Learning Python for Data Analysis and Visualization
Course Description
NOTE: IF YOU ARE A COMPLETE BEGINNER IN PYTHONCHECK 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!
Curriculum
Section 1: Intro to Course and Python  

Lecture 1 
Course Intro

03:52  
Lecture 2 
Course FAQs

Article  
Section 2: Setup  
Lecture 3 
Installation Setup and Overview

07:16  
Lecture 4 
IDEs and Course Resources

10:56  
Lecture 5 
iPython/Jupyter Notebook Overview

14:57  
Section 3: Learning Numpy  
Lecture 6 
Intro to numpy

Article  
Lecture 7 
Creating arrays

07:27  
Lecture 8 
Using arrays and scalars

04:41  
Lecture 9 
Indexing Arrays

14:19  
Lecture 10 
Array Transposition

04:07  
Lecture 11 
Universal Array Function

06:04  
Lecture 12 
Array Processing

21:48  
Lecture 13 
Array Input and Output

07:59  
Section 4: Intro to Pandas  
Lecture 14 
Series

13:58  
Lecture 15 
DataFrames

17:46  
Lecture 16 
Index objects

04:59  
Lecture 17 
Reindex

15:54  
Lecture 18 
Drop Entry

05:41  
Lecture 19 
Selecting Entries

10:22  
Lecture 20 
Data Alignment

10:14  
Lecture 21 
Rank and Sort

05:38  
Lecture 22 
Summary Statistics

22:35  
Lecture 23 
Missing Data

11:37  
Lecture 24 
Index Hierarchy

13:32  
Section 5: Working with Data: Part 1  
Lecture 25 
Reading and Writing Text Files

10:03  
Lecture 26 
JSON with Python

04:12  
Lecture 27 
HTML with Python

04:36  
Lecture 28 
Microsoft Excel files with Python

03:51  
Section 6: Working with Data: Part 2  
Lecture 29 
Merge

20:31  
Lecture 30 
Merge on Index

12:36  
Lecture 31 
Concatenate

09:19  
Lecture 32 
Combining DataFrames

10:20  
Lecture 33 
Reshaping

07:51  
Lecture 34 
Pivoting

05:31  
Lecture 35 
Duplicates in DataFrames

05:54  
Lecture 36 
Mapping

04:12  
Lecture 37 
Replace

03:15  
Lecture 38 
Rename Index

05:55  
Lecture 39 
Binning

06:16  
Lecture 40 
Outliers

06:52  
Lecture 41 
Permutation

05:21  
Section 7: Working with Data: Part 3  
Lecture 42 
GroupBy on DataFrames

17:41  
Lecture 43 
GroupBy on Dict and Series

13:21  
Lecture 44 
Aggregation

12:42  
Lecture 45 
Splitting Applying and Combining

10:02  
Lecture 46 
Cross Tabulation

05:06  
Section 8: Data Visualization  
Lecture 47 
Installing Seaborn

01:44  
Lecture 48 
Histograms

09:19  
Lecture 49 
Kernel Density Estimate Plots

25:58  
Lecture 50 
Combining Plot Styles

06:14  
Lecture 51 
Box and Violin Plots

08:52  
Lecture 52 
Regression Plots

18:39  
Lecture 53 
Heatmaps and Clustered Matrices

16:49  
Section 9: Example Projects.  
Lecture 54 
Data Projects Preview

03:02  
Lecture 55 
Intro to Data Projects

04:34  
Lecture 56 
Titanic Project – Part 1

17:06  
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  
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

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

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 
Decision Trees and Random Forests

31:47  
Lecture 87 
Natural Language Processing Part 1

07:20  
Lecture 88 
Natural Language Processing Part 2

15:39  
Lecture 89 
Natural Language Processing Part 3

20:48  
Lecture 90 
Natural Language Processing Part 4

16:16  
Section 11: Appendix: 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 
TDistribution

05:09  
Lecture 99 
Hypothesis Testing and Confidence Intervals

20:08  
Lecture 100 
Chi Square Test and Distribution

02:53  
Lecture 101 
Bayes Theorem

10:02  
Section 12: Appendix: SQL and Python  
Lecture 102 
Introduction to SQL with Python

09:59  
Lecture 103 
SQL – SELECT,DISTINCT,WHERE,AND & OR

09:58  
Lecture 104 
SQL WILDCARDS, ORDER BY, GROUP BY and Aggregate Functions

08:25  
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

18:52  
Lecture 108 
Python Overview Part 2

12:18  
Lecture 109 
Python Overview Part 3

10:13  
Section 15: BONUS: SPECIAL DISCOUNT COUPONS  
Lecture 110 
BONUS: Special Offers!

Article 
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 startup and provides inperson 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 inperson training sessions.
Course Features
 Lectures 0
 Quizzes 0
 Duration 50 hours
 Skill level All level
 Language English
 Students 33768
 Assessments Self