#### 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