Mathematics For Data Science 101

Understand the Math , Don't Solve the Equation

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.

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 LearnCore 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 DifferentInfographics-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 ForBeginners 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.

Overview

Section 1: Linear Algebra

Lecture 1 Introduction to Linear Algebra

Lecture 2 System of Linear Equations

Lecture 3 Solving Linear Systems

Section 2: Calculus

Lecture 4 Function - Mapping Machine

Lecture 5 A Tangent Problem - Crux of Calculus

Lecture 6 Derivative

Lecture 7 Rules of Differentiation

Lecture 8 Chain Rule

Lecture 9 Critical Points

Lecture 10 Minima and Maxima

Lecture 11 Derivative Test

Lecture 12 Extreme Value Theorem

Lecture 13 Inflection Points

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.