Keeping ahead of the latest developments in a field is key to advancing your skills and your career. Five foundational ideas from recent data science papers are highlighted here with tips on how to leverage these advancements in your work, and keep you on top of the machine learning game.

Features selection is a very important part of machine learning development, It allows you to keep your models as simple as possible keeping at the same time, as much information as possible.

Hypothesis testing is a type of statistical method which is used in making statistical decisions using experimental data. As we have already seen in Inferential Statistics and Central Limit Theorem(CLT), we will work with sample data and confirm our assumption about the population in Hypothesis Testing.

P-Value for the Non-Statistician: Here is a summary of how I was taught to assess the p-value in hopes of helping some other non-statistician out there.

Stop using arbitrary statistical significance cut-offs. How many of you use p=0.05 as an absolute cut off? p ≥ 0.05 means not significant. No evidence. Nada. And then p < 0.05 great it’s significant.

Hypothesis tests are significant for evaluating answers to questions concerning samples of data.

The other day I was reading this book and ended up into a small section trying to explain “Why do we use .05?”. All of a sudden I figured, “HA!” not many people know this for sure [including me before reading] as the story is both ambiguous and quite funny. [This text heavily relies on the explanations from the book mentioned above.]

Why research studies lose credibility, and how you can build yours by asking these simple questions

Theory and intuition behind logistic regressions and implementing that using Python code. This is a part of a series of blogs where I’ll be demonstrating different aspects.

Statistics play an important role in our lives. It eventually becomes an imperative fundamental of any data scientist. Today we are about…

Understand what exactly is P-value and how is it related to the null hypothesis. When we start studying the concepts of probability and statistics.

How to understand if the difference really matters.

Before answering what is hypothesis testing let’s answer why hypothesis testing!