Who is this class for: This course is part of “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.


Created by:  University of Michigan

LevelIntermediate
Language
English
How To PassPass all graded assignments to complete the course.
User Ratings
4.3 stars
Average User Rating 4.3See what learners said
Syllabus

FAQs
How It Works
Coursework
Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

Help from Your Peers
Help from Your Peers

Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

Certificates
Certificates

Earn official recognition for your work, and share your success with friends, colleagues, and employers.

Creators
University of Michigan
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
Pricing
AuditPurchase Course
Access to course materials

Available

Available

Access to graded materials

Not available

Available

Receive a final grade

Not available

Available

Earn a shareable Course Certificate

Not available

Available

Ratings and Reviews
Rated 4.3 out of 5 of 226 ratings

Great course. Here you can learn about applied plotting and data representation in Python

Very helpful course!

Several different API's are touched (matplotlib matlab-style and object oriented interfaces, seaborn, pandas, and it's easy to get lost. Some additional reference material would be helpful: cheat sheets, course slides with a bit more detail of the API's touched.

Its an essential course for all data scientist and also analysts work with every day visualizations. This course taught me about many things of Matplotlib and pandas plots. which I am unable to learn from any other source.