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Mathematics for Data Science 101
Rating: 2.5 out of 5(1 rating)
19 students

Mathematics for Data Science 101

Understand the Math , Don't Solve the Equation
Created byHaris Jafri
Last updated 1/2026
English

What you'll learn

  • Understand the core mathematical concepts required for Data Science.
  • Learn Statistics fundamentals — mean, median, mode, variance, and standard deviation.
  • Master Probability basics and how they apply to Machine Learning models.
  • Grasp Linear Algebra essentials — vectors, matrices, and transformations.
  • Learn Calculus basics for optimization in AI and ML algorithms.

Course content

4 sections • 76 lectures • 14h 19m total length

  • Introduction to Linear Algebra10:32
  • System of Linear Equations2:33
  • Solving Linear Systems5:34
  • System of Linear Equations - Practical Example11:10
  • Vectors6:45
  • Basic Operations on Vectors8:27
  • Polar Coordinate System6:50
  • Unit Vector2:35
  • Dot Product12:21
  • Linear Combinations11:56
  • Basis Vectors2:39
  • Vector Equation4:35
  • Vector Span4:53
  • Linear Independence7:10
  • Matrix Origins3:11
  • Types of Matrix9:08
  • Rank of a Matrix6:31
  • Echelon Form of a Matrix16:42
  • Matrix Operations and Properties11:54
  • Vector Space4:43
  • Concept of Linear Transformation10:38
  • Linear Transformation Function31:34
  • Common Linear Transformations8:50
  • Determinants6:35
  • Multiplicative Inverse13:51
  • Null Space5:30
  • Eigen Values and Eigen Vectors15:58
  • Eigen Decomposition6:47
  • Singular Value Decomposition8:19

Requirements

  • No Prerequisites

Description

Are you struggling with the mathematics needed for Data Science?
Do complex formulas and long equations make you lose interest?

Welcome to "Mathematics for Data Science 101" — the easiest way to master Data Science math, visually.

In this course, we break down essential mathematics concepts into simple, colorful infographics and explain them step-by-step so you can learn without the stress of heavy theory.

Whether you’re a complete beginner or someone brushing up on your math for Machine Learning, this course will guide you through:

What You’ll Learn

  • Core Arithmetic, Algebra, and Probability concepts used in Data Science.

  • Statistics fundamentals: mean, median, variance, standard deviation.

  • Linear Algebra basics: vectors, matrices, transformations.

  • Calculus essentials for optimization in Machine Learning.

  • Probability & Distributions with real-world examples.

  • How these math concepts directly apply to Data Science and Machine Learning models.

Why This Course is Different

  • Infographics-first approach → Concepts are explained visually for faster understanding.

  • Practical focus → See exactly how math is used in Data Science tasks.

  • Beginner-friendly structure → No advanced math background required.

Who This Course is For

  • Beginners in Data Science who struggle with math.

  • Students preparing for Machine Learning, AI, or Data Analytics careers.

  • Professionals transitioning into Data Science who need a math refresher.

By the end of this course, you’ll not only understand the mathematics behind Data Science but also feel confident applying it in real projects.

Who this course is for:

  • Beginners in Data Science who find mathematics challenging.
  • Students preparing for Machine Learning, AI, or Data Analytics careers.
  • Professionals transitioning into Data Science who need a math refresher.
  • Self-learners who want to visualize math concepts instead of memorizing formulas.

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