A-Z Maths For Data Science

Learn about Linear Algebra, Probability, Statistics and more through solved examples and intuition.

What you'll learn

Basics of Linear Algebra - What is a point, Line, Distance of a point from a line

What is a Vector and Vector Operations

What is a Matrix and Matrix Operations

Visualizing data, including bar graphs, pie charts, histograms

Data distributions, including mean, variance, and standard deviation, and normal distributions and z-scores

Analyzing data, including mean, median, and mode, plus range and IQR and box plots

Data Distributions like Normal and Chi Square

Probability, including union vs. intersection and independent and dependent events and Bayes' theorem

Permutation with examples

Combination with examples

Central Limit Theorem

Hypothesis Testing

Requirements

Foundational Mathematics

Description

A-Z MATHS FOR DATA SCIENCE IS SET UP TO MAKE LEARNING FUN AND EASYThis 100+ lesson course includes 24+ hours of high-quality video and text explanations of everything from Linear Algebra, Probability, Statistics, Permutation and Combination. Topic is organized into the following sections:Linear Algebra - Understanding what is a point and equation of a line. What is a Vector and Vector operationsWhat is a Matrix and Matrix operationsData Type - Random variable, discrete, continuous, categorical, numerical, nominal, ordinal, qualitative and quantitative data typesVisualizing data, including bar graphs, pie charts, histograms, and box plotsAnalyzing data, including mean, median, and mode, IQR and box-and-whisker plotsData distributions, including standard deviation, variance, coefficient of variation, Covariance and Normal distributions and z-scores.Different types of distributions - Uniform, Log Normal, Pareto, Normal, Binomial, BernoulliChi Square distribution and Goodness of FitCentral Limit TheoremHypothesis TestingProbability, including union vs. intersection and independent and dependent events and Bayes' theorem, Total Law of ProbabilityHypothesis testing, including inferential statistics, significance levels, test statistics, and p-values.Permutation with examplesCombination with examplesExpected ValueAND HERE'S WHAT YOU GET INSIDE OF EVERY SECTION:We will start with basics and understand the intuition behind each topic.Video lecture explaining the concept with many real-life examples so that the concept is drilled in.Walkthrough of worked out examples to see different ways of asking question and solving them.Logically connected concepts which slowly builds up. Enroll today! Can't wait to see you guys on the other side and go through this carefully crafted course which will be fun and easy.YOU'LL ALSO GET:Lifetime access to the courseFriendly support in the Q&A sectionUdemy Certificate of Completion available for download30-day money back guarantee

Overview

Lecture 1 Introduction

Section 1: Linear Algebra

Lecture 2 Inverse of a matrix

Lecture 3 Preface for Dimensionality Reduction - Part 1

Lecture 4 Preface for Dimensionality Reduction - Part 2

Lecture 5 Preface for Dimensionality Reduction - Part 3

Lecture 6 Preface for Dimensionality Reduction - Part 4

Lecture 7 Preface for Dimensionality Reduction - Part 5

Lecture 8 Geometric Intuition of PCA

Lecture 9 Mathematical formulation of PCA - Part 1

Lecture 10 Mathematical formulation of PCA - Part 2

Students currently studying probability and statistics or students about to start probability and statistics,Anyone who wants to study math for fun,Anyone wanting to learn foundational Maths for Data Science,Anyone who wants to understand what goes behind the popular packages