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
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