you have indeed mastered the concepts and tools, congratulations!

But strive to remember that the most passionate data science practitioners possess a rather paradoxical trait: humility, and an openness to lifelong learning. As Raja Iqbal, CEO of DS-Dojo pointed out in one of the earlier lectures: The more I learn, the more I realize what I don’t know. Happy data-crunching!

 

 

Written by Seif Sekalala

Writing an SEO optimized blog is important because it can help increase the visibility of your blog on search engines, such as Google. When you use relevant keywords in your blog, it makes it easier for search engines to understand the content of your blog and to determine its relevance to specific search queries.

Consequently, your blog is more likely to rank higher on search engine results pages (SERPs), which can lead to more traffic and potential readers for your blog.

In addition to increasing the visibility of your blog, SEO optimization can also help to establish your blog as a credible and trustworthy source of information. By using relevant keywords and including external links to reputable sources, you can signal to search engines that your content is high-quality and valuable to readers.

5 things to consider for writing a top-performing blog

A successful blog reflects top-quality content and valuable information put together in coherent and comprehensible language to hook the readers.

The following key points can assist to strengthen your blog’s reputation and authority, resulting in more traffic and readers in the long haul.

 

 

1. Handpick topics from industry news and trends: One way to identify popular topics is to stay up to date on the latest developments in the data science and analytics industry. You can do this by reading industry news sources and following influencers on social media.

 

2.  Use free – keyword research tools: Do not panic! You are not required to purchase any keyword tool to accomplish this step. Simply enter your potential blog topic on search engine such as Google and check out the top trending write-ups available online.

This helps you identify popular keywords related to data science and analytics. By analyzing search volume and competition for different keywords, you can get a sense of what topics are most in demand.

 

3. Look for the untapped information in the market: Another way to identify high-ranking blog topics is to look for areas where there is a lack of information or coverage. By filling these gaps, you can create content that is highly valuable and unique to your audience.

 

4. Understand the target audience: When selecting a topic, it’s also important to consider the interests and needs of your target audience. Check out the leading tech discussion forums and groups on Quora, LinkedIn, and Reddit to get familiar with the upcoming discussion ideas. What are they most interested in learning about? What questions do they have? By addressing these issues, you can create content that resonates with your readers.

 

5. Look into the leading industry websites: Finally, take a look at what other data science and analytics bloggers are writing about. From these acknowledged websites of the industry, you can get ideas for topics that help you identify areas where you can differentiate yourself from the competition

 

Recommended blog structure for SEO:

Overall, SEO optimization is a crucial aspect of blog writing that can help to increase the reach and impact of your content. The correct flow of your blog can increase your chances of gaining visibility and reaching a wider audience. Following are the step-by-step guidelines to write an SEO optimized blog on data science and analytics:

 

 

1. Choose relevant and targeted keywords:

Identify the keywords that are most relevant to your blog topic. Some of the popular keywords related to data science topics can be:

• Big Data
• Business Intelligence (BI)
• Cloud Computing
• Data Analytics
• Data Exploration
• Data Management

These are some of the keywords that are commonly searched by your target audience. Incorporate these keywords into your blog title, headings, and throughout the body of your post. Read the beginner’s guide to keyword research by Moz.

2. Use internal and external links:

Include internal links to other pages or blog posts on the website you are publishing your blog, and external links to reputable sources to support your content and improve its credibility.

3. Use header tags:

Use header tags (H1, H2, H3, etc.) to structure your blog post and signal to search engines the hierarchy of your content. Here is an example of a blog with the recommended header tags and blog structure:

• H1: Top 10 Machine Learning books you must give a read6 books to help you learn data science
• H2: Machine Learning: 4 Books in 1
• H2: Mathematics for Machine Learning
• H2: Fundamentals of Deep Learning (O’Reilly)
• H2: Linear Algebra and Optimization for Machine Learning
• H2: The Hundred-Page Machine Learning Book
• H2: R for everyone
• H2: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron

 

4. Use alt text for images:

Add alt text to your images to describe their content and improve the accessibility of your blog. Alt text is used to describe the content of an image on a web page. It is especially important for people who are using screen readers to access your website, as it provides a text-based description of the image for them.

Alt text is also used by search engines to understand the content of images and to determine the relevance of a web page to a specific search query.

5. Use a descriptive and keyword-rich URL:

Make sure your blog post URL accurately reflects the content of your post and includes your targeted keywords. For example, if the target keyword for your blog is data science books, then the URL must include the keyword in it such as “top-data-science-books“.

6. Write a compelling meta description:

The meta description is the brief summary that appears in the search results below your blog title. Use it to summarize the main points of your blog post and include your targeted keywords. For the blog topic: Top 6 data science books to learn in 2023, the meta description can be:

“Looking to up your data science game in 2023? Check out our list of the top 6 data science books to read this year. From foundational concepts to advanced techniques, these books cover a wide range of topics and will help you become a well-rounded data scientist.”

 

Share your data science insights with the world

If this blog helped you learn writing a search engine friendly blog, then without waiting a further, choose the topic of your choice and start writing. We offer a platform to industry experts and knowledge geeks to evoke their ideas and share them with a million plus community of data science enthusiasts across the globe.

 

Every eCommerce business depends on information to improve its sales. Data science can source, organize and visualize information. It also helps draw insights about customers, marketing channels, and competitors.

 

Every piece of information can serve different purposes. You can use data science to improve sales, customer service, user experience, marketing campaigns, purchase journeys, and more.

 

How to use Data Science to boost eCommerce sales

Sales in eCommerce depend on a variety of factors. You can use data to optimize each step in a customer’s journey to gain conversions and enhance revenue from each conversion.

Analyze Consumer Behavior

Data science can help you learn a lot about the consumer. Understanding consumer behavior is crucial for eCommerce businesses as it dictates the majority of their decisions.

 

Consumer behavior analysis is all about understanding the relationship between things you can do and customers’ reactions to them. This analysis requires data science as well as psychology. The end goal is not just understanding consumer behavior, but predicting it.

 

For example, if you have an eCommerce store for antique jewelry, you will want to understand what type of people buy antique jewelry, where they search for it, how they buy it, what information they seek before purchasing, what occasions they buy it for, and so on.

 

 

 

You can extract data on consumer behavior on your website, social media, search engines, and even other eCommerce websites. This data will help you understand customers and predict their behavior. This is crucial for audience segmentation.

 

Data science can help segment audiences based on demographics, characteristics, preferences, shopping patterns, spending habits, and more. You create different strategies to convert audiences of different segments.

 

Audience segments play a crucial role in designing purchase journeys, starting from awareness campaigns all the way to purchase and beyond.

 

Optimize digital marketing for better conversion

You need insights from data analytics to make important marketing decisions. Customer acquisition information can tell you where the majority of your audience comes from. You can also identify which sources give you maximum conversions.

 

You can then use data to improve the performance of your weak sources and reinforce the marketing efforts of high-performing sources. Either way, you can ensure that your marketing efforts are helping your bottom line.

 

Once you have locked down your channels of marketing, data science can help you improve results from marketing campaigns. You can learn what type of content or ads perform the best for your eCommerce website.

 

Data science will also tell you when the majority of your audience is online on the channel and how they interact with your content. Most marketers try to fight the algorithms to win. But with data science, you can uncover the secrets of social media algorithms to maximize your conversions.

 

Suggest products for upselling & cross-selling

Upselling & Cross-selling are some of the most common sales techniques employed by ecommerce platforms. Data science can help make them more effective. With Market Basket or Affinity Analysis, data scientists can identify relationships between different products. 

 

By analyzing such information of past purchases and shopping patterns you can derive criteria for upselling and cross-selling. The average amount they spend on a particular type of product tells you how high you can upsell. If the data says that customers are more likely to purchase a particular brand, design, or color; you can upsell accordingly. 

 

 

 

Similarly, you can offer relevant cross-selling suggestions based on customers’ data. Each product opens numerous cross-selling options.

 

Instead of offering general options, you can use data from various sources to offer targeted suggestions. You can give suggestions based on individual customers’ preferences. For instance, A customer is more likely to click on a suggestion saying “A Red Sweater to go with your Blue Jeans’ ‘ if their previous purchase shows an inclination for the color red.

 

This way data science can help increase probability of upsold & cross-sold purchases so that eCommerce businesses get more revenue from their customers.

Analyze consumer feedback

Consumers provide feedback in a variety of ways, some of which can only be understood by learning data science. It is not just about reviews and ratings. Customers speak about their experience through social media posts, social shares, and comments as well.

Feedback data can be extracted from several places and usually comes in large volumes. Data scientists use techniques like text analytics, computational linguistics, and natural language processing to analyze this data.

 

For instance, you can compare the percentage of positive words and negative words used in reviews to get a general idea about customer satisfaction.

 

But feedback analysis does not stop with language. Consumer feedback is also hidden in metrics like time spent on page, CTR, cart abandonment, clicks on page, heat maps and so on. Data on such sublime behaviors can tell you more about the customer’s experience with your eCommerce website than reviews, ratings and feedback forms.

 

This information helps you identify problem areas that cause your customers to turn away from a purchase.

Personalize customer experience

To create a personalized experience, you need information about the customer’s behavior, previous purchases, and social activity. This information is scattered across the web, and you need lessons in data science to bring it to one place. But, more importantly, data science helps you draw insights from information.

 

With this insight you can create different journeys for different customer segments. You utilize data points to map a sequence of options that would lead a customer to conversion. 80% customers are more likely to purchase if the eCommerce website offers a personalized experience.

 

For example: Your data analytics say that a particular customer has checked out hiking boots but has abandoned most purchases at the cart. Now you can focus on personalizing this customer’s experience by focusing on cart abandonment issues such as additional charges, postage shipping cost, payment options etc.

 

Several eCommerce websites use data to train their chatbots to serve as personal shopping assistants for their customers. These bots use different data points to give relevant shopping ideas.

 

You can also draw insights from data science to personalize offers, discounts, landing pages, product gallery, upselling suggestions, cross-selling ideas and more. 

Use data science for decision making & automation

The information provided by data science serves as the foundation for decision-making for eCommerce businesses. In a competitive market, a key piece of information can help you outshine your competitors, gain more customers and provide a better customer experience.

Using data science for business decisions will also help you improve the performance of the company. An informed decision is always better than an educated guess.

In this blog, we asked ChatGPT to come up with some interesting and fun facts about the core areas related to data science, Artificial Intelligence, machine learning, and Statistics. The new AI tool surely knows the struggles of professionals and how to put it in a fun way.

Let’s take a look at some fun facts for data scientists put down by ChatGPT.

 

Some fun facts for Mathematicians by ChatGPT

 

Here are some fun facts for all statisticians to make you laugh by ChatGPT:

• Statisticians have a sense of humor too! The probability of someone laughing at a joke you tell is directly proportional to the number of statistical jokes you know.  
• A statistician’s favorite thing to do is to take a sample. 
• A statistician’s favorite drink is a “mean” martini. 
• A statistician’s favorite time of day is “meantime.” 
• Statisticians tend to “mean” well. 
• Statisticians are always looking for a “standard deviation” from the norm. 
• Statisticians are always looking for a “means” to get ahead. 
• Statisticians are never “mean” to each other – they are always “normally distributed.”
• Mathematicians are always on the lookout for new “equations” to solve.  
• A mathematician’s favorite thing is to “prove” a theorem.  
• Mathematicians are known to have a “matrix” of connections in the industry. 
• Mathematicians have been known to “calculate” the most unlikely scenarios. 
• A mathematician’s favorite type of math is “abstract” math – it keeps things interesting! 
• Mathematicians are always looking for ways to “optimize” their equations. 
• Mathematicians have a saying: “The more complex the problem, the better!” 
• Mathematicians are known to be “precise” – they never leave room for error in their calculations.  

 

Some fun facts for Data Scientists by ChatGPT  

 

Here are a few funny facts about data scientists: 

• Data scientists have been known to “mine” for data in the most unlikely places. 
• A data scientist’s favorite type of data is “big data” – the bigger, the better! 
• A data scientist’s favorite tool is the “data hammer” – they can pound any data into submission. 
• Data scientists have a saying: “The data never lies, but it can be misleading.” 
• Data scientists have been known to “data dunk” their colleagues – throwing them into a pool of data and seeing if they can swim. 
• Data scientists are always “data mining” for new insights and discovering “data gold.” 
• Data scientists are known to have “data-phoria” – a state of excitement or euphoria when they uncover a particularly interesting or valuable piece of data. 
• Data scientists have been known to “data mash” – combining different datasets to create something new and interesting. 

 

 Enroll in our Data Science Bootcamp course to become a Data Scientist today

 

Some fun facts for Machine Learning professionals by ChatGPT 

 

Here are some fun facts about machine learning professionals   

• Machine learning professionals are always on the lookout for new “learning opportunities.” 
• A machine learning professional’s favorite thing is to “train” their algorithms. 
• Machine learning professionals are known to have a “neural network” of friends in the industry. 
• Machine learning professionals have been known to “deep learn” on the job – immersing themselves in their work and picking up new skills along the way. 
• A machine learning professional’s favorite type of data is “clean” data – it makes their job much easier! 
• Machine learning professionals are always looking for ways to “optimize” their algorithms. 
• Machine learning professionals have a saying: “The more data, the merrier!” 
• Machine learning professionals are known to be “adaptive” – they can quickly adjust to new technologies and techniques. 

    

Some fun facts for AI experts by ChatGPT 

 

Here are a few funny facts about artificial intelligence experts:   

• AI experts are always on the lookout for new “intelligent” ideas. 
• AI experts have been known to “teach” their algorithms to do new tasks. 
• AI experts are known to have a “neural network” of connections in the industry. 
• AI experts have been known to “deep learn” on the job – immersing themselves in their work and picking up new skills along the way. 
• AI experts are always looking for ways to “optimize” their algorithms. 
• AI experts have a saying: “The more data, the smarter the AI!” 
• AI experts are known to be “adaptive” – they can quickly adjust to new technologies and techniques. 
• AI experts are always looking for ways to make their algorithms more “human-like.”  
• The term “artificial intelligence” was first coined in 1956 by computer scientist John McCarthy. 
• The first recorded instance of artificial intelligence was in the early 1800s when mathematician Charles Babbage designed a machine that could perform basic mathematical calculations. 
• One of the earliest demonstrations of artificial intelligence was the “Turing Test,” developed by Alan Turing in 1950. The test is a measure of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. 
• The first self-driving car was developed in the 1980s by researchers at Carnegie Mellon University. 
• In 1997, a computer program called Deep Blue defeated world chess champion Garry Kasparov, marking the first time a computer had beaten a human at chess. 
• In 2011, a machine translation system developed by Google called Google Translate was able to translate entire documents from Chinese to English with near-human accuracy. 
• In 2016, a machine learning algorithm developed by Google DeepMind called AlphaGo defeated the world champion at the ancient Chinese board game Go, which is considered to be much more complex than chess. 
• Artificial intelligence has the potential to revolutionize a wide range of industries, including healthcare, finance, and transportation.  

  

Some fun facts for Data Engineers by ChatGPT 

 

Here are a few funny facts about data engineers by ChatGPT: 

• Data engineers are always on the lookout for new “pipelines” to build. 
• A data engineer’s favorite thing is to “ingest” large amounts of data. 
• Data engineers are known to have a “data infrastructure” of connections in the industry. 
• Data engineers have been known to “scrape” the internet for new data sources. 
• A data engineer’s favorite type of data is “structured” data – it makes their job much easier! 
• Data engineers are always looking for ways to “optimize” their data pipelines. 
• Data engineers have a saying: “The more data, the merrier!” 
• Data engineers are known to be “adaptive” – they can quickly adjust to new technologies and techniques. 

 

Do you have a more interesting answer by ChatGPT?

People across the world are generating interesting responses using ChatGPT. The new AI tool has an immense contribution to the knowledge of professionals associated with different industries. Not only does it produce witty responses but also shares information that is not known by many. Share with us your use of this amazing AI tool as a Data Scientist.

In the past few years, the number of people entering the field of data science has increased drastically because of higher salaries, an increasing job market, and more demand. 

Undoubtedly, there are unlimited programs to learn data science, several companies offering in-depth Data Science Bootcamp, and a ton of channels on YouTube that are covering data science content. The abundance of data science content and learning pathways can easily confuse one with where to begin or how to start their data science career.   

 

 

To ease this data science journey for beginners, intermediate, or starters, we are going to list a couple of data science tutorials, crash courses, webinars, and videos. The aim of this blog is to help beginners navigate their data science path, and also help them to determine if data science is the most perfect career choice for them or not. 

 

If you are planning to add value to your data science skillset, check out our Python for Data Science training. 

 

Let’s get started with the list:

 1. A day in the life of a data scientist

 This talk will introduce you to what a typical data scientist’s job looks like. It will familiarize you with the day-to-day work that a data scientist does and differentiate between the different roles and responsibilities that data scientists have across companies. 

This talk will help you understand what a typical day in the data scientist’s life looks like and assist you to decide if data science is the perfect choice for your career.   

 

 

2. Data mining crash course

Data mining has become a vital part of data science and analytics in today’s world. And, if you planning to jumpstart your career in the field of data science, it is important for you to understand data mining. Data mining is a process of digging into different types of data and data sets to discover hidden connections between them.

The concept of data mining includes several steps that we are going to cover in this course.  In this talk, we will cover how data mining is used in feature selection, connecting different data attributes, data aggregation, data exploration, and data transformation.

Additionally, we will cover the importance of checking data quality, reducing data noise, and visualizing the data to demonstrate the importance of good data.  

 

 

3. Intro to data visualization with R & ggplot2 

While tools like Excel, Power BI, and Tableau are often the go-to solutions for data visualizations, none of these tools can compete with R in terms of the sheer breadth of, and control over, crafted data visualizations. Thereby, it is important for one to learn about data visualization with R & ggplot2.  

In this tutorial, you will get a brief introduction to data visualization with the ggplot2 package. The focus of the tutorial will be using ggplot2 to analyze your data visually with a specific focus on discovering the underlying signals/patterns of your business.   

 

 

 

 4. Crash course in data visualization: Tell a story with your data

Telling a story with your data is more important than ever. The best insights and machine learning models will not create an impact unless you are able to effectively communicate with your stakeholders. Hence, it is very important for a data scientist to have an in-depth understanding of data visualization.   

 In this course, we will cover chart theory and pair programs that will help us create a chart using Python, Pandas, and Plotly.   

 

 

5. Feature engineering 

To become a proficient data scientist, it is significant for one to learn about feature engineering. In this talk, we will cover ways to do feature engineering both with dplyr (“mutate” and “transmute”) and base R (“ifelse”). Additionally, we’ll go over four different ways to combine datasets. 

With this talk, you will learn how to impute missing values as well as create new values based on existing columns.  

 

 

6. Intro to machine learning with R & caret 

The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s huge collection of open-source machine-learning algorithms. If you are a data scientist working with R, the caret package (short for Classification and Regression Training) is a must-have tool in your toolbelt.   

In this talk, we will provide an introduction to the caret package. The focus of the talk will be using caret to implement some of the most common tasks of the data science project lifecycle and to illustrate incorporating caret into your daily work.   

 

 

7. Building robust machine learning models 

Modern machine learning libraries make the model building look deceptively easy. An unnecessary emphasis (admittedly, annoying to the speaker) on tools like R, Python, SparkML, and techniques like deep learning is prevalent. 

Relying on tools and techniques while ignoring the fundamentals is the wrong approach to model building. Thereby, our aim here is to take you through the fundamentals of building robust machine-learning models.  

 

 

8. Text analytics crash course with R

 Industries across the globe deal with structured and unstructured data. To generate insights companies, work towards analyzing their text data. The data pipeline for transforming unstructured text into valuable insights consists of several steps that each data scientist must learn about. 

This course will take you through the fundamentals of text analytics and teach you how to transform text data using different machine-learning models.   

 

 

9. Translating data into effective decisions

As data scientists, we are constantly focused on learning new ML techniques and algorithms. However, in any company, value is created primarily by making decisions. Therefore, it is important for a data scientist to embrace uncertainty in a data-driven way.   

 In this talk, we present a systematic process where ML is an input to improve our ability to make better decisions, thereby taking us closer to the prescriptive ideal.   

 

 

10. Data science job interviews 

Once you are through your data science learning path, it is important to work on your data science interviews in order to uplift your career. In this talk, you will learn how to solve SQL, probability, ML, coding, and case interview questions that are asked by FAANG + Wall Street.  

 We will also share the contrarian job-hunting tips that can help you to find a job at Facebook, Google, or an ML startup.  

 

 

 

Choose from Available Data Science Learning Pathways Today!

We hope that the aforementioned 12 talks assist you to get started with your data science learning path. If you are looking for a more detailed guide, then do check out our Data Science Roadmap.

If you want to receive data science blogs, infographics, cheat sheets, and other useful resources right into your inbox, subscribe to our weekly & monthly newsletter.

 

 

Whether you are new to data science or an expert, our upcoming talks, tutorials, and crash courses can help you learn diverse data science & engineering concepts, so make sure to stay tuned with us. 

 

 

The finance industry deals with large volumes of data. With the increase in data and the accessibility of AI, financial institutions can’t ignore the benefits of data science. They have to use data science to improve their services and products. It helps them make better decisions about customer behavior, product development, marketing strategies, etc.

From using machine learning algorithms to Python for Data Science, there are several key methods of applications of data science in finance. Listed below are the top eight examples of data science being used in the finance industry.

1. Trend forecasting

Data science plays a significant role in helping financial analysts forecast trends. For instance, data science uses quantitative methods such as regression analysis and linear programming to analyze data. These methods can help extract hidden patterns or features from large amounts of data, making trend forecasting easier and more accurate for financial institutions

2. Fraud detection

Financial institutions can be vulnerable to fraud because of their high volume of transactions. In order to prevent losses caused by fraud, organizations must use different tools to track suspicious activities. These include statistical analysis, pattern recognition, and anomaly detection via machine/deep learning. By using these methods, organizations can identify patterns and anomalies in the data and determine whether or not there is fraudulent activity taking place.

For example, financial institutions often use historical transaction data to detect fraudulent behavior. So when banks detect inconsistencies in your transactions, they can take action to prevent further fraudulent activities from happening.

3. Market research

Tools such as CRM and social media dashboards use data science to help financial institutions connect with their customers. They provide information about their customers’ behavior so that they can make informed decisions when it comes to product development and pricing.

Remember that the finance industry is highly competitive and requires continuous innovation to stay ahead of the game. Data science initiatives, such as a Data Science Bootcamp or training program, can be highly effective in helping companies develop new products and services that meet market demands.

4. Investment management

Investment management is another area where data science plays an important role. Companies use data-driven approaches to optimize investment portfolios. They also use predictive models, such as financial forecasting, to estimate future returns based on past performance. Such predictions allow investors to maximize profits and minimize risks when it comes to investing. In addition to providing valuable insight into the future, data science also provides guidance on how to best allocate capital and reduce risk exposure.

5. Risk analysis

Risks are unavoidable in any organization. However, managing those risks requires understanding their nature and causes. In the finance industry, companies use data science methods such as risk assessment and analysis to protect themselves against potential losses.

For example, they can tell you which products are likely to fail, and which assets are most susceptible to theft and other types of loss. And when applied properly, these tools can help an organization improve security, efficiency, and profitability.

6. Task automation

One of the greatest challenges faced by many firms today is the need to scale up operations while maintaining efficiency. To do so, they must automate certain processes. One way to achieve this goal is through the use of data science. Data scientists can develop tools that improve existing workflows within the finance industry.

Examples of these tools include speech-to-text, image recognition, and natural language processing. The finance industry uses insights from data science to automate systems that eliminate human error and accelerate operational efficiency.

7. Customer service

It’s no surprise that customer satisfaction affects revenue growth. As a result, companies spend large amounts of money to ensure that their customers receive top-notch service. Data science initiatives can help financial services providers deliver a superior experience to their customers. Whether it’s improving customer support apps or streamlining internal communications, financial companies can leverage this technology to transform their operations.

For instance, financial institutions can track consumer behavior to provide better customer service. A company may use data analytics to identify the best time to contact consumers by analyzing their online behavior. Companies can also monitor social media conversations and other sources for signs of dissatisfaction regarding their services to improve customer satisfaction.

8. Scalability

For certain financial institutions, the ability to scale up could mean the difference between success and failure. The good news is that data science offers solutions and insight that help companies identify what areas need to be scaled. These insights help them decide whether they should hire additional staff or invest in new equipment, among other things.

A good example of using data analytics for scalability is IBM’s HR Attrition Case Study. IBM, one of the world’s leading technology firms, has been able to use data science to solve its own scaling challenges by using it to analyze trends and predict future outcomes. This study shows how data scientists used predictive analytics to understand why employees quit their jobs at IBM.

Data science revolutionizing finance industry

There’s no doubt that data science will revolutionize almost all aspects of the financial industry. By using different data science tools and methods, financial companies can gain competitive advantages. The great thing about data science is that it can be learned through various methods.

Data science bootcamps, online courses, and books offer all the tools necessary to get started. As a result, anyone who works in finance—whether they are junior analysts or senior executives—can learn how to incorporate data science techniques in their industry.

There are several informative data science podcasts out there right now, giving you everything you need to stay up to date on what’s happening. We previously covered many of the best podcasts in this blog, but there are lots more that you should be checking out. Here are 10 more excellent podcasts to try out.

 

 

10 Best Podcasts on Data Science You Must Listen To

1. Analytics Power Hour 

Every week hosts, Michael Helbling, Tin Wilson, and Moe Kiss cover a different analytics topic that you may want to know about. The show was founded on the premise that the best discussions always happen at drinks after a conference or show. 

Recent episodes have covered topics like analytics job interviews, data as a product, and owning vs. helping in analytics. There are a lot to learn here, so they’re well worth a listen. 

2. DataFramed

This podcast is hosted by DataCamp, and in it, you’ll get interviews with some of the top leaders in data. “These interviews cover the entire range of data as an industry, looking at its past, present, and future. The guests are from both the industry and academia sides of the data spectrum too” says Graham Pierson, a tech writer at Ox Essays and UK Top Writers.   

There are lots of episodes to dive into, such as ones on building talent strategy, what makes data training programs successful, and more.

3. Lex Fridman Podcast

If you want a bigger picture of data science, then listen to this show. The show doesn’t exclusively cover data science anymore, but there’s plenty here that will give you what you’re looking for. 

You’ll find a broader view of data, covering how data fits in with our current worldview. There are interviews with data experts so you can get the best view of what’s happening in data right now.

4. The Artists of Data Science

This podcast is geared toward those who are looking to develop their career in data science. If you’re just starting, or are looking to move up the ladder, this is for you. There’s lots of highly useful info in the show that you can use to get ahead. 

There are two types of episodes that the show releases. One is advice from experts, and the others are ‘happy hours, where you can send in your questions and get answers from professionals.

5. Not So Standard Deviations

This podcast comes from two experts in data science. Roger Peng is a professor of biostatistics at John Hopkins School of Public Health, and Hilary Parker is a data scientist at Stitch Fix. They cover all the latest industry news while bringing their own experience to the discussion.

Their recent episodes have covered subjects like QR codes, the basics of data science, and limited liability algorithms.

 

Find out other exciting  18 Data Science podcasts

6. Gradient Dissent

Released twice a month, this podcast will give you all the ins and outs of machine learning, showing you how this tech is used in real-life situations. That allows you to see how it’s being used to solve problems and create solutions that we couldn’t have before. 

Recent episodes have covered high-stress scenarios, experience management, and autonomous checkouts.

7. In Machines We Trust

This is another podcast that covers machine learning. It describes itself as covering ‘the automation of everything, so if that’s something you’re interested in, you’ll want to make sure you tune in. 

“You’ll get a sense of what machine learning is being used for right now, and how it impacts our daily lives,” says Yvonne Richards, a data science blogger at Paper Fellows and Boom Essays. The episodes are around 30 minutes long each, so it won’t take long to listen and get the latest info that you’re looking for.

8. More or Less

This podcast covers the topic of statistics through noticeably short episodes, usually 8 minutes or less each. You’ll get episodes that cover everything you could ever want to know about statistics and how they work.   

For example, you can find out how many swimming pools of vaccines would be needed to give everyone a dose, see the one in two cancers claim debunked, and how data science has doubled life expectancy.

9. Data Engineering Podcast

This show is for anyone who’s a data engineer or is hoping to become one in the future. You’ll find lots of useful info in the podcast, including the techniques they use, and the difficulties they face. 

Ensure you listen to this show if you want to learn more about your role, as you’ll pick up a lot of helpful tips.

10. Data viz Today

This show doesn’t need a lot of commitment from you, as they release 30-minute episodes monthly. The podcast covers data visualization, and how this helps to tell a story and get the most out of data no matter what industry you work in.

Share with us Exciting Data Science Podcasts

These are all great podcasts that you can check out to learn more about data science. If you want to know more, you can check out Data Science Dojo’s informative sessions on YouTube. If we missed any of your favorite podcasts, do share them with us in the comments!

These interviews cover the entire range of data as an industry, looking at its past, present, and future. The guests are from both the industry and academia sides of the data spectrum too, says Graham Pierson, a tech writer at Academized.

Most people have heard the terms “data science” and “AI” at least once in their lives. Indeed, both of these are extremely important in the modern world, as they are technologies that help us run quite a few of our industries. 

But even though data science and Artificial Intelligence are somewhat related to one another, they are still very different. There are things they have in common, which is why they are often used together, but it is crucial to understand their differences as well.

In this blog, we will explore the answers to data science vs AI vs machine learning, hoping to find the right demand for the advancing digital world.

What is Data Science? 

As the name suggests, data science is a field that involves studying and processing large quantities of data using a variety of technologies and techniques to detect patterns, make conclusions about the data, and aid in the decision-making process. Essentially, it is an intersection of statistics and computer science largely used in business and different industries.

 

 

The standard data science lifecycle includes capturing data and then maintaining, processing, and analyzing it before finally communicating conclusions about it through reporting. This makes data science extremely important for analysis, prediction, decision-making, problem-solving, and many other purposes. 

 

 

What is Artificial Intelligence? 

Artificial Intelligence is the field that involves the simulation of human intelligence and the processes within it by machines and computer systems. Today, it is used in a wide variety of industries and allows our society to function as it currently does by using different AI-based technologies. 

Some of the most common examples in action include machine learning, speech recognition, and search engine algorithms. While AI technologies are rapidly developing, there is still a lot of room for their growth and improvement.

For instance, there is no powerful enough content generation tool that can write texts that are as good as those written by humans. Therefore, it is always preferred to hire an experienced writer to maintain the quality of work.  

What is Machine Learning? 

As mentioned above, machine learning is a type of AI-based technology that uses data to “learn” and improve specific tasks that a machine or system is programmed to perform. Though machine learning is seen as a part of the greater field of AI, its use of data puts it firmly at the intersection of data science and AI.

Similarities Between Data Science and AI 

By far the most important point of connection between data science and Artificial Intelligence is data. Without data, neither of the two fields would exist, and the technologies within them would not be used so widely in all kinds of industries.

In many cases, data scientists and AI specialists work together to create new technologies, improve old ones, and find better ways to handle data. 

As explained earlier, there is a lot of room for improvement when it comes to AI technologies. The same can be somewhat said about data science. That’s one of the reasons businesses still hire professionals to accomplish certain tasks, like custom writing requirements, design requirements, and other administrative work.

 

 

Differences Between Data Science and AI

There are quite a few differences between both. These include:

Purpose – It aims to analyze data to make conclusions, predictions, and decisions. Artificial Intelligence aims to enable computers and programs to perform complex processes in a similar way to how humans do. 

Scope – This includes a variety of data-related operations such as data mining, cleansing, reporting, etc. It primarily focuses on machine learning, but there are other technologies involved too such as robotics, neural networks, etc. 

Application – Both are used in almost every aspect of our lives, but while data science is predominantly present in business, marketing, and advertising, AI is used in automation, transport, manufacturing, and healthcare. 

Examples of Data Science and Artificial Intelligence in Use 

To give you an even better idea of what data science and Artificial Intelligence are used for, here are some of the most interesting examples of their application in practice: 

• Analytics – Analyze customers to better understand the target audience and offer the kind of product or service that the audience is looking for. 
• Monitoring – Monitor the social media activity of specific types of users and analyze their behavior. 
• Prediction – Analyze the market and predict demand for specific products or services in the nearest future. 
• Recommendation – Recommend products and services to customers based on their customer profiles, buying behavior, etc. 

• Forecasting – Predict the weather based on a variety of factors and then use these predictions for better decision-making in the agricultural sector. 
• Communication – Provide high-quality customer service and support with the help of chatbots. 
• Automation – Automate processes in all kinds of industries, from retail and manufacturing to email marketing and pop-up on-site optimization. 
• Diagnosing – Identify and predict diseases, give correct diagnoses, and personalize healthcare recommendations. 
• Transportation – Use self-driving cars to get where you need to go. Use self-navigating maps to travel. 

• Assistance – Get assistance from smart voice assistants that can schedule appointments, search for information online, make calls, play music, and more. 
• Filtering – Identify spam emails and automatically get them filtered into the spam folder. 
• Cleaning – Get your home cleaned by a smart vacuum cleaner that moves around on its own and cleans the floor for you. 
• Editing – Check texts for plagiarism, proofread, and edit them by detecting grammatical, spelling, punctuation, and other linguistic mistakes. 

It is not always easy to tell which of these examples is about data science and which one is about Artificial Intelligence because many of these applications use both of them. This way, it becomes even clearer just how much overlap there is between these two fields and the technologies that come from them. 

Data Science vs AI vs ML: What is Your Choice?

At the end of the day, data science and AI remain some of the most important technologies in our society and will likely help us invent more things and progress further. As a regular citizen, understanding the similarities and differences between the two will help you better understand how data science and Artificial Intelligence are used in almost all spheres of our lives. 

 

In this blog, we will discuss how companies apply data science in business and use combinations of multiple disciplines such as statistics, data analysis, and machine learning to analyze data and extract knowledge. 

If you are a beginner or a professional seeking to learn more about concepts like Machine Learning, Deep Learning, and Neural Networks, the overview of these videos will help you develop your basic understanding of Data Science.

 

 

Overview of the Free Data Science Course for Beginners 

If you are an aspiring data scientist, it is essential for you to understand the business problem first. It allows you to set the right direction for your data science project to achieve business goals.  

As you are assigned a data science project, you must assure yourself to gather relevant information around the scope of the project. For that you must perform three steps: 

1. Ask relevant questions from the client 
2. Understand the objectives of the project 
3. Defines the problem that needs to be tackled 

As you are now aware of the business problem, the next step is to perform data acquisition. Data is gathered from multiple sources such as: 

• Web servers 
• Logs 
• Databases 
• APIs 

• Online repositories 

1. Getting Started with Python and R for Data Science 

Python is an open-source, high-level, object-oriented programming language that is widely used for web development and data science. It is a perfect fit for data analysis and machine learning tasks, as it is easy to learn and offers a wide range of tools and features.  

Python is a flexible language that can be used for a variety of tasks, including data analysis, programming, and web development. Python is an ideal tool for data scientists who are looking to learn more about data analysis and machine learning.

 

 

Python is a great choice for beginners as well as experienced developers who are looking to expand their skill set. Python is an ideal language for data scientists who are looking to learn more about data analysis and machine learning. It is used to accomplish a variety of tasks, including data analysis, programming, and web development.  

Python is an ideal tool for data scientists who are looking to learn more about data analysis and machine learning. Python is a great choice for beginners as well as experienced developers who are looking to expand their skill set.  

2. Intro to Big Data, Data Science & Predictive Analytics 

Big data is a term that has been around for a few years now, and it has become increasingly important for businesses to understand what it is and how it can be used. Big data is basically any data that is too large to be stored on a single computer or server and instead needs to be spread across many different computers and servers in order to be processed and analyzed.  

The main benefits of big data are that it allows businesses to gain a greater understanding of their customers and the products they are interested in, which allows them to make better decisions about how to market and sell their products. In addition, big data also allows businesses to take advantage of artificial intelligence (AI) technology, which can allow them to make predictions about the future based on the data they are collecting.

 

Intro to Big Data, Data Science & Predictive Analytics

 

The main areas that businesses need to be aware of when they start using big data are security and privacy. Big data can be extremely dangerous if it is not properly protected, as it can allow anyone with access to the data to see the information that is being collected.

In addition, big data can also be extremely dangerous if it is not properly anonymized, as it can allow anyone with access to the data to see the information that is being collected. 

One of the best ways to protect your data is by using encryption technology. Encryption allows you to hide your data from anyone who does not have access to it, so you can ensure that no one but you have access to your data. However, encryption does not protect 

 3. Intro to Azure ML & Cloud Computing 

Cloud computing is a growing trend in IT that allows organizations to perform delivery of computing services including servers, storage, databases, networking, software, analytics, and intelligence. Cloud offers a number of benefits, including reduced costs and increased flexibility.  

Organizations can take advantage of the power of the cloud to reduce their costs and increase flexibility, while still being able to stay up to date with new technology. In addition, organizations can take advantage of the flexibility offered by the cloud to quickly adopt new technologies and stay competitive. 

 

Intro to Azure ML & Cloud Computing

 

In this intro to Azure Machine learning & Cloud Computing, we’ll cover some of the key benefits of using Azure and how it can help organizations get started with machine learning and cloud computing. We’ll also cover some of the key tools that are available in Azure to help you get started with your machine learning and cloud computing projects. 

 

Start Your Data Science Journey Today 

If you are afraid of spending hundreds of dollars to enroll in a data science course, then direct yourself to the hundreds of free videos available online. Master your Data Science learning and step into the world of advanced technology.

You can also explore our data science bootcamp to kickstart your journey!

 

Data science is used in different fields and industries. And believe it or not, it also plays a significant role in digital marketing. In this post, that is what we’re going to be discussing. 

Data science is a big field, and it is employed extensively in different industries, from healthcare and transport to education and commerce. In fact, it is the cornerstone of groundbreaking technologies such as AI-based virtual assistants and self-driving cars. 

The definition of data science proffered by The Journal of Data Science is: 

“By ‘Data Science’, we mean almost everything that has something to do with data.” 

Looking at this definition, it’s easy to appreciate the fact that there is virtually no field or industry that does not utilize data science in some capacity. It’s everywhere, albeit in varying degrees. 

And as such, it’s also utilized in digital marketing. 

At a glance, it can be a little difficult to understand just how data science plays a role in digital marketing and how it benefits the same. But don’t worry. That’s what we’re going to be clearing up in this post. 

What is Data Science? 

We want to start off with the basics, so let’s look at what data science is. Although we did start off with a definition from The Journal of Data Science, it’s not very explanatory. 

Data science can be defined as the field or study that deals with finding and extracting useful and meaningful statistics and insights from a collection of structured and unstructured data. 

If we wanted to, we could go a little sophisticated and step into the shoes of some sage from the Middle Ages to define data science as “…to make ordered, that which is unordered…”. It’s a bit much, but it conveys the idea nicely. 

The process involved in data science is divided into various steps, which are collectively known as the Data Science Life Cycle. There aren’t any specific steps that can be universally enumerated as being part of the Data Science life cycle but, generally, it involves the following: 

• Data collection 
• Data organization 
• Data processing i.e., data mining, data modeling etc. 
• Data analysis 
• Finalization of results 

If you want, you can learn more about data science by taking this course. 

How Data Science is useful in digital marketing 

Now that we’re done with this preamble, let’s move on to discuss how data science can be useful in digital marketing. 

1. Keyword research 

One of the main benefits of data science in digital marketing is providing help with keyword research. Actually, before moving on, let’s clear up how exactly keyword research is related to digital marketing. 

Keyword research is a vital and necessary part of Search Engine Optimization (SEO). And SEO itself is a major branch of digital marketing. That’s basically how these two are connected. 

Let’s get back to the point. 

Whenever a digital marketing expert wants to work on the SEO of their website, they first have to create a keyword strategy for the content. The keyword strategy basically describes the short-tail and long-tail keywords that have to be featured in the website’s content and metadata. It also describes the number of times that the keywords have to be used and so on. 

Now, there is no limit to the number of keywords that are (and can be) searched by online users. They literally run into trillions. When someone has to select a few from this vast and virtually endless trove of keywords, they have to employ data science. 

Read more about marketing analytics features

6 marketing analytics features to drive greater revenue

 

Here is how data science can work in keyword research: 

• For the first phase, the digital marketer (or the SEO specialist) will narrow the keywords down to the ones related to their niche. This is, as we mentioned above, the “data collection” step. 
• Then, from this collection of keywords, the ones with high search volumes will be prioritized and short-listed. This is the “data organization” step. 
• After this, the specialist will have to find those long-tail and short-tail keywords that have a manageable ranking difficulty. In other words, this step will entail going through the shortlisted keywords and handpicking the most suitable ones. 
• Then, the selected keywords will be refined even more until the finalized list is prepared. This can be referred to as the “data analysis” step. 
• And once all the above is done, the list of keywords will be prepared in a document and given to the relevant personnel. This is the last step of the data science life cycle. 

So, taking a look from the first step of the process to the last one, we can observe that from a list of infinite keywords, a selected number of them were handpicked and finalized. Again, this is basically what data science is. To find patterns and useful insights from unsorted or sorted data. 

2. Analysis of website performance metrics 

This is yet another instance of digital marketing where data science can be highly beneficial. 

Basically, digital markers have to keep an eye on the performance of their website or online platform. They have to see how users are interacting with the various web pages and how much traffic the website(s) is/are generating. 

To measure website performance, there are actually a lot of different stats and metrics. For example, some of them include: 

• Dwell time 
• Bounce rate 
• Amount of traffic 
• Requests per second 
• Error rate 

By employing data science strategies to gather and analyze the various metrics, digital marketers can easily understand how well their website is working and how users are interacting with it. 

Similarly, by analyzing these metrics, they can also easily find out if the website (or a particular webpage) has been hit by a search engine penalty. This is actually a very useful benefit of keeping on top of website performance metrics. 

There are different types of violations that can bring about a penalty from the search engine, or that can just simply reduce the traffic/popularity of a certain webpage. 

For one, if a page takes a lot of time to load, it can get abandoned by a lot of users. This can be detected if there is a rise in the bounce rate and a decrease in the dwell time. Incidentally, the loading time itself is a website performance metric on its own. 

To improve the loading time, methods such as code beautification and minimization can be used. Similarly, the images and effects featured on the page can be toned down etc. 

Plagiarism is also a harmful factor that can get websites penalized. These types of penalties can either reduce a website’s rank or get it completely de-listed. 

To avoid this, webmasters always have to check plagiarism before finalizing any content for their websites. 

This is usually done with the help of plagiarism-checking tools that can scan the given content against the internet in order to find any duplication that may exist in the former. 

3. Monitoring website ranking statistics 

Just as monitoring website performance by analyzing statistics like the bounce rate, dwell time etc., is important, staying on top of the ranking statistics is equally necessary. 

By staying up-to-date with the website ranking in the SERPs, digital marketers are able to adjust and manage their SEO strategies. If upon taking a certain step, the rank of the site drops, then it means that it (the step) should not be taken in the future. On the other hand, if the rank rises after making some changes to the website, then it is a signal indicating that the changes are beneficial rather than harmful. 

Data science can be employed to keep up with this information as well. 

Grow digital marketing with Data Science

There are actually a lot of other ways in which data science can be useful in digital marketing. But, since we want to stick to brevity, we’ve listed some common and main ones above. 

 

Written by Eiswan Ali Kazmi

Get hired as a Data Analyst by confidently responding to the most frequently asked interview questions. No matter how qualified or experienced you are, if you stumble over your thoughts while answering the interviewer, it might take away some of your chances of getting onboard. 

 

In this blog, you will find the top data analysts interview questions covering both technical and non-technical areas of expertise.  

List of Data Analysts interview questions 

1. Share about your most successful/most challenging data analysis project? 

In this question, you can also share your strengths and weaknesses with the interviewer.   

When answering questions like these, data analysts must attempt to share both their strengths and weaknesses. How do you deal with challenges and how do you measure the success of a data project? You can discuss how you succeeded with your project and what made it successful.  

Take a look at the original job description to see if you can incorporate some of the requirements and skills listed. If you were asked the negative version of the question, be honest about what went wrong and what you would do differently in the future to fix the problem. Despite our human nature, mistakes are a part of life. What’s critical is your ability to learn from them. 

Further talk about any SAAS platforms, programming languages, and libraries. Why did you use them and how did you use them to accomplish yours?

Discuss the entire pipeline of your projects from collecting data, to turning it into valuable insights. Describe the ETL pipeline, including data cleaning, data preprocessing, and exploratory data analysis. What were your learnings and what issues did you encounter, and how did you deal with them. 

Enroll in Data Science Bootcamp today to begin your journey

2. Tell us about the largest data set you’ve worked with? Or what type of data have you worked with in the past? 

What they’re really asking is: Can you handle large data sets?  

Data sets of varying sizes and compositions are becoming increasingly common in many businesses. Answering questions about data size and variety requires a thorough understanding of the type of data and its nature. What data sets did you handle? What types of data were present? 

It is not necessary that you only mention a dataset you worked with at your job. But you can also share about varying sizes, specifically large datasets, you worked with as a part of a data analysis course, Bootcamp, certificate program, or degree. As you put together a portfolio, you may also complete some independent projects where you find and analyze a data set. All of this is valid material to build your answer.  

The more versatile your experience with datasets will be, the greater the chances there are of getting hired.  

Read more about several types of datasets here:

32 datasets to uplift your skills in data science

 

3. What is your process for cleaning data? 

The expected answer to this question will include details about: How you handle missing data, outliers, duplicate data, etc.?c.? 

Data analysts are widely responsible for data preparation, data cleansing, or data cleaning. Organizations expect data analysts to spend a significant amount of time preparing data for an employer. As you answer this question, share in detail with the employer why data cleaning is so important. 

In your answer, give a short description of what data cleaning is and why it’s important to the overall process. Then walk through the steps you typically take to clean a data set. 

 Learn about Data Science Interview Questions and begin your career as a data scientist today.

4. Name some data analytics software you are familiar with. OR what data software have you used in the past? OR What data analytics software are you trained in? 

What they need to know: Do you have basic competency with common tools? How much training will you need? 

Before you appear for the interview, it’s a good time to look at the job listing to see what software was mentioned. As you answer this question, describe how you have used that software or something similar in the past. Show your knowledge of the tool by employing associated words.  

Mention software solutions you have used for a variety of data analysis phases. You don’t need to provide a lengthy explanation. What data analytics tools you used and for what purpose will satisfy the interviewer. 

  

5. What statistical methods have you used in data analysis? OR what is your knowledge of statistics? OR how have you used statistics in your work as a Data Analyst? 

What they’re really asking: Do you have basic statistical knowledge? 

Data analysts should have at least a rudimentary grasp of statistics and know-how that statistical analysis helps business goals. Organizations look for a sound knowledge of statistics in Data analysts to handle complex projects conveniently. If you used any statistical calculations in the past, be sure to mention it. If you haven’t yet, familiarize yourself with the following statistical concepts: 

• Mean 
• Standard deviation 
• Variance
• Regression 
• Sample size 
• Descriptive and inferential statistics 

While speaking of these, share information that you can derive from them. What knowledge can you gain about your dataset? 

Read these amazing 12 Data Analytics books to strengthen your knowledge

 

12 excellent Data Analytics books you should read in 2022

 

6. What scripting languages are you trained in? 

In order to be a data analyst, you will almost certainly need both SQL and a statistical programming language like R or Python. If you are already proficient in the programming language of your choice at the job interview, that’s fine. If not, you can demonstrate your enthusiasm for learning it.  

In addition to your current languages’ expertise, mention how you are developing your expertise in other languages. If there are any plans for completing a programming language course, highlight its details during the interview. 

To gain some extra points, do not hesitate to mention why and in which situations SQL is used, and why R and python are used. 

 

7. How can you handle missing values in a dataset? 

This is one of the most frequently asked data analyst interview questions, and the interviewer expects you to give a detailed answer here, and not just the name of the methods. There are four methods to handle missing values in a dataset. 

• Listwise Deletion 

In the listwise deletion method, an entire record is excluded from analysis if any single value is missing. 

• Average Imputation  

Take the average value of the other participants’ responses and fill in the missing value. 

• Regression Substitution 

You can use multiple-regression analyses to estimate a missing value. 

• Multiple Imputations 

It creates plausible values based on the correlations for the missing data and then averages the simulated datasets by incorporating random errors in your predictions. 

 

8. What is Time Series analysis? 

Data analysts are responsible for analyzing data points collected at different intervals. While answering this question you also need to talk about the correlation between the data evident in time-series data. 

Watch this short video to learn in detail:

 

9. What is the difference between data profiling and data mining?

Profiling data attributes such as data type, frequency, and length, as well as their discrete values and value ranges, can provide valuable information on data attributes. It also assesses source data to understand its structure and quality through data collection and quality checks. 

On the other hand, data mining is a type of analytical process that identifies meaningful trends and relationships in raw data. This is typically done to predict future data. 

 

10. Explain the difference between R-Squared and Adjusted R-Squared.

The most vital difference between adjusted R-squared and R-squared is simply that adjusted R-squared considers and tests different independent variables against the model, and R-squared does not. 

An R-squared value is an important statistic for comparing two variables. However, when examining the relationship between a single stock and the rest of the S&P500, it is important to use adjusted R-squared to determine any discrepancies in correlation. 

 

11. Explain univariate, bivariate, and multivariate analysis.

Bivariate analysis, which is simpler than univariate analysis, is used when the data set only has one variable and does not involve causes or effects.  

Univariate analysis, which is more complicated than bivariate analysis, is used when the data set has two variables and researchers are looking to compare them.  

When the data set has two variables and researchers are investigating similarities between them, multivariate analysis is the right type of statistical approach. 

 

12. How would you go about measuring the business performance of our company, and what information do you think would be most important to consider?

Before appearing for an interview, make sure you study the company thoroughly and gain enough knowledge about it. It will leave an impression on the employer regarding your interest and enthusiasm to work with them. Also, in your answer you talk about the added value you will bring to the company by improving its business performance. 

 

13. What do you think are the three best qualities that great data analysts share?

List down some of the most critical qualities of a Data Analyst. This may include problem-solving, research, and attention to detail. Apart from these qualities, do not forget to mention soft skills, which are necessary to communicate with team members and across the department.    

 

Are you interested in learning more about data science for a boost to your professional career? Join our Data Science Bootcamp and learn all you need to know about the world of data!

Data Science Dojo is offering RStudio for FREE on Azure Marketplace packaged with a pre-installed running version of R alongside other language backends to simplify Data Science. 

 

What is data science? 

 

Data Science is one of the quickest-growing areas of work in the industry. According to Harvard Business Review, it’s regarded as the “sexiest job of the 21st century”. 

Data science joins math and measurements, programming, refined analyses, machine learning and AI to reveal significant knowledge concealed in an association’s dataset. These understandings can be utilized to direct businesses in planning and decision making. The lifecycle of Data Science involves data collection (ingestion), data pre-processing and wrangling, predictive data analysis via machine learning and finally communication of outcomes for future strategies. 

 

Pro Tip: Join our 6-months instructor-led Data Science Bootcamp to master data science. 

 

Challenges faced by developers 

 

Individuals who were learning or pursuing Data Science and Machine Learning through R found it difficult to code and develop models using only a terminal or command line interface. Developers who wanted to perform extensive high powered ML operations but didn’t have enough computation power to do it locally was also another challenge.  

In these circumstances an interactive environment configured with R can help the users in gaining hands-on experience with machine learning, data analysis and other statistical operations. 

Working with RStudio 

 

RStudio is an open-source tool that gives you an effortless coding IDE in the cloud with a pre-installed R programming language to start your data mining and analytics work. It is integrated with a set of modules that make code development, scientific computing, and graphical jobs to be more productive and easier. This tool allows developers to perform a variety of technical tasks such as predictive modeling, clustering, multivariate querying, stock market rate, spam filtering, recommendation systems, malware, and anomaly detection, image recognition, and medical diagnosis. 

 

 

Key attributes 

 

• Provides an in-browser coding environment with syntax suggestions, autocomplete code feature and smart indentation 
• Provides the user with an easy-to-use free coding platform accessible at the local web server, powered by Azure machines 
• Apart from the primary built of R, RStudio has support for other famous interpreters as well such as Python, SQL, HTML, CSS, JS, C, Quarto and a few others 
• In-built debugging functionality by toggling breakpoints to detect and eradicate the issues or fix them quickly 
• As the computations are carried on Microsoft’s cloud servers, there is no memory or performance pressure on the company’s storage devices 
• In order to optimize the workload, the RAM and compute power can be scaled accordingly, thanks to Azure services 

 

What Data Science Dojo has for you 

 

The RStudio instance packaged by Data Science Dojo provides an in-browser coding environment with a running version of R pre-deployed in it, reducing the burden of installation. With an interactive user-friendly GUI-based application, developers can perform Machine Learning tasks with comfort and flexibility.  

• A browser based RStudio environment up and running with R pre-deployed 
• Convenient accessibility and navigation 
• Ability to work with different language scripts simultaneously 

• Rich graphics and interactive environment 
• Support for git and version control 
• Code consoles to run code interactively, with full support for rich output 
• Integrated R documentation and user help 
• Readily available cheat sheets to get started 

Our instance supports the following backends: 

• R 
• Python 
• HTML 
• CSS 

• JavaScript 
• Quarto 
• C 
• SQL 
• Shell 

• Markdown and Header files 

 

Conclusion 

 

RStudio provides customers with an easy-to-use environment to gain hands-on experience with Machine Learning and Data Science. The responsiveness and processing speed are much better than the traditional desktop environment as it uses Microsoft cloud services. It comes with built-in support for git and version control.

Several variants of the R script can be executed in RStudio. It allows users to work on a variety of language backends at the same time with smart observability of variables and values side by side. The documentation and user support are incorporated into the tool to make it easy for developers to code. 

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 RStudio instance dedicated specifically to Machine Learning and Data Science on Azure Marketplace. Now hurry up and avail 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 Rstudio 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.   

In this blog, we will have a look at the list of free Data Science crash courses to help you succeed in Data Science

With more and more people entering the field, data science and data engineering are surely amongst the topmost emerging areas of work in the 21st century. Higher salaries, perks, benefits, and demand have made it a field of interest for 1000s of people.

While a good chunk of students are opting for data science in their undergraduate and graduate programs, there are people who are opting for different Data Science Bootcamps to get started in the field. 

However, enrolling instantly in an expensive undergraduate, master’s, or data science Bootcamp might not be the correct choice for one to go with. An individual would want to explore more within the scope of data science before switching fields or making the final call. Hence, below we present a list of free data science crash courses that an individual can go through before choosing their career path.   

 

 

 

If you are completely new to data science or planning to switch your career, our Data Science Practicum Program should be able to help you. 

Likewise, data science is an emerging field. Just a single program or bootcamp cannot help you to excel within the domain of data science, engineering, and analytics. You will have to keep learning and update your skillsets with short courses like Python for Data Science to remain competitive in the job market. This list of free crash courses can help you acquire a number of skills like Power BI, SQL, MLOps, and many others. 

 

 

Set of Data Science Free Crash Courses

So, if you are the one who is already in a data science career or the one who is planning to make a transition, this set of free data science crash courses can help you all out in every possible way. Check them out:  

  1. SQL Crash Course for Beginners:

 This crash course can help beginners with no previous experience in SQL. By the end of this course, you will understand the difference between SQL and NoSQL, what is a database, the differentiation between MySQL, Oracle, PostgreSQL, SQL Server, and SQLite, how to find data in a database by writing a SQL query, and much more. 

  

 

2. Python Crash Course for Excel Users:

This course can assist all Excel users with no prior knowledge for Python. In this course, you will understand how Python is different from Excel as an open-source software tool, navigation & execution of codes in Jupyter Notebook, implementing useful packages for data analytics, and translating common Excel concepts such as cells, ranges, and tables to Python equivalents. 

 

  

3. Redis Crash Course for Artificial Intelligence and Machine Learning:

If you have no experience with Redis, then this crash course is for you. This course covers the difference between Redis and SQL databases, key machine learning concepts and use cases Redis enables, data types and structures that can be stored in Redis, Redis as an online feature store, and Redis as a vector database for embeddings & neural search. 

  

  

4. MLOps Crash Course for Beginners:

Do you have the basic knowledge of developing machine learning models in a Jupyter notebook setting? Then this course is a perfect fit for you.

We will cover what is MLOps and machine learning pipelines, why is MLOps important, how to create and deploy a fully reproducible MLOps pipeline from scratch, and Learn the basics of continuous training, drift detection, alerts, and model deployment. 

  

 

  5. Crash Course on Naïve Bayes Classification:

Need an introduction to Naïve Bayes Classification? Then this short course will take you through the theory and coding examples. With this course, you should be able to acquire a strong understanding of this technique. 

  

 

 

6. Crash Course in Modern Data Warehousing Using the Snowflake Platform:

With this crash course, you can get started with the new generation of data warehouse i.e. Snowflake. We will discuss Snowflake architecture, its user interface, and the data caching feature of Snowflake. We have also included a lot of instructor-led demos to provide you with a pragmatic experience regarding the Snowflake Platform. 

  

 

7. Crash Course in Data Visualization:

This crash course is planned for intermediate users with previous experience in python. In this session, introduce chart theory, outline data to visual representations, get access to a Google Colab Notebook that you’re able to code your own interactive charts with, transform data to be ingested by pandas and plotly, and customize your chart with options & properties to make it unique for your use case. 

  

   

8. Power BI Crash Course for Beginners:

With this crash course, get started with Microsoft’s Power BI. We will walk you through how to prepare your data, analyze it and build insightful visualizations on the interactive data visualization software Power BI Desktop. By the end of the course, you will know the basics of importing data into Power BI, carrying out exploratory data analysis, cleaning, manipulating, and aggregating data, and building insightful visualizations with Power BI. 

  

 

You can also get an in-depth Introduction to Power BI with our live-instructor-led training. Do check it out.   

 

9. Crash Course on Designing a Dashboard in Tableau:

 This crash course is intended for beginners. In this course, you will know what is Tableau, how to design a basic dashboard in tableau, how to include a bar chart in your dashboard, and how to create a map in tableau.   

  

 

  

10. Crash Course in Predictive Analytics:

The uncertainty after Covid-19 has made it difficult for companies to thrive but data and analytics helped companies survive it. Companies need to work proactively with predictive and prescriptive analytics to optimize their operations and compete in a changing world. This crash course will provide an in-depth overview of predictive analytics.  

  

 

 

11. Crash Course on Transfer Learning:

In this course, we will discuss the idea of transfer learning, learn how deep learning models communicate with each other, explore the real-world applications of transfer learning, and compare transfer learning with a human’s continuous growth model.  

 

 

Need help with your data science career? This Data Science Roadmap can navigate your way.   

 

12. R and Python- the Best of Both Worlds:

One of the common data science arguments has been what language to learn, R and Python. This argument has led to a language rivalry between R and Python. The purpose of this course is to take through the main defining features of both languages and how they compare different workflows in data science and data types.

We will also show what methods are available for combining both in the same workspace and demonstrate this with a case study.  

  

 

Want to learn more about free Data Science crash courses? 

Only a top few popular data science crash courses are listed here, however, these might not be sufficient enough to sustain in such a competitive environment. If you are in a search for more data science crash courses, then make sure to go through this list of free data science courses. 

If you are absolutely new to data science, then I can assure you that  our YouTube channel  can navigate your journey, do check it out! 

 

This blog will learn about “Data Science career growth in 2022”. It is no longer a secret that today’s economy is entirely dependent on analytics and data-driven solutions/decisions.

Businesses, enterprises, and governments have spent the last few years collecting and analyzing massive volumes of data. If you are interested in the field of Data Science enroll in some Data Science courses offered by reputed Institutions which will be an added advantage during your job hunt.

 

 

Data scientists are currently playing a crucial part in the success or failure of any organization, one can even consider choosing a proper Data science certification program that will help learn practically as well as theoretically. Therefore, it is not a stretch to state that “there is a data scientist behind every huge successful company.”

Overview of Data Science Career

Data science is a fascinating, interesting, intriguing, forward-thinking, and lucrative profession. Importantly, unlike other traditional careers, you do not need an established degree or specialized educational background to begin your journey in Data Science.

All you need are the proper abilities, some connected experience, and a curious mind. Considering the need for data scientists in the current market trends indicate that data science course fees are growing.

In this blog, I’ll go over the ins and outs of the data scientist job path, as well as the abilities necessary for data Science. In addition, I’ll guide you on how to choose which data science career is best for you.

Alright!! Let’s dive into the topics.

What is Data Science?

Data science is the study of massive amounts of data using current tools and methodologies to discover previously unknown patterns, extract valuable information, and make business choices.

Data for analysis can come from a wide range of sources and be provided in a variety of ways.

Now that you know what data science is, let’s look at what a Data Scientist will do in 2022.

What Does a Data Scientist Do?

Data science is a highly interdisciplinary field that works with a broad variety of data and, unlike other analytical fields, focuses on the overall perspective.

 

 

In business, the purpose of data science is to give an insight into customers and campaigns, as well as to aid organizations in building effective plans to engage their audiences and sell their products. 

Big data, or enormous amounts of information gathered through different methods such as data mining, necessitates the use of creative thinking on the part of data scientists. So, what exactly does a data scientist do?

Data scientists use forecasting models to evaluate data and information to produce key insights that help enterprises expand their businesses in the right direction. One of the key responsibilities is to analyze large data sets of quantitative and qualitative data.

This personnel is in charge of developing statistical learning models for data analysis and must be knowledgeable with statistical tools. They must also be knowledgeable enough to create complex prediction models.

Is Data Science Right for You?

In my opinion, it is crucial to have an answer to this issue before embarking on your path in data science. Unfortunately, many blogs on the internet indicate that the area of data science is full of demand, great incomes, and respect. 

Nevertheless, the fact is that your journey to data science is not at all easy; it takes continual learning and unlearning of complicated subjects and concepts from different professions, and you must be technically knowledgeable throughout your career.

 

Learn more about Data Science Roadmap 

 

In this section, I’ll provide you with some suggestions that will take you to the answer to this question. Fundamentally, anyone can acquire and practice any data science skill if they are truly committed to it.

Simply said, if you want to learn data science, you can do so.

Why Choose a Career in Data Science?

Data science has been termed the “sexiest job of the twenty-first century.” I’m sure this is a significant role in your decision to pursue a career in data science. Nowadays, any company, large or little, is looking for employees who can interpret and dissect data.

Choosing a profession in data science involves respecting the numerous disciplines on which data science as a subject has been founded, such as statistics, math, and technology, among others. The variety of abilities required to become a data scientist might be considered an advantage.

Now, let me direct your attention to a few key reasons why you should pursue a career in data science;

• High prestige
• Be part of future
• Excellent pay
• Constant challenging work or NO boring work
• Exceptional growth & demand in the market
• Endless career opportunities

Data Science has shown the ability to transform companies and our society. It has become a lucrative job due to a limited supply of trained workers in Data Science and high demand.

Job Statistics in Data Science Career

If you’re here, I’m presuming you’ve picked or are thinking about choosing a career path. Let me direct your attention to a few more key criteria that might assist you in making your final decision.

• 650% Job growth since 2015 (Via: Linkedin)
• By 2026, 11.5 million additional jobs are expected to be created (source: U.S. Bureau of Labor Statistics)
• A data scientist earns an average annual income of $120,931. (source: Glassdoor)
• In 2020, there are expected to be 2.7 million available positions in data analysis, data science, and related fields (source: IBM).
• By 2020, there will be a 39% increase in employer demand for both data scientists and data engineers (source IBM).
• 59% of employment will be in finance, information technology (IT), insurance, and professional services. This is divided as follows: 
• 19% in banking and insurance, 18% in professional services, and 17% in information technology.
• Bachelor’s degree holders will be able to apply for 61% of data scientist and advanced analytic roles, while 39% will require a master’s or Ph.D.
• Positions in data science and data analysis are available for 5 days longer than the average for all jobs, indicating that there is less competition in these professional sectors and recruiters must work harder to locate competent individuals.
• A possible annual salary of $8,736 more than any other bachelor’s degree position (source: IBM).

 

Pro-Tip: Build up your Data Science career as a licensed Data Scientist

 

The data presented above indicates the development and need for data science specialists across various business areas, geographical regions, and even experience levels. As more businesses implement data-driven solutions, the need for data scientists will continue to rise.

So, relax, you’re on the correct track!

Are you ready to become a Data Scientist?

Data science is the most in-demand career this decade and will continue to be so in the future. With increased awareness of the industry, competition for positions among professionals is at an all-time high. If you follow this approach and do an honest self-evaluation, I am confident you will make the best decision for you.

 

Enroll in Data Science Bootcamp today to begin your Data Science career

 

Remember that selecting the proper career path is only the beginning of your journey.

 

Written by Dhannush Subramani

Artificial Intelligence and Data Science applications and technologies have penetrated our society so deeply that they are now being used in every industry let alone the eCommerce industry.

In some cases, the usage of AI and Data Science are so seamlessly integrated into the picture that you might not even be noticing them. Without further ado, here are the seven interesting applications of Data Science in the e-commerce industry. 

#1 Recommendation systems 

The first example of Data Science being used in e-commerce is that of recommendation systems. It is quite obvious that these systems largely rely on data to make their recommendations, so Data Science pretty much lies at the foundation of the recommendation systems used in e-commerce. 

Every time a customer makes a purchase (or even simply checks out a product page), their activities are recorded and then used by the system to make personalized recommendations. This way, businesses can sell more products. Such systems pretty much offer exactly the kinds of products specific customers are interested in. 

Likewise, the data collected and analyzed by recommendation systems can be used by your marketers and customer service managers to create special offers for individual customers. You can then send these offers by email, SMS, etc. to directly reach the customers and increase the chances of them making a purchase. 

Learn in detail about data-driven marketing for better ROI

#2 Predictive customer segmentation 

Another popular usage of Data Science in e-commerce is that of predictive customer segmentation. Every e-commerce store has its own target audience, but to work with this audience most effectively and efficiently, you need to segment it and target each segment separately.

In most cases, this segmentation is done manually (or to a large extent manually). However, when you are using predictive customer segmentation, the system helps you segment your target audience. By gathering data and using AI technologies, you can predict customer interest in your offer and identify different groups of customers accordingly. 

Moreover, with the help of predictive customer segmentation, you can also identify the types of users who likely won’t become your customers. You can then exclude them from your target audience and avoid wasting part of your budget in vain. Essentially, you will be making smarter decisions in terms of targeting and segmentation. 

#3 Pricing optimization 

Pricing optimization is one more way of using Data Science in e-commerce. There are so many factors that are being considered when deciding the price of a specific product. From the cost of materials to the quality of the product to its competitive edge when compared to alternatives – all of these need to be taken into account when pricing it. 

Pricing optimization solves this issue for you because the system will consider the demand for your product when setting its price. Similarly, it will consider the supply (i.e. the number of items available) when displaying the price. This way, you can sell your products at a higher price when you know your customers are willing to pay more. 

#4 AI chatbots and assistants 

AI chatbots and virtual assistants have been using AI and Data Science for what feels like ages now even though truly smart chatbots are relatively new. Such chatbots and assistants can help your customers by providing them with a more engaging and enjoyable buying process and improving their overall experience. 

For example, when a customer has questions about the products, they don’t need to send an email and wait for a response or contact the call center and wait for someone to pick up the phone. All they should do is send a text to the chatbot on your website and get an instant answer to their question or concern. 

 

Of course, AI chatbots are still limited, but they are already quite advanced in what they can do. A lot of chatbots use past customer data to give them suggestions, guide them in their choices, answer their questions, and so on. As this technology continues developing, chatbots will likely become even more common and helpful. 

Read more about how you can improve customer service using data science

#5 Inventory management 

While this is not something you were likely thinking of when you were considering Data Science, inventory management is still an aspect of e-commerce where Data Science is extremely helpful. This is because managing your inventory efficiently takes more than simple manual management, and e-commerce support services can help you with that.

#6 Customer sentiment analysis 

Just like target audience segmentation can be made easier with the help of AI and Data Science, so can be customer sentiment analysis.

To put it simply, customer sentiment analysis is about analyzing the conversations online between your current and potential customers to determine what their opinions about and experiences with your brand are. 

Customer sentiment analysis is most commonly performed on social media platforms where conversations are abundant, but you can also perform it on forums and even by analyzing media outlets like services (though in this case, it will be non-customer sentiment analysis).

Once you have performed the analysis, you can make smarter decisions about your product design, marketing, customer service, and so much more.

#7 Lifetime value prediction 

Last but not least, Data Science is also being used in e-commerce for predicting the lifetime value of customers. Essentially, the customer lifetime value is the total value of the profit you get from a specific customer over your entire relationship with that customer. 

Of course, making such predictions accurately is extremely difficult, but it isn’t completely impossible. Different systems and algorithms are used to collect and analyze a lot of data about your customers and then make predictions about their lifetime value. Then, you can make further decisions based on these predictions about your customers. 

AI and Data Science applications revolutionizing the eCommerce industry

At the end of the day, the way the e-commerce industry operates will likely continue to change soon. And even the way AI and Data Science applications are being used in e-commerce will eventually evolve. For now, it’s worth using these two technologies to their fullest and reaping the benefits they provide to online store owners.

 

 

Written by Lafond Wanda

In this blog, we will learn the proven successful data science tips to experience exponential growth as a data scientist. There are a few key things that aspiring data scientists should keep in mind if they want to be successful in the field. Let’s learn each tip in detail:

 

1. Learn competitive skills through competitions

Participating in data science competitions is a great way to test your skills and learn from your peers. These competitions will also give you the chance to work on real-world datasets and solve complex problems.  Learn competitive skills through hackathons and Kaggle competitions. Sometimes Kaggle competitions can feel lonely so go to hackathons and build alongside other people to broaden your ideas and get better feedback.

On Kaggle you can learn from some of the best data scientists in the world and participate in interesting competitions with novel datasets to truly build your knowledge and data science expertise. Observable is another free, community-supported place where you can learn a great deal about all things related to data exploration. 

 

2. Develop an understanding of business goals

Data scientists have to be well organized, know statistics, and understand how data work connects to a business objective, not just how to code a model. There’s a popular saying that 85% of modeling projects fail and to beat the odds you have to understand how to connect your model with existing business goals and processes. Usually, this comes with experience and the ability to find creative solutions. 

  

3. Stay calm to tackle the complex data

Expect things to be messy. The data is hardly ever exactly what you need, it can live in many places, and is almost always messier than you thought it would be. It can be hard to estimate how long a project or model will take to build, but I found if you plan and give yourself a one or two-day buffer you’ll find better success with communicating and meeting deadlines. – Ayodele Odubela, Data Scientist, Observable 

 

4. Don’t neglect the basics

It is important to have a strong foundation in mathematics and statistics. This will give you the ability to understand and work with complex data sets. Additionally, it will also allow you to develop sophisticated models and algorithms.

 

5. Choosing the right model

Don’t get too caught up in modeling methods. So many data scientists are constantly worried about choosing the right model, when sometimes a model isn’t needed at all. Sometimes a rules-based system is more applicable, and sometimes a dashboard is the better deliverable for a project. 

 

6. Collaborate with your team

Get more comfortable collaborating with your team. You can optimize your tools so you can cooperate with the least amount of friction. Data scientists often do work for many parts of the business, so reach out to your colleagues to gain better context around the data and how the models you build may be used.  

 

7. Stay up to date with the latest technology

 The field of data science is constantly evolving, with new tools and techniques being developed all the time. As a result, it is important to keep up-to-date with the latest technology. This will ensure that you are able to use the best tools available to solve complex problems.  

 

8. Be creative

Data scientists need to be creative in order to find new ways to solve problems. This means thinking outside of the box and coming up with innovative solutions. Additionally, it is also important to be able to communicate your ideas effectively so that others can understand them.   

 

9. Learn data science through Bootcamps

Bootcamps are another great option for learning data science. These intensive programs will give you the opportunity to learn from experienced data scientists and work on real-world projects.  

 

10. Attend conferences and workshops

Attending conferences and workshops to network with other data scientists and stay up to date with the latest trends in the field. This is also a great opportunity to learn new skills and techniques.   

 

11. Develop strong technical skills

 As a data scientist, you will need to have strong technical skills. This includes expertise in programming languages such as Python and R, as well as experience working with databases and big data platforms. Additionally, you should also be familiar with machine learning algorithms and statistical modeling techniques.   

Technical skills are usually obvious and include core skills such as statistics, programming, mathematics, and data visualization. However, the non-technical skills are equally important if not more so. Chief amongst these is communication skills. If you can’t communicate your findings to the right audience, at the right time, in the right way then it doesn’t matter how good your technical analysis is. 

 

12. Possess business acumen

In addition to technical skills, it is also important to have business acumen. This will allow you to understand the needs of the business and find ways to use data to solve problems. Additionally, being able to effectively communicate with non-technical stakeholders is crucial for success in this role.  

 

13. Be able to use critical thinking

Data scientists need to be able to think critically in order to identify patterns and insights in data. This includes being able to ask the right questions and identify assumptions that need to be tested. Additionally, being able to think creatively is also important for coming up with innovative solutions.   Boris Jabes, Census

 

14. Develop a growth mindset

Developing a growth mindset helps you not to avoid failure and to instead view it as an opportunity to grow. Further, it lets you develop a self-belief that you can learn anything.; fully embrace trying new things, ideas, tools, and techniques; see feedback as a gift that will move you forward and finally to be inspired by the success of others. These attitudes will make an enormous difference to your future success as a data scientist. 

 

 15. Adopt a problem-solving approach 

A data scientist’s job is to solve business problems through data, AI, and ML tools. Data science is problem driven. That means a data scientists need to immerse themselves in learning what the business does and how the business works. Otherwise, the data scientist’s work just because a science experiment in a vacuum. 

  

16. Improve your interpersonal skills

To get anything done, data scientists need access to data. To secure access to data, they need to learn who to ask and how to ask for data. Downloading a dataset from Kaggle is easy. Figuring out who has the previous five years of company sales data, and how to request that data is an underappreciated skill. 

  

17. Evaluate technology on a periodic basis

Never put all your eggs in one tool, one platform, or one framework. Expect technology to change and learn how to adapt to new tools. At the same time, don’t just adopt new tools for the sake of having the latest toys. Do your due diligence and evaluate technology vendors on a periodic basis, to learn which tools are likely to become the next standard, and which are likely to remain niche products. – David, Coda Strategy 

  

18. Prove to be the right fit for the job

 The hiring agents are not only looking for someone having knowledge of data science but someone who is tailor-fitted for the job and one who will produce actual numbers that will be valuable for the company, like sales conversion data, audience engagement data, etc. 

If you look at the US, for example, there’s a need right now for more than 150,000 data scientists. And this need will just grow as we move towards more digital transformation. Aside from the U.S., there’s also a global shortage of data science skills and professionals in Europe and Asia.

It’s also interesting to cite research showing that 94 percent of data scientists and graduates have gotten jobs since 2011. Ninety-four percent is quite encouraging and if you are skilled in data science, you can feel amazingly comfortable that moving in this direction would offer amazing employment potential. This indicates how reliable a career option in data science is now essential moving into the future. 

  

19. Be curious to learn more

Lastly, an intuitive mind and someone with curiosity is what are essential in a data science job. In enormous data sets, valuable data insights are not always obvious, and a trained data scientist needs to have intuition and understand when to go beneath the surface for insightful information. One of the most important soft skills of a data scientist is the ability to ask questions on a regular basis. You can follow all of the processes of the machine learning project lifecycle if you are bored, but you will not be able to attain the final objective and justify your results.

For me, data science is still growing and evolving which means learning in this discipline never ends. One day you master these new tools and have learned a new skill set, and the following day it is run over by a more complex tool and a thirst for another important skill set. So, a data scientist must be inquisitive and always learn to adapt to these rapid changes. Victoria, MediaPeanut

  

20. Know the role you want

There are quite a few distinct roles within data science that are all quite different. Before you enter the career, it can be worth knowing which roles you prefer, and which suits your interests. Talk to people in the industry and ask them about what role they do and who they work with, whether you want to be a data architect or visualization expert you need to know the role suits you. Once you know your role you can fine tune what you need to know and learn to have success in the role. 

  

21. Consider taking a course

Even if you know a lot about data science already, taking a course can help you understand the necessary tools and techniques you need to implement in a specific role. Moreover, many of these courses are work-oriented, as far as they teach you with a career in mind rather than just teaching generic data skills.

 

 

21. Build a portfolio

One of the important things to do is practice data analysis and science. Yet rather than just letting go of each project, try to optimize each project to show off your skills. Find a secure place to keep all your projects as your data science portfolio, once you are accepted for an interview you can demonstrate actionable skills for the prospective employer. – Peter, Lantech 

  

22. Work on real-world data science projects

In addition to competitions, another wonderful way to get hands-on experience is by working on real-world data science projects. There are many online repositories (such as GitHub) that contain datasets that you can use to practice your data wrangling, modeling, and visualization skills. Working on projects is also a great way to build your portfolio, which will come in handy when you’re ready to start applying for jobs. – Luke, Ever Wallpaper 

 

23. Obtain the confidence of your peers

As we move about, we assist various teams. We understand that a lot of managers don’t even believe their data. However, they demand brand-new monitors, data science teams, and everything else. But what’s the point? If your data isn’t even reliable. Sherlock Holmes said one of our favorite things:

“Data is the basis for the basic building block of reasoning.”

If such is the case and you have doubts about your home, it will hit you when it drops. Get your superiors to believe in your data and you! 

  

24. Implement a straightforward project with success first

We understand that everyone wants to create the next algorithm for Google or Facebook. Why not? They are hip, incredibly strong, and generate billions of dollars annually. However, if you want your team to flourish and they are just getting started, start small. Don’t worry; even a basic task can offer your executives incomparable value if done correctly. once you’ve achieved your first victory.

The executives will ask you to assist them with everything. You will then need to put in some effort to ensure that either only the proper projects are all being worked on, or that your projects are constantly inundated with requests. 

  

25. Explain the importance of your project

Being a salesman is one technique to garner support from executives. How? Explain the need for the project and create it. Considering how new data science is, many executives are unsure of its benefits and applications. Let them see! That is what you do! Show them how they can employ data science to save time, money, and other resources. – William Drow, Starlinkhow 

 

26. Always give details while requesting assistance

You should always be honest and direct when asking for help, whether it be information, an introduction, or a suggestion. Be direct in your request. People are more willing to help, if you ask them for a modest favor that is not too tough to give. A specific request that is within my sphere of influence makes me far more inclined to say yes when individuals seek my assistance in studying data science.

 

27. It’s important to follow your passions

For many personal and professional reasons, you may be considering a data science career. If, on the other hand, you’re thinking about the financial and social benefits, you should reconsider. Even if the pay and status are decent, working in this field may become challenging if you don’t enjoy it.

Data science initiatives are like any other form of experiment in that not everything turns out perfectly. You also have responsibilities to the company’s shareholders. It’s possible that you won’t always get to work on the kinds of issues that fascinate or excite you. Instead, you’ll probably have to solve issues that benefit your company. – Adam Crossling, Zenzero 

 

Do you have any more successful Data Science tips? Share in the comments 

Data science is a challenging but rewarding field, and I hope these tips have helped you get started on your journey. Remember to keep learning and practicing, and you’ll be well on your way to having a successful career in data science!