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
4.5 stars
Average User Rating 4.5See 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.
Ratings and Reviews
Rated 4.5 out of 5 of 1,656 ratings

Very informative. Would be interested to see the correct way to complete many of the assignments, as I'm sure there are more elegant ways to complete them than I used

Nice course for beginners

excellent course

Much harder than I thought. Very in-depth introductory learning of python.

Preferably better if you allow scripting in .py because notebook is rather heavy and hard to

debug while assignments..

Hope you cover a bit more in detail with language structure, as well as give hints for solving assignments, since many parts were pretty above course level.

I would say the assignments were hard even for an R practitioner learning python like myself.