About this Course

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Learner Career Outcomes

36%

started a new career after completing these courses

38%

got a tangible career benefit from this course

14%

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Shareable Certificate
Earn a Certificate upon completion
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Flexible deadlines
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Intermediate Level
Approx. 24 hours to complete
English
Subtitles: English, Korean

What you will learn

  • Describe what makes a good or bad visualization

  • Understand best practices for creating basic charts

  • Identify the functions that are best for particular problems

  • Create a visualization using matplotlb

Skills you will gain

Python ProgrammingData VirtualizationData Visualization (DataViz)Matplotlib

Learner Career Outcomes

36%

started a new career after completing these courses

38%

got a tangible career benefit from this course

14%

got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 24 hours to complete
English
Subtitles: English, Korean

Instructor

Offered by

University of Michigan logo

University of Michigan

Syllabus - What you will learn from this course

Content RatingThumbs Up93%(6,419 ratings)Info
Week
1

Week 1

5 hours to complete

Module 1: Principles of Information Visualization

5 hours to complete
7 videos (Total 37 min), 6 readings, 2 quizzes
7 videos
About the Professor: Christopher Brooks1m
Tools for Thinking about Design (Alberto Cairo)8m
Graphical heuristics: Data-ink ratio (Edward Tufte)4m
Graphical heuristics: Chart junk (Edward Tufte)5m
Graphical heuristics: Lie Factor and Spark Lines (Edward Tufte)3m
The Truthful Art (Alberto Cairo)8m
6 readings
Syllabus10m
Help us learn more about you!10m
Notice for Coursera Learners: Assignment Submission10m
Dark Horse Analytics (Optional)10m
Useful Junk?: The Effects of Visual Embellishment on Comprehension and Memorability of Charts30m
Graphics Lies, Misleading Visuals10m
Week
2

Week 2

7 hours to complete

Module 2: Basic Charting

7 hours to complete
7 videos (Total 42 min), 2 readings, 1 quiz
7 videos
Matplotlib Architecture6m
Basic Plotting with Matplotlib7m
Scatterplots8m
Line Plots8m
Bar Charts4m
Dejunkifying a Plot3m
2 readings
Matplotlib30m
Ten Simple Rules for Better Figures30m
Week
3

Week 3

8 hours to complete

Module 3: Charting Fundamentals

8 hours to complete
6 videos (Total 39 min), 2 readings, 2 quizzes
6 videos
Histograms9m
Box Plots7m
Heatmaps3m
Animation5m
Interactivity5m
2 readings
Selecting the Number of Bins in a Histogram: A Decision Theoretic Approach (Optional)10m
Assignment Reading10m
Week
4

Week 4

5 hours to complete

Module 4: Applied Visualizations

5 hours to complete
3 videos (Total 18 min), 2 readings, 1 quiz
3 videos
Seaborn8m
Becoming an Independent Data Scientist1m
2 readings
Spurious Correlations10m
Post-course Survey10m

Reviews

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About the Applied Data Science with Python Specialization

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate....
Applied Data Science with Python

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