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Udemy Data Analysis with Pandas and Python

Created/Published/Taught by:
Boris Paskhaver

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Cost: $20.00

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From Udemy:

“The most comprehensive Pandas course available on Udemy! An excellent choice for both beginners and experts looking to refresh their technical knowledge on one of the most popular Python libraries in the world!

Data Analysis with Pandas and Python offers 19+ hours of in-depth tutorials on one of the most powerful data analysis toolkits available today. Lessons include:

  • installing
  • sorting
  • filtering
  • grouping
  • aggregating
  • de-duplicating
  • pivoting
  • munging
  • deleting
  • merging
  • text cleaning
  • visualizing

If you’ve spent time in a spreadsheet software like Microsoft Excel, Apple Numbers, or Google Sheets and are eager to take your data analysis skills to the next level, this course is for you!

Data Analysis with Pandas and Python introduces you to the popular Pandas library built on top of the Python programming language.

Pandas is a powerhouse tool that allows you to do anything and everything with colossal data sets — analyzing, organizing, sorting, filtering, pivoting, aggregating, munging, cleaning, calculating, and more! I call it “Excel on steroids”!

Over the course of more than 19 hours, I’ll take you step-by-step through Pandas, from installation to visualization! We’ll cover hundreds of different methods, attributes, features, and functionalities packed away inside this awesome library. We’ll dive into tons of different datasets, short and long, broken and pristine, to demonstrate the incredible versatility and efficiency of this package.

Data Analysis with Pandas and Python is bundled with dozens of datasets for you to use. Dive right in and follow along with my lessons to see how easy it is to get started with pandas!”

Recommended Prerequisites: Basic/intermediate experience with Microsoft Excel or another spreadsheet software (common functions, vlookups, Pivot Tables, etc.). Basic experience with the Python programming language. Strong knowledge of data types (strings, integers, floating points, booleans, etc.).

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