The University of Michigan's applied data-science specialisation in Python, covering data manipulation, visualisation, machine learning and text mining across five courses. It is practical and hands-on, but pitched at intermediate level — you should already know some Python before starting.
Good for: Learners with some Python who want practical, applied data-science skills.
Less suitable if: You are new to Python, or you want gentle, beginner pacing.
Requirements: Some Python and basic statistics; intermediate.
Realistic time: About 200 hours across five courses; a few months part-time.
About this course
Michigan's Applied Data Science with Python Specialization is the most tool-focused data science path on Coursera — five courses that go deep into practical Python data science rather than conceptual foundations. Course 1 covers pandas and Python for data manipulation; course 2 covers matplotlib and visualization; course 3 covers scikit-learn for applied machine learning; course 4 covers text analysis and NLP with Python; course 5 covers social network analysis with NetworkX. Every course uses real datasets and emphasizes applied skill.
What you'll learn
Manipulate, clean, and transform data with pandas
Create effective data visualizations with matplotlib and seaborn
Apply scikit-learn for classification, regression, and clustering
Analyze text data using NLP techniques in Python
Model and analyze social network structures with NetworkX
This course includes
200h
On-demand video
Yes
Certificate
Yes
Mobile access
English
Language
What it costs
Free to audit on Coursera; the certificate needs a subscription (about $49 a month), with financial aid available.
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Issued through Coursera with the University of Michigan; the projects and skills matter most.
Instructor
CB
Christopher Brooks / Kevyn Collins-Thompson
Coursera instructor
470K+ learners5 courses4.5 instructor rating
Taught by Christopher Brooks and Kevyn Collins-Thompson, University of Michigan School of Information faculty specializing in applied data science and information retrieval.