Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Coursera

Data Analysis in Python with pandas & matplotlib in Spyder

Codio via Coursera

Overview

Prepare for a new career with $100 off Coursera Plus
Gear up for jobs in high-demand fields: data analytics, digital marketing, and more.
Code and run your first Python script in minutes without installing anything!

This course is designed for learners with no coding experience, providing a crash course in Python, which enables the learners to delve into core data analysis topics that can be transferred to other languages. In this course, you will learn how to import and organize your data, use functions to gather descriptive statistics, and perform statistical tests.

To allow for a truly hands-on, self-paced learning experience, this course is video-free.

Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You’ll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to small, approachable coding exercises that take minutes instead of hours. Finally, a longer-form lab at the end of the course will provide you an opportunity to apply all learned concepts within a real-world context.

Syllabus

  • Describing a Numerical Data Set
  • Importing and Describing Mixed Data Sets (pandas)
  • Statistical Tests to Determine if Populations are Different
  • Statistical Tests to Describe Relationships
  • Python Data Analysis Lab

Taught by

Kevin Noelsaint and Anh Le

Reviews

3.1 rating at Coursera based on 16 ratings

Start your review of Data Analysis in Python with pandas & matplotlib in Spyder

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.