Learning Python for Data Analysis and Visualization

Learn python and how to use it to analyze,visualize and present data. Includes tons of sample code and hours of video!
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  • Lectures 110
  • Contents Video: 21 hours
    Other: 4 mins
  • Skill Level All Levels
  • Languages English, captions
  • Includes Lifetime access
    30 day money back guarantee!
    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 3/2015 English Closed captions available

Course Description

NOTE: IF YOU ARE A COMPLETE BEGINNER IN PYTHON-CHECK OUT MY OTHER COURSE "COMPLETE PYTHON BOOTCAMP"!

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 100 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 100+ lectures and over 20 hours of information and more than 100 example python code notebooks, you will be excellently prepared for a future in data science!

What are the requirements?

  • Basic math skills.
  • Basic to Intermediate Python Skills
  • Have a computer (either Mac, Windows, or Linux)
  • Desire to learn!

What am I going to get from this course?

  • Have an intermediate skill level of Python programming.
  • Use the Jupyter Notebook Environment.
  • Use the numpy library to create and manipulate arrays.
  • Use the pandas module with Python to create and structure data.
  • Learn how to work with various data formats within python, including: JSON,HTML, and MS Excel Worksheets.
  • Create data visualizations using matplotlib and the seaborn modules with python.
  • Have a portfolio of various data analysis projects.

What is the target audience?

  • Anyone interested in learning more about python, data science, or data visualizations.
  • Anyone interested about the rapidly expanding world of data science!

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Intro to Course and Python
03:52

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

Course FAQs
Article
Section 2: Setup
Installation Setup and Overview
07:16
10:56

More course info

iPython/Jupyter Notebook Overview
14:57
Section 3: Learning Numpy
Article

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

07:27

Learn to create arrays with numpy and Python.

04:41

Learn how to perform operations on multiple arrays and scalars!

14:19

Learn how to index arrays with numpy.

04:07

Learn several universal array functions in numpy.

06:04

Learn how to transpose arrays with numpy.

21:48

Learn different methods of processing arrays.

07:59

Learn how to import and export your arrays.

Section 4: Intro to Pandas
13:58

Learn about the Series data structure in pandas.

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()

04:59

Learn how to index Series and DataFrames in pandas.

15:54

Learn how to reindex in pandas.

05:41

Learn how to drop data entries in pandas.

10:22

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

10:14

Learn how to align your data in Python.

05:38

Learn how to rank and sort data entries.

22:35

Learn how to quickly get summary statistics in pandas.

11:37

Learn different ways of dealing with missing data in pandas.

13:32

Learn how to create hierarchical indexes in pandas.

Section 5: Working with Data: Part 1
10:03

Learn how to import and export text files with pandas.

04:12

Learn how to import and export JSON files with pandas.

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

03:51

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

Section 6: Working with Data: Part 2
20:31

Learn the basics of merging data sets.

12:36

Learn how to merge using an index.

09:19

Learn how to concatenate arrays,matrices, and DataFrames.

10:20

Learn how to combine DataFrames in pandas.

07:51

Learn how to reshape data sets.

05:31

Learn how to create Pivot tables with Python.

05:54

Learn how to take care of duplicate data entries.

04:12

Learn how to use mapping with pandas.

03:15

Learn how to replace data in pandas.

05:55

Learn how to rename indexes in pandas.

06:16

Learn how to use bins with pandas.

06:52

Learn how to find outliers in your data with pandas.

05:21

Learn how to use permutation with numpy and pandas.

Section 7: Working with Data: Part 3
17:41

Learn how to use advanced groupby techniques.

13:21

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

12:42

Learn about Data Aggregation with Python and pandas.

10:02

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

05:06

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

Section 8: Data Visualization
01:44

Quick overview on installing seaborn. Use "conda install seaborn" or "pip install seaborn".

09:19

Learn how to create histograms using seaborn and python.

25:58

Learn how to create kernel Density Estimation Plots with seaborn.

06:14

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

08:52

Learn how to create box and violin plots with seaborn.

18:39

Learn how to create regression plots in seaborn.

16:49

Learn how to create heatmaps with seaborn.

Section 9: Example Projects.
03:02

Quick Preview for those interested in enrolling in the course!

04:34

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

17:06

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

Titanic Project - Part 2
16:08
Titanic Project - Part 3
15:49
Titanic Project - Part 4
02:05
Intro to Data Project - Stock Market Analysis
03:13
Data Project - Stock Market Analysis Part 1
11:19
Data Project - Stock Market Analysis Part 2
18:06
Data Project - Stock Market Analysis Part 3
10:24
Data Project - Stock Market Analysis Part 4
06:56
Data Project - Stock Market Analysis Part 5
27:40
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!

Data Project - Election Analysis Part 1
18:00
Data Project - Election Analysis Part 2
20:34
Data Project - Election Analysis Part 3
15:04
Data Project - Election Analysis Part 4
25:57
Section 10: Machine Learning
12:51

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

17:40

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

Linear Regression Part 2
18:21
Linear Regression Part 3
18:45
Linear Regression Part 4
22:08
Logistic Regression Part 1
14:18
Logistic Regression Part 2
14:25
Logistic Regression Part 3
12:20
Logistic Regression Part 4
22:22
Multi Class Classification Part 1 - Logistic Regression
18:33
Multi Class Classification Part 2 - k Nearest Neighbor
23:05
Support Vector Machines Part 1
12:52
Support Vector Machines - Part 2
29:07
Naive Bayes Part 1
10:03
Naive Bayes Part 2
12:26
31:47

Learn how to Use SciKit Learn for Decision Trees and Random Forests

07:20

Learn about Natural Language Processing!

15:39

Learn about Natural Language Processing!

20:48
Learn about Natural Language Processing!
16:16
Learn about Natural Language Processing!

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Instructor Biography

Jose Portilla, Data Scientist

Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and over 3 years experience as a teaching assistant for various engineering classes. He has publications and patents in various fields such as microfluidics and materials science. Over the course of his career he has developed a skill set in analyzing data, specifically using Python and a variety of modules and libraries. He hopes to use his experience in teaching and data science to help other people learn the power of the Python programming language and its ability to analyze data, as well as present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for a start-up and provides in-person data science and python training courses to a variety of companies, including top banks such as Credit Suisse. Feel free to contact him on LinkedIn for more information on in-person training sessions.

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