YouTube Video Review: Statistics for Data Science -Probability Distribution with Dr Sarkar

12 min readAug 13, 2022

Since I have never taken one proper statistics course whilst undertaking formal education, I have been watching lectures on YouTube that focus on statistics in an attempt to rectify that deficiency in my education. I have therefore been watching lectures given by Dr Sarkar of GreatLearning and have found his lectures to be quite informative; basically the same as if I had travelled to India and sat in on one of his classes in person.

One video I watched that has taught entirely by Dr Sarkar was Statistics for Data Science — Probability and Statistics, and I intend to review the video in this blog post:-

Statistics forms the basis of all data science concepts, which is a good reason for any person endeavouring to embark upon the field of data science to become proficient in this subject. Statistics is easier from a computational perspective.

A professional statistician struggles between doing the right thing badly or the wrong thing well.

There are three types of statistics, being:-

  1. Descriptive — isolate the problem
  2. Prescriptive — look at the data and give an idea of what might happen
  3. Prescriptive — an action that is designed to do something

What is the difference between data science and machine learning?

  1. In statistics, it is necessary to formulate a problem and get statistics to solve the problem. The question is cheap and the data is expensive. The statistician is paid to collect the data.
  2. In machine learning, data is cheap and questions are expensive. The data scientist is paid for asking the right questions.

Fuzzy logic is an approach to variable processing that allows for multiple possible truth values to be processed through the same variable. Fuzzy logic attempts to solve problems with an open, imprecise spectrum of data and heuristics that makes it possible to obtain an array of accurate conclusions.

In descriptive analytics, the statistician tries to understand certain things about data to come to a conclusion.


I have close to five decades experience in the world of work, being in fast food, the military, business, non-profits, and the healthcare sector.