Pandas for Data Science

This course is part of Programming for Python Data Science: Principles to Practice Specialization

Instructors: Genevieve M. Lipp +3 more

What you'll learn

  •   How and when to leverage the Pandas library for your data science projects
  •   Best practices for cleaning, manipulating, and optimizing data with Pandas
  • Skills you'll gain

  •   Data Manipulation
  •   NumPy
  •   Pandas (Python Package)
  •   Data Import/Export
  •   Data Analysis
  •   Data Integration
  •   Python Programming
  •   Query Languages
  •   Debugging
  •   Data Cleansing
  • There are 4 modules in this course

    We recommend you should take this course after the first two courses of the specialization. However, if you hold a prerequisite knowledge of basic algebra, Python programming, and NumPy, you should be able to complete the material in this course. In the first week, we’ll discuss Python file concepts, including the programming syntax that allows you to read and write to a file. Then in the following weeks, we’ll transition into discussing Pandas more specifically and the pros and cons of using this library for specific data projects. By the end of this course, you should be able to know when to use Pandas, how to load and clean data in Pandas, and how to use Pandas for data manipulation. This will prepare you to take the next step in your data scientist journey using Python; creating larger software programs.

    Module 2: Tabular Data with Pandas

    Module 3: Loading and Cleaning Data

    Module 4: Data Manipulation

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