Mathematics for Data Science

  • Course level: Beginner


Mathematics for Data Science

  • To introduce the mathematical principles required to understand concepts in Data Science
  • To build the intuition behind the working of Machine Learning Algorithms

What this course will offer

Mathematics forms the basis of almost all the Machine Learning algorithms. Without math, there is no Machine Learning. Machine Learning uses mathematical implementation of the algorithms and without understanding the math behind it is like driving a car without knowing what kind of engine powers it.
You may have studied all these math topics during school or universities and may want to freshen it up. However, many of these topics, you may have studied in a different context without understanding why you were learning them. They may not have been taught intuitively or though you may know majority of the topics, you cannot correlate them with Machine Learning.
This course on Mathematics for Data Science, aims to bridge that gap. By taking this course, students will get up to speed in the mathematics required for Machine Learning and Data Science. The course will go through all the relevant concepts in detail, derive various formulas and equations intuitively.

View full course outline here

What Will I Learn?

  • Pre-requisite course for Data Science

Topics for this course

7 Lessons

Module 1 – Linear Algebra and Statistics

Introduction to Linear Algebra14:13
Advanced Concepts in Linear Algebra15:02
Basic Concepts in Statistics24:13

Module 2 – Probability and Information Theory

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  • Basic coding experience in any programming language
  • Some exposure to Python and its libraries would help

Target Audience

  • People looking for a career shift in data science
  • Students wanting to advance their skills in data science