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.

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

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Certificates

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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.
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Ratings and Reviews
Rated 4.5 out of 5 of 2,485 ratings

This is a very nice, structured organized course to start on empirical machine learning.

Very well constructed course. Admittedly, there is a pretty big gap between the content in the lecture and what is needed to finish the programming assignment. However, I think this is by design to help learners understand the content better and to seek solutions outside classroom. Highly recommended for anyone interested in data science.

Hi Professor,

Thank you so much for taking the time to structure this program. Looking forward to another great learning experience in course 2.

Thanks,

Neel Roshania

Good course