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

4.5
stars
6,136 ratings

Christopher Brooks

177,834 already enrolled

Offered By

Associated with the Master of Applied Data Science degree
Learn More

Applied Data Science with Python SpecializationUniversity of Michigan

About this Course

100,002 recent views

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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 5 in the

Applied Data Science with Python Specialization

Intermediate Level

Approx. 24 hours to complete

English

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 Programming
Data Virtualization
Data Visualization (DataViz)
Matplotlib

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 5 in the

Applied Data Science with Python Specialization

Intermediate Level

Approx. 24 hours to complete

English

Instructor

Instructor rating

4.46/5 (454 Ratings)

Christopher Brooks

Assistant Professor

School of Information

786,056 Learners

14 Courses

Offered by

University of Michigan

Start working towards your Master's degree

This course is related to the 100% online Master of Applied Data Science from University of Michigan. It will provide you with a preview of the topics, materials and instructors so you can decide if the full online degree program is right for you.

Learn More

Syllabus - What you will learn from this course

Content Rating93%(10,389 ratings)

Week1

Week 1

4 hours to complete

Module 1: Principles of Information Visualization

4 hours to complete

8 videos (Total 38 min), 6 readings, 2 quizzes

8 videos

Introduction4mUpdates1mAbout the Professor: Christopher Brooks1mTools for Thinking about Design (Alberto Cairo)8mGraphical heuristics: Data-ink ratio (Edward Tufte)4mGraphical heuristics: Chart junk (Edward Tufte)5mGraphical heuristics: Lie Factor and Spark Lines (Edward Tufte)3mThe Truthful Art (Alberto Cairo)8m

6 readings

Syllabus10mHelp us learn more about you!10mNotice for Coursera Learners: Assignment Submission10mDark Horse Analytics (Optional)10mUseful Junk?: The Effects of Visual Embellishment on Comprehension and Memorability of Charts30mGraphics Lies, Misleading Visuals10m

Week2

Week 2

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 hours to complete

7 videos (Total 60 min), 2 readings, 1 quiz

Week3

Week 3

9 hours to complete

Module 3: Charting Fundamentals

9 hours to complete

6 videos (Total 65 min), 3 readings, 2 quizzes

Week4

Week 4

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.

5 hours to complete

4 videos (Total 31 min), 2 readings, 1 quiz

Reviews

4.5

1035 reviews

5 stars
66.89%
4 stars
23.33%
3 stars
6.27%
2 stars
1.98%
1 star
1.51%

TOP REVIEWS FROM APPLIED PLOTTING, CHARTING & DATA REPRESENTATION IN PYTHON

by ELOct 1, 2017

it is a good course to help me have a glance to the data visualization area. However, I think I cannot learned a lot from the course and the homework is so easy that I haven't practice enough.

by AKJul 4, 2018

Practical course with hands on exercise to make you well versed in Applied Plotting, Charting & Data Representation in Python. I recommend at least every college student should experience this course

by TASep 12, 2020

Nice course to study more aplicable to become data science. this provide any basic ploting that give insight and knowledge how to build data visualization more efectively and insightfull

by RCJun 18, 2020

It was a great learning experience as an individual is forced to explore all the official documentations of plotting and charting.The assignments were also very versatile .Loved the course!

View all reviews

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.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Applied Plotting, Charting & Data Representation in Python

About this Course

What you will learn

Skills you will gain