Section 1 : Intro to Course and Python

 		Lecture 1 Course Intro 3:52
Lecture 2 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 2 : Setup

 		Lecture 3 Installation Setup and Overview 7:17
Lecture 4 IDEs and Course Resources 10:56
Lecture 5 iPythonJupyter Notebook Overview 14:57

Section 3 : Learning Numpy

 		Lecture 6 Intro to numpy Text
Lecture 7 Creating arrays 7:27
Lecture 8 Using arrays and scalars 4:42
Lecture 9 Indexing Arrays
Lecture 10 Array Transposition 4:7
Lecture 11 Universal Array Function 6:5
Lecture 12 Array Processing 21:48
Lecture 13 Array Input and Output

Section 4 : Intro to Pandas

 		Lecture 14 Series 13:56
Lecture 15 DataFrames 17:46
Lecture 16 Index objects 4:59
Lecture 17 Reindex 15:52
Lecture 18 Drop Entry 5:42
Lecture 19 Selecting Entries 10:23
Lecture 20 Data Alignment 10:14
Lecture 21 Rank and Sort 5:37
Lecture 22 Summary Statistics 22:35
Lecture 23 Missing Data 11:38
Lecture 24 Index Hierarchy 13:29

Section 5 : Working with Data Part 1

 		Lecture 25 Reading and Writing Text Files 10:2
Lecture 26 JSON with Python 4:11
Lecture 27 HTML with Python 4:35
Lecture 28 Microsoft Excel files with Python 3:52

Section 6 : Working with Data Part 2

 		Lecture 29 Merge 20:31
Lecture 30 Merge on Index 12:36
Lecture 31 Concatenate 9:19
Lecture 32 Combining DataFrames 10:18
Lecture 33 Reshaping 7:51
Lecture 34 Pivoting 5:31
Lecture 35 Duplicates in DataFrames 5:54
Lecture 36 Mapping 4:12
Lecture 37 Replace 3:16
Lecture 38 Rename Index 5:53
Lecture 39 Binning 6:16
Lecture 40 Outliers 6:52
Lecture 41 Permutation 5:22

Section 7 : Working with Data Part 3

 		Lecture 42 GroupBy on DataFrames 17:42
Lecture 43 GroupBy on Dict and Series 13:21
Lecture 44 Aggregation 12:41
Lecture 45 Splitting Applying and Combining 10:2
Lecture 46 Cross Tabulation 5:6

Section 8 : Data Visualization

 		Lecture 47 Installing Seaborn 1:44
Lecture 48 Histograms 9:19
Lecture 49 Kernel Density Estimate Plots 25:58
Lecture 50 Combining Plot Styles 6:14
Lecture 51 Box and Violin Plots 8:51
Lecture 52 Regression Plots 18:39
Lecture 53 Heatmaps and Clustered Matrices 16:46

Section 9 : Example Projects

 		Lecture 54 Data Projects Preview 3:2
Lecture 55 Intro to Data Projects 4:33
Lecture 56 Titanic Project - Part 1 17:7
Lecture 57 Titanic Project - Part 2 16:7
Lecture 58 Titanic Project - Part 3 15:47
Lecture 59 Titanic Project - Part 4 2:1
Lecture 60 Intro to Data Project - Stock Market Analysis 3:9
Lecture 61 Data Project - Stock Market Analysis Part 1 11:20
Lecture 62 Data Project - Stock Market Analysis Part 2 18:6
Lecture 63 Data Project - Stock Market Analysis Part 3 10:24
Lecture 64 Data Project - Stock Market Analysis Part 4 6:57
Lecture 65 Data Project - Stock Market Analysis Part 5 27:40
Lecture 66 Data Project - Intro to Election Analysis 2:21
Lecture 67 Data Project - Election Analysis Part 1 18:0
Lecture 68 Data Project - Election Analysis Part 2 20:34
Lecture 69 Data Project - Election Analysis Part 3 15:5
Lecture 70 Data Project - Election Analysis Part 4 25:57

Section 10 : Machine Learning

 		Lecture 71 Introduction to Machine Learning with SciKit Learn 12:51
Lecture 72 Linear Regression Part 1 17:40
Lecture 73 Linear Regression Part 2
Lecture 74 Linear Regression Part 3 18:45
Lecture 75 Linear Regression Part 4 22:8
Lecture 76 Logistic Regression Part 1 14:19
Lecture 77 Logistic Regression Part 2 14:26
Lecture 78 Logistic Regression Part 3 12:20
Lecture 79 Logistic Regression Part 4 22:22
Lecture 80 Multi Class Classification Part 1 - Logistic Regression 18:33
Lecture 81 Multi Class Classification Part 2 - k Nearest Neighbor 23:5
Lecture 82 Support Vector Machines Part 1 12:53
Lecture 83 Support Vector Machines - Part 2 29:7
Lecture 84 Naive Bayes Part 1 10:4
Lecture 85 Naive Bayes Part 2 12:26
Lecture 86 Decision Trees and Random Forests 31:48
Lecture 87 Natural Language Processing Part 1 7:20
Lecture 88 Natural Language Processing Part 2 15:39
Lecture 89 Natural Language Processing Part 3
Lecture 90 Natural Language Processing Part 4 16:17

Section 11 : Appendix Statistics Overview

 		Lecture 91 Intro to Appendix B 2:39
Lecture 92 Discrete Uniform Distribution 6:5
Lecture 93 Continuous Uniform Distribution 6:56
Lecture 94 Binomial Distribution 12:31
Lecture 95 Poisson Distribution 10:39
Lecture 96 Normal Distribution 6:25
Lecture 97 Sampling Techniques 4:51
Lecture 98 T-Distribution 5:7
Lecture 99 Hypothesis Testing and Confidence Intervals 20:8
Lecture 100 Chi Square Test and Distribution 2:53
Lecture 101 Bayes Theorem 10:3

Section 12 : Appendix SQL and Python

 		Lecture 102 Introduction to SQL with Python 9:59
Lecture 103 SQL - SELECT,DISTINCT,WHERE,AND & OR 9:59
Lecture 104 SQL WILDCARDS, ORDER BY, GROUP BY and Aggregate Functions 8:25

Section 13 : Appendix Web Scraping with Python

 		Lecture 105 Web Scraping Part 1 12:14
Lecture 106 Web Scraping Part 2 12:14

Section 14 : Appendix Python Special Offers

 		Lecture 107 Python Overview Part 1
Lecture 108 Python Overview Part 2 12:19
Lecture 109 Python Overview Part 3 10:9