Learning Python for Data Analysis and Visualization
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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!
4.5 (3,781 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
44,061 students enrolled
Created by Jose Portilla
Last updated 3/2017
English
Current price: $10 Original price: $195 Discount: 95% off
1 day left at this price!
30-Day Money-Back Guarantee
Includes:
  • 21 hours on-demand video
  • 3 Articles
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I 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.
View Curriculum
Requirements
  • Basic math skills.
  • Basic to Intermediate Python Skills
  • Have a computer (either Mac, Windows, or Linux)
  • Desire to learn!
Description

PLEASE READ BEFORE ENROLLING: 

1.) IF YOU ARE A COMPLETE BEGINNER IN PYTHON-CHECK OUT MY OTHER COURSE "COMPLETE PYTHON BOOTCAMP"!

2.) THERE IS AN UPDATED VERSION OF THIS COURSE: "PYTHON FOR DATA SCIENCE AND MACHINE LEARNING" CLICK ON MY PROFILE TO FIND IT.

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!

Who 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!
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Curriculum For This Course
Expand All 110 Lectures Collapse All 110 Lectures 21:05:16
+
Intro to Course and Python
2 Lectures 07:18

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

Preview 03:52

Course FAQs
03:26
+
Setup
3 Lectures 33:09
Installation Setup and Overview
07:16

More course info

IDEs and Course Resources
10:56

iPython/Jupyter Notebook Overview
14:57
+
Learning Numpy
8 Lectures 01:06:52

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

Intro to numpy
00:27

Learn to create arrays with numpy and Python.

Creating arrays
07:27

Learn how to perform operations on multiple arrays and scalars!

Using arrays and scalars
04:41

Learn how to index arrays with numpy.

Indexing Arrays
14:19

Learn several universal array functions in numpy.

Array Transposition
04:07

Learn how to transpose arrays with numpy.

Universal Array Function
06:04

Learn different methods of processing arrays.

Array Processing
21:48

Learn how to import and export your arrays.

Array Input and Output
07:59
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Intro to Pandas
11 Lectures 02:12:16

Learn about the Series data structure in pandas.

Series
13:58

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()

DataFrames
17:46

Learn how to index Series and DataFrames in pandas.

Index objects
04:59

Learn how to reindex in pandas.

Reindex
15:54

Learn how to drop data entries in pandas.

Drop Entry
05:41

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

Selecting Entries
10:22

Learn how to align your data in Python.

Data Alignment
10:14

Learn how to rank and sort data entries.

Rank and Sort
05:38

Learn how to quickly get summary statistics in pandas.

Summary Statistics
22:35

Learn different ways of dealing with missing data in pandas.

Missing Data
11:37

Learn how to create hierarchical indexes in pandas.

Index Hierarchy
13:32
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Working with Data: Part 1
4 Lectures 22:42

Learn how to import and export text files with pandas.

Reading and Writing Text Files
10:03

Learn how to import and export JSON files with pandas.

JSON with Python
04:12

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

HTML with Python
04:36

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

Microsoft Excel files with Python
03:51
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Working with Data: Part 2
13 Lectures 01:43:53

Learn the basics of merging data sets.

Merge
20:31

Learn how to merge using an index.

Merge on Index
12:36

Learn how to concatenate arrays,matrices, and DataFrames.

Concatenate
09:19

Learn how to combine DataFrames in pandas.

Combining DataFrames
10:20

Learn how to reshape data sets.

Reshaping
07:51

Learn how to create Pivot tables with Python.

Pivoting
05:31

Learn how to take care of duplicate data entries.

Duplicates in DataFrames
05:54

Learn how to use mapping with pandas.

Mapping
04:12

Learn how to replace data in pandas.

Replace
03:15

Learn how to rename indexes in pandas.

Rename Index
05:55

Learn how to use bins with pandas.

Binning
06:16

Learn how to find outliers in your data with pandas.

Outliers
06:52

Learn how to use permutation with numpy and pandas.

Permutation
05:21
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Working with Data: Part 3
5 Lectures 58:52

Learn how to use advanced groupby techniques.

GroupBy on DataFrames
17:41

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

GroupBy on Dict and Series
13:21

Learn about Data Aggregation with Python and pandas.

Preview 12:42

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

Splitting Applying and Combining
10:02

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

Cross Tabulation
05:06
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Data Visualization
7 Lectures 01:27:35

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

Installing Seaborn
01:44

Learn how to create histograms using seaborn and python.

Histograms
09:19

Learn how to create kernel Density Estimation Plots with seaborn.

Preview 25:58

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

Combining Plot Styles
06:14

Learn how to create box and violin plots with seaborn.

Box and Violin Plots
08:52

Learn how to create regression plots in seaborn.

Regression Plots
18:39

Learn how to create heatmaps with seaborn.

Heatmaps and Clustered Matrices
16:49
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Example Projects.
17 Lectures 03:38:17

Quick Preview for those interested in enrolling in the course!

Data Projects Preview
03:02

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

Intro to Data Projects
04:34

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

Titanic Project - Part 1
17:06

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

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 - Intro to Election Analysis
02:20

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
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Machine Learning
20 Lectures 05:51:06

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

Introduction to Machine Learning with SciKit Learn
12:51

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

Linear Regression Part 1
17:40

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

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

Decision Trees and Random Forests
31:47

Learn about Natural Language Processing!

Natural Language Processing Part 1
07:20

Learn about Natural Language Processing!

Natural Language Processing Part 2
15:39

Learn about Natural Language Processing!
Natural Language Processing Part 3
20:48

Learn about Natural Language Processing!
Natural Language Processing Part 4
16:16
5 More Sections
About the Instructor
Jose Portilla
4.6 Average rating
31,167 Reviews
170,607 Students
10 Courses
Data Scientist

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 and programming. He has publications and patents in various fields such as microfluidics, materials science, and data science technologies. 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, as well as present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Data Inc. and provides in-person data science and python training courses to a variety of companies all over the world, including top banks such as Credit Suisse. Feel free to contact him on LinkedIn for more information on in-person training sessions.