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

Basic Info
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.
Pricing
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Ratings and Reviews
Rated 4.5 out of 5 of 2,441 ratings

It's a comprehensive course for the learners who wants to learn the skills of data science using python.

No words. Smashing one

Python is huge. Course helps you focus and apply Python to data science.

Me? was new to Python; was novice at programming; had strong background in math and business (both helpful, but not prerequisite); read "Python for Data Analysis" by Wes Mckinney to supplement lack of programming experience -- focused on numpy and pandas chapters; frequented stack overflow

Overall, I liked the course but there were some flaws (not too big ones but still worth mentioning) - The way things were explained seemed more like just giving information about something rather than explaining it well (of course at times!). Exercises were really very good as they promoted individual learning more than just depending on what was taught. Recommended for beginners/