Sampling and Estimation - Overview

Welcome to this session on ‘Sampling and Estimation’. In the previous session, you learnt about probability and explored some probability distributions. This session will introduce one of the most important theorems in statistics, the central limit theorem. This session also acts as a gateway to more complex topics in Inferential Statistics. In inferential statistics, you focus on a sample of the data set and use it to speculate the properties of the entire population.

In this session

You will learn how to approach large populations that cannot be realistically studied in their entirety and focus on the following topics:

1. Sampling
2. Central Limit Theorem
3. Confidence Intervals

People you will hear from in this session

Thomas Dougherty

Xpert, upGrad 

Thomas has a long association with the field of data science. He has experience of working with data and has taught data science. He is currently an Executive in Residence at a well-known university in Rhode Island, USA. Prior to working as an Executive in Residence, Thomas has led data science teams across many industries, including the travel, telecom, automotive, consulting and investment industries, to name a few.

The transcript of this session is provided below for your reference.

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