Howell, Egor. “Chi-Square Distribution Simply Explained”. 2022. *Medium*. https://towardsdatascience.com/chi-square-distribution-simply-explained-87f707ba631a.

In this post, we will explain a special case of the Gamma Distribution, the Chi-Square Distribution. This distribution is ubiquitous in Statistics and even has its own test which is used frequently in Data Science, the Chi-Square Test. We will go through the origins of the distribution by deriving its **Probability Density Function (PDF) **and show how it relates to the Gamma Distribution.

Howell, Egor. “Chi-Square Goodness Of Fit Test”. 2022. *Medium*. https://towardsdatascience.com/chi-square-goodness-of-fit-test-7774d3410896.

In this post we will have a discussion about one of the Chi-Square Tests, the goodness of fit test. This test is used to **verify if our distribution of sample data is inline with some expected distribution of that data**. In other-words, it determines whether the difference between the sample and expected distribution is by random chance or if it is statistically significant. In this article we will dive through the maths behind the goodness of fit test and walk through an example problem to gain our intuition!

Rathi, Ankit. “Mathematics For Data Science”. 2021. *Medium*. https://rathi-ankit.medium.com/mathematics-for-data-science-d95a255f1dec.

**Mathematics for Data Science **

1. Context & Introduction

2. Linear Algebra for Data Science

3. Multivariate Calculus for Data Science

4. Probability & Statistics for Data Science **Linear Algebra for Data Science **

1. What is Linear Algebra?

2. Why Linear Algebra is important in Data Science?

3. How Linear Algebra is applied in Data Science? **Multivariate Calculus for Data Science **

1. What is Multivariate Calculus?

2. Why Multivariate Calculus is important in Data Science?

3. How Multivariate Calculus is applied in Data Science? **Probability and Statistics for Data Science **

1. Probability

2. Descriptive Statistics

3. Inferential Statistics

4. Bayesian Statistics

5. Statistical Learning

Rathi, Ankit. “5 Data Science Use Cases For Every Business”. 2020. *Medium*. https://rathi-ankit.medium.com/5-data-science-use-cases-for-every-business-8eb4bce12abe.

Every organization, every business is trying to make the most of available data, to get a competitive advantage. But you don’t get data science projects out of anywhere. You need to identify and validate data science use cases, before getting into typical data science lifecycle. So identifying and validating the data science use cases is a task in itself. Today, I would like to talk about 5 common areas to look for data science use cases, which are relevant to any business.

Sharma, Atul. “Mean, Median & Mode — Which Central Tendency Measure To Use & When?”. 2020. *Medium*. https://towardsdatascience.com/mean-median-mode-which-central-tendency-measure-to-use-when-9fb3ebbe3006.

To represent a dataset as a 1-number summary, we use central tendency measure. There exist three central tendency measures i.e. Mean, Median & Mode. Why was there a need for these three measures when only one (Mean) could have done the job? This is what this blog is all about, as this blog ends you will be able to answer the notorious question — Which one to choose & when? Since each one of them has its own pros and cons, the same will be elaborated to establish conceptual clarity.

theDataStrategist. “4 Statistical Processes That Every Data Scientist Should Know”. 2019. *Medium*. https://thedatastrategist.medium.com/4-statistical-processes-that-every-analyst-should-know-5fdf1a23d7e2.

The depth and variety of skills that fit under the analytics umbrella are extensive. Different roles — such as strategic analysts, digital analysts, data scientists, data engineers — require distinct skillsets and varying levels of technical expertise. However, a handful of statistical processes are so common that every analyst should be acquainted with them. Further, it’s beneficial to know how to code these in at least one programming language (or if not, in Excel). Below, are 4 of the most common and versatile statistical methods used in business, along with examples and educational sources.