We may earn an affiliate commission when you visit our partners.
Christopher Brooks
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/8o4z7s/applied
Two deals to help you save
We found two deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
What's inside
Syllabus
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.
Read more
Syllabus
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.
Read more
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.
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.
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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces information visualization basics with a focus on reporting & charting using matplotlib
Emphasizes design & information literacy, exploring effective visualization principles
Provides hands-on practice with matplotlib, guiding learners in creating basic charts & realizing design decisions
Covers a range of basic statistical charts, helping learners identify appropriate methods for different problems
Exposes learners to various forms of data structuring & visualization, broadening their understanding
Recommends taking Introduction to Data Science in Python before this course
Save this course
Save Applied Plotting, Charting & Data Representation in Python to your list so you can find it easily later:
Save
Save
Reviews summary
Applied plotting, charting & data representation
Learners say that "Applied Plotting, Charting & Data Representation in Python" is an engaging course for beginners who want to learn more about data visualization. The course covers a wide range of topics from the basics of Matplotlib and Seaborn to more advanced charting techniques. The assignments are challenging but rewarding and the peer-reviewed feedback system helps learners improve their work. However, some learners found the lectures to be too short and superficial and felt that they had to do a lot of research on their own to complete the assignments.
The assignments are challenging and time-consuming, but they are also a great way to learn the material. The peer-reviewed feedback system is helpful for improving your work.
"It's normally very time-consuming, but it's also very rewarding. The assignments were fun to do and very beneficial to my learning."
"It could be challenging if you are willing to dig into fancier plotting options. But in the end, I had a super satisfying experience, 5 ⭐s! Looking back at the projects, I was amazed how I accomplished them!."
"This course requires you to look up the material yourself. Although the lectures provide a baseline, they are not at all useful for understanding matplotlib. However I find this useful because I did the research and was able to learn myself."
The instructor is an experienced data scientist and researcher. He is passionate about teaching and makes the material easy to understand.
"Dr Brooks, himself an eminent researcher in Data Science himself, has made a well-laid out plan towards the data science learning track."
"The theory behind each application is also explained appropriately for better understanding of each topic."
The assignments are practical andhands-on. They force you to apply the concepts you learn in the lectures.
"The assignments are really challenging and force you to work hard."
"The final assignment give opportunity to do a little independent data gathering / wrangling / visualisation mini-project."
"It’s up to you."
The course is well-structured and organized. The lectures are short and to the point, and the assignments are challenging but manageable.
"The course is well structured and high quality lectures."
"The course is well structured. I went through online search to understand all details mentioned in video, moreover I gained knowledge by looking at the assignment and solving myself with the help of stack overflow."
"The course is extraordinary. The videos, quizzes and assignments are pretty fun to dive into and, this is the first course in which I see Peer Assignment (because it makes sense) and it was great to learn with other people from all around the world!"
You will need to do a lot of research on your own to complete the assignments. This can be frustrating at times, but it is also a good way to learn.
"The course teaches you about important plotting and design skills but the peer reviews and lack of clarity for the assignments makes this class less useful than it could be."
"I thought the video lecture did not go into enough depth on how to manipulate matplotlib (eg. ax objects and ticks markers are still not clear) and even in general, the explanations were lacking."
"It would be helpful if the course is modified in a similar style to Python courses."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Applied Plotting, Charting & Data Representation in Python with these
activities:
Create a comprehensive study guide
Show steps
Enhance your understanding and retention of course materials by creating a comprehensive study guide.
Browse courses on
Study Guide
Show steps
-
Gather all course materials, including slides, notes, and assignments.
-
Organize the materials into logical sections.
-
Summarize key concepts and definitions.
-
Include practice questions and exercises.
Become a mentor for aspiring data visualization enthusiasts
Show steps
Reinforce your knowledge and help others by mentoring aspiring data visualization learners.
Browse courses on
Mentoring
Show steps
-
Join online forums or platforms where beginners seek guidance.
-
Offer your help and provide support to those with questions.
-
Share your knowledge and experiences in data visualization.
Participate in peer review sessions
Show steps
Improve your visualization skills by receiving and providing feedback from peers.
Browse courses on
Data Visualization
Show steps
-
Find a study partner or group.
-
Share your visualizations with each other.
-
Provide constructive feedback on the design, clarity, and impact of the visualizations.
-
Incorporate feedback into your visualizations.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create an interactive data visualization dashboard
Show steps
Develop your skills in creating engaging and informative data visualizations by building a dashboard.
Browse courses on
Data Visualization
Show steps
-
Gather data from various sources and clean the data.
-
Design the dashboard layout and choose appropriate charts.
-
Implement the dashboard using matplotlib and Jupyter Notebook.
-
Test the dashboard's functionality and make necessary adjustments.
Explore advanced charting techniques using Seaborn
Show steps
Enhance your data visualization skills by learning and applying advanced techniques with the Seaborn library.
Browse courses on
Data Visualization
Show steps
-
Install and import the Seaborn library.
-
Follow tutorials on advanced Seaborn charts such as heatmaps, histograms, and scatterplots.
-
Create visualizations using Seaborn's built-in datasets or your own data.
Participate in data visualization competitions
Show steps
Challenge yourself and showcase your skills by participating in data visualization competitions.
Browse courses on
Data Visualization
Show steps
-
Find relevant data visualization competitions.
-
Download the data and explore the problem statement.
-
Create compelling visualizations to address the problem statement.
-
Submit your visualizations and receive feedback from experts.
Build a data visualization portfolio
Show steps
Showcase your data visualization skills and enhance your employability by creating a portfolio.
Browse courses on
Data Visualization
Show steps
-
Collect your best data visualizations from assignments and projects.
-
Create a website or online gallery to display your portfolio.
-
Write brief descriptions and provide context for each visualization.
Create a comprehensive study guide
Show steps
Enhance your understanding and retention of course materials by creating a comprehensive study guide.
Browse courses on
Study Guide
Show steps
Show steps
Show steps
- Gather all course materials, including slides, notes, and assignments.
- Organize the materials into logical sections.
- Summarize key concepts and definitions.
- Include practice questions and exercises.
Become a mentor for aspiring data visualization enthusiasts
Show steps
Reinforce your knowledge and help others by mentoring aspiring data visualization learners.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Join online forums or platforms where beginners seek guidance.
- Offer your help and provide support to those with questions.
- Share your knowledge and experiences in data visualization.
Participate in peer review sessions
Show steps
Improve your visualization skills by receiving and providing feedback from peers.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Find a study partner or group.
- Share your visualizations with each other.
- Provide constructive feedback on the design, clarity, and impact of the visualizations.
- Incorporate feedback into your visualizations.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create an interactive data visualization dashboard
Show steps
Develop your skills in creating engaging and informative data visualizations by building a dashboard.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Gather data from various sources and clean the data.
- Design the dashboard layout and choose appropriate charts.
- Implement the dashboard using matplotlib and Jupyter Notebook.
- Test the dashboard's functionality and make necessary adjustments.
Explore advanced charting techniques using Seaborn
Show steps
Enhance your data visualization skills by learning and applying advanced techniques with the Seaborn library.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Install and import the Seaborn library.
- Follow tutorials on advanced Seaborn charts such as heatmaps, histograms, and scatterplots.
- Create visualizations using Seaborn's built-in datasets or your own data.
Participate in data visualization competitions
Show steps
Challenge yourself and showcase your skills by participating in data visualization competitions.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Find relevant data visualization competitions.
- Download the data and explore the problem statement.
- Create compelling visualizations to address the problem statement.
- Submit your visualizations and receive feedback from experts.
Build a data visualization portfolio
Show steps
Showcase your data visualization skills and enhance your employability by creating a portfolio.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Collect your best data visualizations from assignments and projects.
- Create a website or online gallery to display your portfolio.
- Write brief descriptions and provide context for each visualization.
Career center
Learners who complete Applied Plotting, Charting & Data Representation in Python will develop knowledge and skills
that may be useful to these careers:
Data Visualization Specialist
Data Visualization Specialist
A Data Visualization Specialist is responsible for creating visualizations that communicate data insights to a wide range of audiences. The course will help you develop the skills necessary to become a successful Data Visualization Specialist, including data analysis, data visualization, and design.
Data Analyst
Data Analyst
A Data Analyst uses data to identify trends, patterns, and insights that can help a company improve its operations. The course will help you develop the skills necessary to become a successful Data Analyst, including data cleaning, data analysis, and data visualization.
Data Scientist
Data Scientist
A Data Scientist takes in raw data and transforms it into meaningful insights that can help a company understand its customers, make better decisions, and improve its operations. The course will help you develop the skills necessary to become a successful Data Scientist, including data cleaning, data analysis, and data visualization.
Information Systems Manager
Information Systems Manager
An Information Systems Manager is responsible for the planning, implementation, and management of an organization's information systems. The course will help you develop the skills necessary to become a successful Information Systems Manager, including data management, data analysis, and data visualization.
Business Analyst
Business Analyst
A Business Analyst uses data to identify and solve business problems. The course will help you develop the skills necessary to become a successful Business Analyst, including data gathering, data analysis, and data visualization.
Data Engineer
Data Engineer
A Data Engineer is responsible for the design, construction, and maintenance of data pipelines. The course will help you develop the skills necessary to become a successful Data Engineer, including data management, data analysis, and data visualization.
Database Administrator
Database Administrator
A Database Administrator is responsible for the maintenance and administration of an organization's databases. The course will help you develop the skills necessary to become a successful Database Administrator, including data management, data analysis, and data visualization.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses data to improve the efficiency of operations. The course will help you develop the skills necessary to become a successful Operations Research Analyst, including data analysis, data visualization, and modeling.
Statistician
Statistician
A Statistician uses data to make inferences about a population. The course will help you develop the skills necessary to become a successful Statistician, including data analysis, data visualization, and probability.
Market Researcher
Market Researcher
A Market Researcher uses data to understand the needs and wants of customers. The course will help you develop the skills necessary to become a successful Market Researcher, including data gathering, data analysis, and data visualization.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. The course will help you develop the skills necessary to become a successful Software Engineer, including data structures, algorithms, and software development.
Actuary
Actuary
An Actuary uses mathematics and statistics to assess risk. The course will help you develop the skills necessary to become a successful Actuary, including data analysis, data visualization, and probability.
Web Developer
Web Developer
A Web Developer designs and develops websites. The course will help you develop the skills necessary to become a successful Web Developer, including HTML, CSS, and JavaScript.
Financial Analyst
Financial Analyst
A Financial Analyst uses data to make investment decisions. The course will help you develop the skills necessary to become a successful Financial Analyst, including data analysis, data visualization, and finance.
Computer Scientist
Computer Scientist
A Computer Scientist researches and develops new computer technologies. The course will help you develop the skills necessary to become a successful Computer Scientist, including data structures, algorithms, and computer architecture.
For more career information including salaries, visit:
OpenCourser.com/course/8o4z7s/applied
Reading list
We've selected 25 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Applied Plotting, Charting & Data Representation in Python.
This classic book on data visualization must-read for anyone who wants to learn more about the principles of effective visual communication. Tufte provides a wealth of insights into how to design charts and graphs that are both informative and visually appealing.
Provides a comprehensive overview of the principles and best practices of data visualization. It covers a wide range of topics, including data structures, visual perception, and design principles.
Save
This classic book on data visualization provides a wealth of practical advice on how to create effective charts and graphs. It must-read for anyone who wants to learn how to communicate data clearly and effectively.
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone who wants to learn how to use deep learning for data science.
Save
Provides a comprehensive guide to using the ggplot2 package in R for data visualization. ggplot2 powerful and versatile tool for creating a wide variety of charts and graphs, and this book provides clear and concise instructions on how to use it effectively.
Provides a comprehensive overview of the field of information visualization, covering both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about the cognitive and perceptual factors that influence how people interact with visualizations.
Provides a formal grammar for creating graphics. It valuable resource for anyone who wants to learn more about the underlying principles of data visualization.
Provides a comprehensive guide to using D3.js for data visualization. D3.js powerful library for creating interactive and visually appealing visualizations.
Save
Provides a comprehensive overview of the field of data visualization, covering both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about how to create visualizations that are both informative and visually appealing.
Provides a practical guide to choosing the right chart type for your data. It valuable resource for anyone who wants to learn more about how to create visualizations that are both informative and visually appealing.
Provides a comprehensive overview of the principles of information visualization, covering topics such as visual perception, design principles, and evaluation methods. It valuable resource for anyone interested in learning more about the theory and practice of information visualization.
Save
Provides a thought-provoking look at the role of data visualization in communication. It explores the ethical and aesthetic considerations that go into creating effective data visualizations.
Provides a comprehensive overview of the theory and practice of statistical learning. It covers a wide range of topics, including regression, classification, and clustering. It valuable resource for anyone who wants to learn more about the statistical foundations of data science.
Provides a comprehensive overview of machine learning for data science. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning. It valuable resource for anyone who wants to learn how to use machine learning for data science.
Save
Provides a comprehensive overview of data visualization techniques, with a focus on best practices for presenting data in a clear and effective way. It covers a wide range of topics, from basic chart types to more advanced techniques such as interactive visualizations and dashboards.
Provides a practical guide to using Python and the matplotlib library for data visualization. It covers a wide range of topics, from basic chart types to more advanced techniques such as interactive visualizations and dashboards.
Provides a comprehensive introduction to the matplotlib library, which is used for creating charts and graphs in Python. It valuable resource for anyone who wants to learn how to use matplotlib to visualize data.
Provides a practical guide to choosing the right chart for every data story. It covers a wide range of chart types and provides advice on how to use them effectively.
Provides a practical guide to creating data visualizations using Python and JavaScript. It covers a wide range of topics, including data preparation, visualization techniques, and interactive visualizations.
Save
Provides a practical guide to data visualization, with a focus on using simple and effective techniques to communicate data insights. It valuable resource for anyone who wants to learn more about how to create visualizations that are both informative and visually appealing.
Provides a collection of essays from experts in the field of data visualization. It covers a wide range of topics, from the history of data visualization to the latest trends and techniques.
Provides a comprehensive overview of data science techniques using Python. It covers a wide range of topics, from data cleaning and preparation to machine learning and deep learning. While not specifically focused on data visualization, it provides a solid foundation for understanding the underlying principles.
Save
Provides a comprehensive guide to using the R programming language for data science. It covers a wide range of topics, from data cleaning and preparation to machine learning and deep learning. While not specifically focused on data visualization, it provides a solid foundation for understanding the underlying principles.
Save
Provides a gentle introduction to data visualization, covering the basics of creating charts and graphs in Python and R. It good choice for beginners who want to learn the fundamentals of data visualization.
Save
Provides a simple and practical guide to data visualization. It covers the basics of creating charts and graphs, as well as more advanced techniques for telling stories with data.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8o4z7s/applied
Share
Similar courses
Here are nine courses similar to
Applied Plotting, Charting & Data Representation in Python.
Statistical Data Visualization in Python
OpenCourser.com/course/l1skzd/statistical
Statistical Data Visualization in Python
Most relevant
Complete Course on Data Visualization, Matplotlib and Python
OpenCourser.com/course/4hu30v/complete
Complete Course on Data Visualization, Matplotlib and...
Most relevant
Building Data Visualizations Using Matplotlib
OpenCourser.com/course/54fidj/building
Building Data Visualizations Using Matplotlib
Most relevant
Data Understanding and Visualization
OpenCourser.com/course/4081kv/data
Data Understanding and Visualization
Most relevant
Build Your First Data Visualization with Altair 4
OpenCourser.com/course/pghwqz/build
Build Your First Data Visualization with Altair 4
Most relevant
Build Your First Data Visualization with Matplotlib
OpenCourser.com/course/i95dmo/build
Build Your First Data Visualization with Matplotlib
Most relevant
Understanding and Visualizing Data with Python
OpenCourser.com/course/2qy9zc/understanding
Understanding and Visualizing Data with Python
Most relevant
Plotting Data with Pandas
OpenCourser.com/course/oxzq9a/plotting
Plotting Data with Pandas
Most relevant
Communicating Data Insights
OpenCourser.com/course/jhjlg2/communicating
Communicating Data Insights
Most relevant
Level
Intermediate
Via
Coursera
Institution
University of Michigan
Instructor
Christopher Brooks
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
magyar nyelv
Bahasa Indonesia
ไทย
қазақша
اُردُو,
فارسی
বাংলা
پښتو
آذربایجان دیلی
This course belongs to a
collection
called
Applied Data Science with Python. It's comprised of five courses:
- Applied Text Mining in Python
- Introduction to Data Science in Python
- Applied Social Network Analysis in Python
- Applied Machine Learning in Python
- Applied Plotting, Charting & Data Representation in Python (viewing)
Good to know
Introduces information visualization basics with a focus on reporting & charting using matplotlib
Emphasizes design & information literacy, exploring effective visualization principles
Provides hands-on practice with matplotlib, guiding learners in creating basic charts & realizing design decisions
Covers a range of basic statistical charts, helping learners identify appropriate methods for different problems
Exposes learners to various forms of data structuring & visualization, broadening their understanding
Recommends taking Introduction to Data Science in Python before this course
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser