Published 3/2023MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 3.98 GB | Duration: 9h 45m

 

Learn about Numpy, Pandas, SQL, Linear Algebra, Visualization and more through solved case study

What you'll learn

Basics of Python

Introduction to Numpy package for handling arrays

Introduction to Pandas package for cleaning and analysing data

Introduction to SQL

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

Central Limit Theorem

Hypothesis Testing

Requirements

Foundational Mathematics

Description

THE COMPREHENSIVE DATA ANALYST COURSE IS SET UP TO MAKE LEARNING FUN AND EASYThis 100+ lesson course includes 20+ 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:Python Basics, Data Structures - List, Tuple, Set, Dictionary, StringsPandas and NumpyLinear 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 ValueDonors Choose case studyAND 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:Life access to the courseFriendly support in the Q&A sectionUdemy Certificate of Completion available for 30-day money back guarantee

Overview

Section 1: Basic Python for Data Analysis

Lecture 1 Keywords, Identifiers and Variables

Lecture 2 Variable Assignment

Lecture 3 Strings & List

Lecture 4 Tuple

Lecture 5 Set

Lecture 6 Dictionary

Lecture 7 Data type conversion

Lecture 8 Python Comments

Lecture 9 Print Statement

Lecture 10 Python Arithmetic and Logical Operators

Lecture 11 Identity & Membership Operators

Lecture 12 For & While loop

Lecture 13 Conditional Statement

Lecture 14 Functions

Lecture 15 Modules

Lecture 16 List - Part 1

Lecture 17 List - Part 2

Lecture 18 List - Part 3

Lecture 19 List - Part 4

Lecture 20 List - Part 5

Lecture 21 Tuple - Part 1

Lecture 22 Tuple - Part 2

Lecture 23 Set - Part 1

Lecture 24 Set - Part 2

Lecture 25 Set - Part 3

Lecture 26 Dictionary

Lecture 27 Strings

Lecture 28 Numpy Introduction

Lecture 29 Creating arrays

Lecture 30 Array Operations - Part 1

Lecture 31 Array Masking

Lecture 32 Array Operations - Part 2

Lecture 33 Array Operations - Part 3

Lecture 34 Array broadcasting

Lecture 35 Array - Shape Manipulation & Sorting

Section 2: Basics of SQL

Lecture 36 SQL Introduction

Lecture 37 Select Command

Lecture 38 Limit Command

Lecture 39 Column Filtering

Lecture 40 DISTINCT command

Lecture 41 WHERE command

Lecture 42 AGGREGATE Functions

Lecture 43 GROUP BY command

Lecture 44 AND, OR, NULL commands

Lecture 45 LIKE command & WILDCARD characters

Lecture 46 JOINS - Part 1

Lecture 47 JOINS - Part 2

Lecture 48 JOINS - Part 3

Lecture 49 IN command

Lecture 50 HAVING Command

Lecture 51 UNION command

Lecture 52 ANY & ALL command

Section 3: Principal Component Analysis

Lecture 53 Preface for Dimensionality Reduction - Part 1

Lecture 54 Preface for Dimensionality Reduction - Part 2

Lecture 55 Preface for Dimensionality Reduction - Part 3

Lecture 56 Preface for Dimensionality Reduction - Part 4

Lecture 57 Preface for Dimensionality Reduction - Part 5

Lecture 58 Gometric Intuition of PCA

Lecture 59 Mathematical formulation of PCA - Part 1

Lecture 60 Mathematical formulation of PCA - Part 2

Lecture 61 Mathematical formulation of PCA - Part 3

Lecture 62 Failure cases of PCA

Lecture 63 Connecting Colab to Gdrive

Lecture 64 Understanding MNIST dataset

Lecture 65 Visualizing MNIST single digit

Lecture 66 MNIST Visualization - Method 1

Lecture 67 MNIST Visualization - Method 2

Aspiring Data Analysts,Business Analyst,Business Managers,Anyone wanting to learn basics of story telling through data

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