Applied Plotting, Charting & Data Representation in Python

Applied Plotting, Charting & Data Representation in Python

This course is part of Applied Data Science with Python Specialization

Instructor: Christopher Brooks

208,631 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

4.5

6,284 reviews

Intermediate level

Some related experience required

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

93%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

4.5

6,284 reviews

Intermediate level

Some related experience required

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

93%

Most learners liked this course

What you'll 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'll gain

Tools you'll learn

Python Programming

Details to know

Shareable certificate

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Taught in English

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This course is part of the Applied Data Science with Python Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 4 modules in this course

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.

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.

What's included

8 videos6 readings1 peer review1 app item1 discussion prompt

8 videos•Total 38 minutes

Introduction•4 minutes
Updates•1 minute
About the Professor: Christopher Brooks•1 minute
Tools for Thinking about Design (Alberto Cairo)•9 minutes
Graphical heuristics: Data-ink ratio (Edward Tufte)•5 minutes
Graphical heuristics: Chart junk (Edward Tufte)•5 minutes
Graphical heuristics: Lie Factor and Spark Lines (Edward Tufte)•4 minutes
The Truthful Art (Alberto Cairo)•9 minutes

6 readings•Total 80 minutes

Syllabus•10 minutes
Help us learn more about you!•10 minutes
Notice for Coursera Learners: Assignment Submission•10 minutes
Dark Horse Analytics (Optional)•10 minutes
Useful Junk?: The Effects of Visual Embellishment on Comprehension and Memorability of Charts•30 minutes
Graphics Lies, Misleading Visuals•10 minutes

1 peer review•Total 60 minutes

Graphics Lies, Misleading Visuals •60 minutes

1 app item•Total 30 minutes

Hands-on Visualization Wheel•30 minutes

1 discussion prompt•Total 10 minutes

Must a visual be enlightening?•10 minutes

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.

What's included

7 videos2 readings1 peer review2 ungraded labs

7 videos•Total 60 minutes

Introduction•2 minutes
Matplotlib Architecture•7 minutes
Basic Plotting with Matplotlib•10 minutes
Scatterplots•13 minutes
Line Plots•13 minutes
Bar Charts•7 minutes
Dejunkifying a Plot•9 minutes

2 readings•Total 60 minutes

Matplotlib•30 minutes
Ten Simple Rules for Better Figures•30 minutes

1 peer review•Total 180 minutes

Plotting Weather Patterns•180 minutes

2 ungraded labs•Total 120 minutes

Module 2 Jupyter Notebooks•60 minutes
Plotting Weather Patterns•60 minutes

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.

What's included

6 videos3 readings2 peer reviews3 ungraded labs

6 videos•Total 65 minutes

Subplots•15 minutes
Histograms•13 minutes
Box Plots•10 minutes
Heatmaps•8 minutes
Animation•7 minutes
Widget Demonstration•11 minutes

3 readings•Total 50 minutes

Selecting the Number of Bins in a Histogram: A Decision Theoretic Approach (Optional)•10 minutes
Assignment Reading•30 minutes
Understanding Error Bars•10 minutes

2 peer reviews•Total 240 minutes

Building a Custom Visualization •120 minutes
Practice Assignment: Understanding Distributions Through Sampling•120 minutes

3 ungraded labs•Total 180 minutes

Module 3 Jupyter Notebooks•60 minutes
Practice Assignment: Understanding Distributions Through Sampling•60 minutes
Building a Custom Visualization•60 minutes

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.

What's included

4 videos3 readings1 peer review2 ungraded labs

4 videos•Total 31 minutes

Plotting with Pandas•8 minutes
Seaborn•9 minutes
Mapping and Geographic Investigation•13 minutes
Becoming an Independent Data Scientist•2 minutes

3 readings•Total 23 minutes

Spurious Correlations•10 minutes
Post-course Survey•10 minutes
5 reasons to keep going•3 minutes

1 peer review•Total 120 minutes

Becoming an Independent Data Scientist•120 minutes

2 ungraded labs•Total 120 minutes

Module 4 Jupyter Notebooks•60 minutes
Project Description•60 minutes

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Instructor

Instructor ratings

4.5(493 ratings)

Christopher Brooks

15 Courses•968,364 learners

Offered by

University of Michigan

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Learner reviews

4.5

6,284 reviews

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Showing 3 of 6284

5·

Reviewed on Jun 10, 2020

Great course with lots of learning. The lectures were crisp and the course inspired us to look at materials beyond the course and in the internet which is a important skill for any data scientist

5·

Reviewed on Sep 2, 2020

Excellent Course as the previous one in this specialization. One of the things I liked is the skills gained in previous course are required to start the assignment. Very well designed as always

5·

Reviewed on Mar 5, 2018

Very helpful to understand what it takes to make a scientific and sensible visual. Recommended for someone who is interested in learning data visualization and does not have a background.

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