2015
Udemy
Jose Portilla
19:27:11
English

This course will give you the resources to learn python and effectively use it analyze and visualize data! Start your career in Data Science!
You'll get a full understanding of how to program with Python and how to use it in conjunction with scientific computing modules and libraries to analyze data.
You will also get lifetime access to over 90 example python code notebooks, new and updated videos, as well as future additions of various data analysis projects that you can use for a portfolio to show future employers!
By the end of this course you will:
- Have an understanding of how to program in Python.
- Know how to create and manipulate arrays using numpy and Python.
- Know how to use pandas to create and analyze data sets.
- Know how to use matplotlib and seaborn libraries to create beautiful data visualization.
- Have an amazing portfolio of example python data analysis projects!
- Have an understanding of Machine Learning and SciKit Learn!
With 90+ lectures and over 16 hours of information and more than 80 example python code notebooks, you will be excellently prepared for a future in data science!

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

https://www.udemy.com/learning-python-for-data-analysis-and-visualization/

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