Applied Plotting, Charting & Data Representation in Python

About 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 describe the gamut 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.

Who is this class for: This course is part of “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.

Created by:  University of Michigan

• Taught by:  Christopher Brooks

Basic Info Course 2 of 5 in the Applied Data Science with Python Specialization Level Intermediate Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.4 stars Average User Rating 4.4See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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Syllabus


WEEK 1


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, 5 readings expand

1. Video: Introduction
2. Reading: Syllabus
3. Reading: Help us learn more about you!
4. Video: About the Professor: Christopher Brooks
5. Video: Tools for Thinking about Design (Alberto Cairo)
6. LTI Item: Hands-on Visualization Wheel
7. Video: Graphical heuristics: Data-ink ratio (Edward Tufte)
8. Reading: Dark Horse Analytics (Optional)
9. Video: Graphical heuristics: Chart junk (Edward Tufte)
10. Reading: Useful Junk?: The Effects of Visual Embellishment on Comprehension and Memorability of Charts
11. Video: Graphical heuristics: Lie Factor and Spark Lines (Edward Tufte)
12. Video: The Truthful Art (Alberto Cairo)
13. Discussion Prompt: Must a visual be enlightening?
14. Reading: Graphics Lies, Misleading Visuals

Graded: Graphics Lies, Misleading Visuals

WEEK 2


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, 2 readings expand

1. Notebook: Module 2 Jupyter Notebook
2. Video: Introduction
3. Video: Matplotlib Architecture
4. Reading: Matplotlib
5. Reading: Ten Simple Rules for Better Figures
6. Video: Basic Plotting with Matplotlib
7. Video: Scatterplots
8. Video: Line Plots
9. Video: Bar Charts
10. Video: Dejunkifying a Plot
11. Notebook: Plotting Weather Patterns

Graded: Plotting Weather Patterns

WEEK 3


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, 2 readings expand

1. Notebook: Module 3 Jupyter Notebook
2. Video: Subplots
3. Video: Histograms
4. Reading: Selecting the Number of Bins in a Histogram: A Decision Theoretic Approach (Optional)
5. Video: Box Plots
6. Video: Heatmaps
7. Video: Animation
8. Video: Interactivity
9. Notebook: Practice Assignment: Understanding Distributions Through Sampling
10. Peer Review: Practice Assignment: Understanding Distributions Through Sampling
11. Notebook: Building a Custom Visualization
12. Reading: Assignment Reading

Graded: Building a Custom Visualization

WEEK 4


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, 2 readings expand

1. Notebook: Module 4 Jupyter Notebook
2. Video: Plotting with Pandas
3. Video: Seaborn
4. Reading: Spurious Correlations
5. Video: Becoming an Independent Data Scientist
6. Notebook: Project Description
7. Reading: Post-course Survey

Graded: Becoming an Independent Data Scientist