Learning Python for Data Analysis and Visualization Ver 1

Focused View

Jose Portilla

21:01:24

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01 - Intro to Course and Python

001 Course Intro.mp4

03:52

002 Course FAQs.html

02 - Setup

001 Installation Setup and Overview.mp4

07:17

002 IDEs and Course Resources.mp4

10:56

003 Lecture-4-Info.txt

003 iPythonJupyter Notebook Overview.mp4

14:57

external-links.txt

03 - Learning Numpy

001 Intro to numpy.html

002 Creating arrays.mp4

07:27

003 Using arrays and scalars.mp4

04:42

004 Indexing Arrays.mp4

14:19

005 Array Transposition.mp4

04:07

006 Universal Array Function.mp4

06:04

007 Array Processing.mp4

21:48

008 Array Input and Output.mp4

07:59

04 - Intro to Pandas

001 Series.mp4

13:58

002 DataFrames.mp4

17:46

003 Index objects.mp4

04:59

004 Reindex.mp4

15:54

005 Drop Entry.mp4

05:42

006 Selecting Entries.mp4

10:22

007 Data Alignment.mp4

10:14

008 Rank and Sort.mp4

05:38

009 Summary Statistics.mp4

22:35

010 Missing Data.mp4

11:38

011 Index Hierarchy.mp4

13:32

05 - Working with Data Part 1

001 Reading and Writing Text Files.mp4

10:03

002 JSON with Python.mp4

04:12

003 HTML with Python.mp4

04:36

004 Microsoft Excel files with Python.mp4

03:51

06 - Working with Data Part 2

001 Merge.mp4

20:31

002 Merge on Index.mp4

12:36

003 Concatenate.mp4

09:19

004 Combining DataFrames.mp4

10:20

005 Reshaping.mp4

07:51

006 Pivoting.mp4

05:31

007 Duplicates in DataFrames.mp4

05:54

008 Mapping.mp4

04:12

009 Replace.mp4

03:15

010 Rename Index.mp4

05:55

011 Binning.mp4

06:16

012 Outliers.mp4

06:52

013 Permutation.mp4

05:21

07 - Working with Data Part 3

001 GroupBy on DataFrames.mp4

17:41

002 GroupBy on Dict and Series.mp4

13:21

003 Aggregation.mp4

12:42

004 Splitting Applying and Combining.mp4

10:02

005 Cross Tabulation.mp4

05:06

08 - Data Visualization

001 Installing Seaborn.mp4

01:44

002 Histograms.mp4

09:19

003 Kernel Density Estimate Plots.mp4

25:58

004 Combining Plot Styles.mp4

06:14

005 Box and Violin Plots.mp4

08:52

006 Regression Plots.mp4

18:39

007 Heatmaps and Clustered Matrices.mp4

16:49

09 - Example Projects

001 Data Projects Preview.mp4

03:02

002 First-Data-Project.txt

002 Intro to Data Projects.mp4

04:34

003 First-Data-Project.txt

003 Titanic Project - Part 1.mp4

17:06

004 Titanic Project - Part 2.mp4

16:08

005 Titanic Project - Part 3.mp4

15:49

006 Titanic Project - Part 4.mp4

02:05

007 Intro to Data Project - Stock Market Analysis.mp4

03:13

008 Data Project - Stock Market Analysis Part 1.mp4

11:19

009 Data Project - Stock Market Analysis Part 2.mp4

18:06

010 Data Project - Stock Market Analysis Part 3.mp4

10:24

011 Data Project - Stock Market Analysis Part 4.mp4

06:56

012 Data Project - Stock Market Analysis Part 5.mp4

27:40

013 Data Project - Intro to Election Analysis.mp4

02:20

014 Data Project - Election Analysis Part 1.mp4

18:00

015 Data Project - Election Analysis Part 2.mp4

20:34

016 Data Project - Election Analysis Part 3.mp4

15:04

017 Data Project - Election Analysis Part 4.mp4

25:57

10 - Machine Learning

001 Introduction to Machine Learning with SciKit Learn.mp4

12:51

002 Linear Regression Part 1.mp4

17:40

003 Linear Regression Part 2.mp4

18:21

004 Linear Regression Part 3.mp4

18:45

005 Linear Regression Part 4.mp4

22:08

006 Logistic Regression Part 1.mp4

14:19

007 Logistic Regression Part 2.mp4

14:26

008 Logistic Regression Part 3.mp4

12:20

009 Logistic Regression Part 4.mp4

22:22

010 Multi Class Classification Part 1 - Logistic Regression.mp4

18:33

011 Multi Class Classification Part 2 - k Nearest Neighbor.mp4

23:05

012 Support Vector Machines Part 1.mp4

12:52

013 Support Vector Machines - Part 2.mp4

29:07

014 Naive Bayes Part 1.mp4

10:03

015 Naive Bayes Part 2.mp4

12:26

016 Decision Trees and Random Forests.mp4

31:47

017 Natural Language Processing Part 1.mp4

07:20

018 Natural Language Processing Part 2.mp4

15:39

019 Natural Language Processing Part 3.mp4

20:48

020 Natural Language Processing Part 4.mp4

16:16

11 - Appendix Statistics Overview

001 Intro to Appendix B.mp4

02:44

001 Viewer-Link-For-Stats-Notes.txt

002 Discrete Uniform Distribution.mp4

06:11

003 Continuous Uniform Distribution.mp4

07:03

004 Binomial Distribution.mp4

12:35

005 Poisson Distribution.mp4

10:55

006 Normal Distribution.mp4

06:24

007 Sampling Techniques.mp4

04:54

008 T-Distribution.mp4

05:09

009 Hypothesis Testing and Confidence Intervals.mp4

20:08

010 Chi Square Test and Distribution.mp4

02:53

011 Bayes Theorem.mp4

10:02

12 - Appendix SQL and Python

001 Introduction to SQL with Python.mp4

09:59

002 SQL - SELECT,DISTINCT,WHERE,AND & OR.mp4

09:58

003 SQL WILDCARDS, ORDER BY, GROUP BY and Aggregate Functions.mp4

08:25

13 - Appendix Web Scraping with Python

001 Web Scraping Part 1.mp4

12:14

002 Web Scraping Part 2.mp4

12:14

14 - Appendix Python Special Offers

001 Python Overview Part 1.mp4

18:52

002 Python Overview Part 2.mp4

12:18

003 Python Overview Part 3.mp4

10:13

15 - BONUS SECTION THANK YOU!

001 Bonus Lecture.html

Description

Learn python and how to use it to analyze,visualize and present data. Includes tons of sample code and hours of video!

What You'll Learn?

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.

Who is this for?

Anyone interested in learning more about python, data science, or data visualizations.

Anyone interested about the rapidly expanding world of data science!

What You Need to Know?

Basic math skills.

Basic to Intermediate Python Skills

Have a computer (either Mac, Windows, or Linux)

Desire to learn!

More details

Description

This course will give you the resources to learn python and effectively use it analyze and visualize data! Start your career in Data Science!
Â Â 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.Â
Â 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!Â
Â Â By the end of this course you will:Â
Â - Have an understanding of how to program in Python.Â
Â - Know how to create and manipulate arrays using numpy and Python.Â
Â - Know how to use pandas to create and analyze data sets.Â
Â - Know how to use matplotlib and seaborn libraries to create beautiful data visualization.Â
Â - Have an amazing portfolio of example python data analysis projects!Â
- Have an understanding of Machine Learning and SciKit Learn!
Â 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!Â

Please make sure you read the entire page to understand if the course is the correct version for you.
Who this course is for:
Anyone interested in learning more about python, data science, or data visualizations.
Anyone interested about the rapidly expanding world of data science!

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Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings.

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