A– Z Maths For Data Science

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

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https://www.udemy.com/course/a-z-maths-for-data-science-u/

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