Applied Data Science with Python Specialization

This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.

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Intermediate Level

English

Subtitles: English, Korean

What you will learn

Create a visualization using matplotlb

Describe what makes a good or bad visualization

Identify the functions that are best for particular problems

Understand best practices for creating basic charts

Skills you will gain

Data VisualizationPython ProgrammingPandasData Analysis

100% online courses

Start instantly and learn at your own schedule.

Intermediate Level

English

Subtitles: English, Korean

Syllabus - What you will learn from this course

1

Section

5 hours to complete

Module 1: Principles of Information Visualization

In this module, you will get an introduction to principles of information visualization. We will be introduced to tools for thinking about design and graphical heuristics for thinking about creating effective visualizations. All of the course information on grading, prerequisites, and expectations are on the course syllabus, which is included in this module. ...

7 videos (Total 37 min), 6 readings, 2 quizzes

7 videos

Introduction4m

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

2

Section

7 hours to complete

Module 2: Basic Charting

In this module, you will delve into basic charting. For this week’s assignment, you will work with real world CSV weather data. You will manipulate the data to display the minimum and maximum temperature for a range of dates and demonstrate that you know how to create a line graph using matplotlib. Additionally, you will demonstrate the procedure of composite charts, by overlaying a scatter plot of record breaking data for a given year....

7 videos (Total 42 min), 2 readings, 1 quiz

7 videos

Introduction1m

Matplotlib Architecture6m

Basic Plotting with Matplotlib7m

Scatterplots8m

Line Plots8m

Bar Charts4m

Dejunkifying a Plot3m

2 readings

Matplotlib30m

Ten Simple Rules for Better Figures30m

3

Section

8 hours to complete

Module 3: Charting Fundamentals

In this module you will explore charting fundamentals. For this week’s assignment you will work to implement a new visualization technique based on academic research. This assignment is flexible and you can address it using a variety of difficulties - from an easy static image to an interactive chart where users can set ranges of values to be used....

6 videos (Total 39 min), 2 readings, 2 quizzes

6 videos

Subplots7m

Histograms9m

Box Plots7m

Heatmaps3m

Animation5m

Interactivity5m

2 readings

Selecting the Number of Bins in a Histogram: A Decision Theoretic Approach (Optional)10m

Assignment Reading10m

4

Section

5 hours to complete

Module 4: Applied Visualizations

In this module, then everything starts to come together. Your final assignment is entitled “Becoming a Data Scientist.” This assignment requires that you identify at least two publicly accessible datasets from the same region that are consistent across a meaningful dimension. You will state a research question that can be answered using these data sets and then create a visual using matplotlib that addresses your stated research question. You will then be asked to justify how your visual addresses your research question....

3 videos (Total 18 min), 2 readings, 1 quiz

3 videos

Plotting with Pandas7m

Seaborn8m

Becoming an Independent Data Scientist1m

2 readings

Spurious Correlations10m

Post-course Survey10m

Instructor

Christopher Brooks

About 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....

Frequently Asked Questions

When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I pay for this course?
If you pay for this course, you will have access to all of the features and content you need to earn a Course Certificate. If you complete the course successfully, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. Note that the Course Certificate does not represent official academic credit from the partner institution offering the course.
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Applied Data Science with Python Specialization