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Handling Data: Beginner’s Guide to Data Analytics

Data analytics is one of the fastest growing industries in the world and is driving the performances of companies worldwide. Join this course to learn more about it.

02h:59m
Lifetime access
1152 learners

About the topic:

Data Analytics is the process of inspecting, cleaning, transforming and modeling data in order to discover information, suggest conclusions and make effective decisions. It is the science of examining raw data with the purpose of drawing conclusions from that information. Today, Data Analytics is used in almost every industry to allow companies to make better business decisions. It is important to almost all businesses. In fact, no business can survive without analyzing the available data.

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Course Objectives:

A basic knowledge of Probability and Statistics is essential for analyzing data and drawing conclusions. This course introduces you to the basic concepts of probability and statistics that are important in the field of data analytics. It will also help you to get acquainted with the basic concepts of data handling and pave the foundation for a bright career in the field of data sciences.

Target Audience:

There is a huge surge in the demand for data analysts. This course has been designed to meet the needs of:

  • Business analysts who would like to brush up their basics of probability and statistics.
  • Graduates who wish to do their higher studies in the field of business analytics.
  • Anybody who wishes to learn more about probability distributions.
Course Plan

1. Descriptive Statistics
3 videos
Measures of Central Tendency 25:50

Measures of Dispersion 23:07
2. Random Variables and Introduction to Probablity Distribution
1 video
Random Variables and Introduction to Probablity Distribution 23:41
3. Probablity Distributions
4 videos
Part- 1 23:47

Part- 2 19:26

Part- 3 12:31

Part- 4 21:54

Meet the Author


Dr. Nandan Sudarsanam
Assistant Professor, Department of management studies, IIT Madras.
Prof. Nandan Sudarsanam holds a Ph. D. in Engineering Systems from Massachusetts Institute of Technology (MIT). His research interests and work experience span the areas of Data mining/Machine learning, Experimentation, Applied Statistics and Algorithmic approaches to problem solving.
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