Uncovering the strategy I used in writing blogs that got me more than 50K views and consistent engagement over a year — If you are about to start with technical blog in the field of Data Science that would give you more than 50K views, then this blog is for you. In this blog I will uncover the strategies I used in my first year of writing technical blogs in Data Science…

End-to-end A/B testing for your Data Science experiments for non-technical and technical specialists with examples and Python implementation — A/B testing also called split testing, originated from the randomized control trials in Statistics, is one of the most popular ways for Businesses to test new UX features, new versions of a product, or an algorithm to decide whether your business should launch that new product/feature or not.

Key statistical concepts for your data science or data analysis journey — As Karl Pearson, a British mathematician has once stated, Statistics is the grammar of science and this holds especially for Computer and Information Sciences, Physical Science, and Biological Science. …

How to make yourself irreplaceable as a Data Scientist during recession and hiring freezes — “It’s a recession when your neighbor loses his job; it’s a depression when you lose your own.” ― Harry S. Truman This article is about making yourself valuable member of your company and its Data Science community, such that no matter economic shake downs you stay confident about your career…

Free Data Science Resources for aspiring Data Scientists: Theory, Programming, Portfolio Project Examples and Interview Preparation — If you an aspiring Data Scientist looking for specific resources in the form of blogs, videos, complete portfolio project examples and interview preparation tools, then this article is for you. Here I will share my most favourite resources, that will help you to enter the field smoothly!

Common issues in Data Analytics and Data Science Projects you should be aware of, how to check and how to fix them — Common Data and Model issues you should be aware of and check when working with data and when training Machine Learning or Deep Learning models. Expect problems and eat them for breakfast. Alfred A. Montapert In this article we will talk about the most common data problems you should know…

Everything you need to know about the simplest yet the most popular Machine Learning regression model — Correlation is not causation. — Kenneth L. Woodward The first thing you can read in every statistics book is that correlation is not causation. However, it’s also the first thing many students forget once they see their data and start to look for insights about it. Linear Regression is one…

Must know statistical tests for testing statistical significance of features in Regression Model: One-sided & Two-sided T-test, F-test, 2 Sample Z-test, 2 Sample T-test, Chi-Squared test — “Facts are stubborn things, but statistics are pliable.” ― Mark Twain Trying to determine whether a variable or set of variables had a real impact on another, dependent, variable is one of the most common reasons for using regression models such as Linear Regression.

End-to-end Data Science Case Study with Tips and Python Implementation for Real Life Business Problem: What Makes Spotify Playlist Successful — If you are preparing for your Data Science or Data Consultant Case Study interview then this article is for you. This end-to-end Data Science Case Study with its Python implementation, asked during the final stage of recruitment processes will help you to get an understanding where to start and what…