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30-Day Money-Back Guarantee

This course includes:

  • 21 hours on-demand video
  • 3 articles
  • 4 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
Development Programming Languages Data Analysis

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!
Bestseller
Rating: 4.3 out of 54.3 (15,666 ratings)
166,062 students
Created by Jose Portilla
Last updated 9/2019
English
English [Auto], Indonesian [Auto], 
30-Day Money-Back Guarantee

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.

Course content

15 sections • 110 lectures • 21h 5m total length

  • Preview03:52
  • Course FAQs
    03:12

  • Installation Setup and Overview
    07:16
  • IDEs and Course Resources
    10:56
  • iPython/Jupyter Notebook Overview
    14:57

  • Intro to numpy
    00:24
  • Creating arrays
    07:27
  • Using arrays and scalars
    04:41
  • Indexing Arrays
    14:19
  • Array Transposition
    04:07
  • Universal Array Function
    06:04
  • Array Processing
    21:48
  • Array Input and Output
    07:59

  • Series
    13:58
  • DataFrames
    17:46
  • Index objects
    04:59
  • Reindex
    15:54
  • Drop Entry
    05:41
  • Selecting Entries
    10:22
  • Data Alignment
    10:14
  • Rank and Sort
    05:38
  • Summary Statistics
    22:35
  • Missing Data
    11:37
  • Index Hierarchy
    13:32

  • Reading and Writing Text Files
    10:03
  • JSON with Python
    04:12
  • HTML with Python
    04:36
  • Microsoft Excel files with Python
    03:51

  • Merge
    20:31
  • Merge on Index
    12:36
  • Concatenate
    09:19
  • Combining DataFrames
    10:20
  • Reshaping
    07:51
  • Pivoting
    05:31
  • Duplicates in DataFrames
    05:54
  • Mapping
    04:12
  • Replace
    03:15
  • Rename Index
    05:55
  • Binning
    06:16
  • Outliers
    06:52
  • Permutation
    05:21

  • GroupBy on DataFrames
    17:41
  • GroupBy on Dict and Series
    13:21
  • Preview12:42
  • Splitting Applying and Combining
    10:02
  • Cross Tabulation
    05:06

  • Installing Seaborn
    01:44
  • Histograms
    09:19
  • Preview25:58
  • Combining Plot Styles
    06:14
  • Box and Violin Plots
    08:52
  • Regression Plots
    18:39
  • Heatmaps and Clustered Matrices
    16:49

  • Data Projects Preview
    03:02
  • Intro to Data Projects
    04:34
  • 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
  • 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

  • Introduction to Machine Learning with SciKit Learn
    12:51
  • 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
  • Decision Trees and Random Forests
    31:47
  • Natural Language Processing Part 1
    07:20
  • Natural Language Processing Part 2
    15:39
  • Natural Language Processing Part 3
    20:48
  • Natural Language Processing Part 4
    16:16

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.) THERE IS AN UPDATED VERSION OF THIS COURSE: 

"PYTHON FOR DATA SCIENCE AND MACHINE LEARNING BOOTCAMP" 

2.) IF YOU ARE A COMPLETE BEGINNER IN PYTHON-CHECK OUT MY OTHER COURSE "COMPLETE PYTHON MASTERCLASS JOURNEY"!


CLICK ON MY PROFILE TO FIND IT. (PLEASE WATCH THE FIRST PROMO VIDEO ON THIS PAGE FOR MORE INFO)

**********************************************************************************************************


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 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!

Featured review

Scott Ferguson
Scott Ferguson
35 courses
5 reviews
Rating: 4.5 out of 56 months ago
Excellent introduction to the topics. There are very minor attribute errors for updated modules, but that gives the student an excellent opportunity to search for solutions on StackOverflow or other websites.

Instructor

Jose Portilla
Head of Data Science, Pierian Data Inc.
Jose Portilla
  • 4.6 Instructor Rating
  • 700,557 Reviews
  • 2,143,330 Students
  • 31 Courses

  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 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 contact him on LinkedIn for more information on in-person training sessions or group training sessions in Las Vegas, NV.

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