2015

19:27 hours

Section 1: Intro to Course and Python

Lecture 1 Course Intro Preview 04:17

Get a basic overview of what you will learn in this course.

Lecture 2 Resources for Learning Python 01:38

Get links to free resources online to interactively learn Python! Check out the attached text file.

Check out the Appendix A videos for a quick crash course in python by me, or check out the links in the attached file!

Section 2: Setup

Lecture 3 Installation Setup and Overview 14:43

Lecture 4 iPython Notebook Overview 05:42

Lecture 5 Course Environment Overview 02:36

Overview of the remaining lectures will be structured. Using iPython Notebooks and links to nbviewer.

Section 3: Learning Numpy

Lecture 6 Intro to numpy

Text

Take a quick glance at the links in the text and then move on to the next lecture for the video lessons!

Lecture 7 Creating arrays 07:27

Learn to create arrays with numpy and Python.

Lecture 8 Using arrays and scalars 04:41

Learn how to perform operations on multiple arrays and scalars!

Lecture 9 Indexing Arrays 14:19

Learn how to index arrays with numpy.

Lecture 10 Array Transposition 04:07

Learn several universal array functions in numpy.

Lecture 11 Universal Array Function 06:04

Learn how to transpose arrays with numpy.

Lecture 12 Array Processing 21:48

Learn different methods of processing arrays.

Lecture 13 Array Input and Output 07:59

Learn how to import and export your arrays.

Section 4: Intro to Pandas

Lecture 14 Series 13:58

Learn about the Series data structure in pandas.

Lecture 15 DataFrames 17:46

Learn about the DataFrame structure in pandas.

Important Note: If copying directly from Wikipedia does not work, paste the data into a word processor or NotePad Editor and then copy it from there and then run pd.read_clipboard()

Lecture 16 Index objects 04:59

Learn how to index Series and DataFrames in pandas.

Lecture 17 Reindex 15:54

Learn how to reindex in pandas.

Lecture 18 Drop Entry 05:41

Learn how to drop data entries in pandas.

Lecture 19 Selecting Entries 10:22

Learn how to select particular entries in a pandas data structure.

Lecture 20 Data Alignment 10:14

Learn how to align your data in Python.

Lecture 21 Rank and Sort 05:38

Learn how to rank and sort data entries.

Lecture 22 Summary Statistics 22:35

Learn how to quickly get summary statistics in pandas.

Lecture 23 Missing Data 11:37

Learn different ways of dealing with missing data in pandas.

Lecture 24 Index Hierarchy 13:32

Learn how to create hierarchical indexes in pandas.

Section 5: Working with Data: Part 1

Lecture 25 Reading and Writing Text Files 10:03

Learn how to import and export text files with pandas.

Lecture 26 JSON with Python 04:12

Learn how to import and export JSON files with pandas.

Lecture 27 HTML with Python 04:36

Learn how to import HTML files with pandas.

NOTE: Install the following before this lecture, using either conda install or pip install:

pip install beautifulsoup4

pip install lxml

Lecture 28 Microsoft Excel files with Python 03:51

Learn how to import and export MS Excel files with pandas.

Section 6: Working with Data: Part 2

Lecture 29 Merge 20:31

Learn the basics of merging data sets.

Lecture 30 Merge on Index 12:36

Learn how to merge using an index.

Lecture 31 Concatenate 09:19

Learn how to concatenate arrays,matrices, and DataFrames.

Lecture 32 Combining DataFrames 10:20

Learn how to combine DataFrames in pandas.

Lecture 33 Reshaping 07:51

Learn how to reshape data sets.

Lecture 34 Pivoting 05:31

Learn how to create Pivot tables with Python.

Lecture 35 Duplicates in DataFrames 05:54

Learn how to take care of duplicate data entries.

Lecture 36 Mapping 04:12

Learn how to use mapping with pandas.

Lecture 37 Replace 03:15

Learn how to replace data in pandas.

Lecture 38 Rename Index 05:55

Learn how to rename indexes in pandas.

Lecture 39 Binning 06:16

Learn how to use bins with pandas.

Lecture 40 Outliers 06:52

Learn how to find outliers in your data with pandas.

Lecture 41 Permutation 05:21

Learn how to use permutation with numpy and pandas.

Section 7: Working with Data: Part 3

Lecture 42 GroupBy on DataFrames 17:41

Learn how to use advanced groupby techniques.

Lecture 43 GroupBy on Dict and Series 13:21

Learn how to use the groupby method on Dictionaries and Series.

Lecture 44 Aggregation Preview 12:42

Learn about Data Aggregation with Python and pandas.

Lecture 45 Splitting Applying and Combining 10:02

Learn about the powerful Split-Apply-Combine technique and how to use it in pandas.

Lecture 46 Cross Tabulation 05:06

Learn about cross-tabulation in pandas, a special case of pivot table!

Section 8: Data Visualization

Lecture 47 Installing Seaborn 01:44

Quick overview on installing seaborn. Use “conda install seaborn” or “pip install seaborn”.

Lecture 48 Histograms 09:19

Learn how to create histograms using seaborn and python.

Lecture 49 Kernel Density Estimate Plots 25:58

Learn how to create kernel Density Estimation Plots with seaborn.

Lecture 50 Combining Plot Styles 06:14

Learn how to combine histograms, KDE , and rug plots onto a single figure.

Lecture 51 Box and Violin Plots 08:52

Learn how to create box and violin plots with seaborn.

Lecture 52 Regression Plots 18:39

Learn how to create regression plots in seaborn.

Lecture 53 Heatmaps and Clustered Matrices 16:49

Learn how to create heatmaps with seaborn.

Section 9: Example Projects.

Lecture 54 Data Projects Preview 03:02

Quick Preview for those interested in enrolling in the course!

Lecture 55 Intro to Data Projects 04:34

Get an introduction to Github, Kaggle, and great public data sets!

Lecture 56 Titanic Project – Part 1 17:06

Learn how to analyze the Titanic Kaggle Problem with Python, pandas, and seaborn!

Lecture 57 Titanic Project – Part 2 16:08

Lecture 58 Titanic Project – Part 3 15:49

Lecture 59 Titanic Project – Part 4 02:05

Lecture 60 Intro to Data Project – Stock Market Analysis 03:13

Lecture 61 Data Project – Stock Market Analysis Part 1 11:19

Lecture 62 Data Project – Stock Market Analysis Part 2 18:06

Lecture 63 Data Project – Stock Market Analysis Part 3 10:24

Lecture 64 Data Project – Stock Market Analysis Part 4 06:56

Lecture 65 Data Project – Stock Market Analysis Part 5 27:40

Lecture 66 Data Project – Intro to Election Analysis 02:20

Please Note: The second presidential debate was Oct 16 and not Oct 11. Oct 11 was the date of the Vice Presidential Debate!

Lecture 67 Data Project – Election Analysis Part 1 18:00

Lecture 68 Data Project – Election Analysis Part 2 20:34

Lecture 69 Data Project – Election Analysis Part 3 15:04

Lecture 70 Data Project – Election Analysis Part 4 26:02

Section 10: Machine Learning

Lecture 71 Introduction to Machine Learning with SciKit Learn 12:51

Learn about the Pydata Ecosystem and SciKit Learn and what Machine Learning is all about!

Lecture 72 Linear Regression Part 1 17:40

Learn about the Math behind Linear Regression then implement it with SciKit Learn!

Lecture 73 Linear Regression Part 2 18:21

Lecture 74 Linear Regression Part 3 18:45

Lecture 75 Linear Regression Part 4 22:08

Lecture 76 Logistic Regression Part 1 14:18

Lecture 77 Logistic Regression Part 2 14:25

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 23:05

Lecture 82 Support Vector Machines Part 1 12:52

Lecture 83 Support Vector Machines – Part 2 29:07

Lecture 84 Naive Bayes Part 1 10:03

Lecture 85 Naive Bayes Part 2 12:26

Lecture 86 Upcoming topics! Text

Section 11: Appendix A: Python Overview

Lecture 87 Intro to Resources for Learning Python 05:07

Even more great and free resources to learn Python!

Lecture 88 Python Overview Part 1 18:52

Lecture 89 Python Overview Part 2 12:18

Lecture 90 Python Overview Part 3 10:13

Section 12: Appendix B: Statistics Overview

Lecture 91 Intro to Appendix B 02:44

Lecture 92 Discrete Uniform Distribution 06:11

Lecture 93 Continuous Uniform Distribution 07:03

Lecture 94 Binomial Distribution 12:35

Lecture 95 Poisson Distribution 10:55

Lecture 96 Normal Distribution 06:24

Lecture 97 Sampling Techniques 04:54

Lecture 98 T-Distribution 05:09

Lecture 99 Hypothesis Testing and Confidence Intervals 20:08

Lecture 100 Chi Square Test and Distribution 02:53