Welcome to the Data visualization competency course material for ITSE-1302 Computer Programming: Scientific Python 1 at Austin Community College in Austin, TX. Click here for a course overview.
The college website for this course is: http://www.austincc.edu/baldwin/
This course does not use a conventional paper or electronic textbook. Instead, this online study guide and the Blackboard learning management system will guide you through a variety of free online resources on topics that you will need to learn in order to succeed in the course.
The course is structured into four major units: one review unit and three competency units.
This is the web page for the competency titled Data visualization using Matplotlib. See the other pages in this online study guide for material that deals with the other competencies.
This is one of three competencies in this course. There is a lot of material to cover and not much time to cover it. Therefore, in the interest of time, instruction for this competency will be limited to the following basic topics:
The matplotlib gallery provides dozens of different data visualization examples along with the code required to produce those visualizations. You are encouraged to visit that website and take advantage of what you can learn by examining the images and the corresponding code.
Data visualization is a key competency for data science and analytics. One of the best and most comprehensive free online resources is Chapter 4. Visualization with Matplotlib. However, this is a free preview of an online Safari book and could be taken down at any time. Therefore, you may find it useful to download and save the resource if it is still available when you click the above link.
Other good resources are listed below. Some of these resources contain links to other resources.
The free Udacity course titled Intro to Data Analysis also covers the Python libraries NumPy, Pandas, and Matplotlib.
The following free edX courses contain material on using Matplotlib:
DataCamp provides an apparently free Matplotlib module as part of its paid course titled Intermediate Python for Data Science.
There may also be some available courses that teach Matplotlib at Coursera. However, those courses usually have defined start and end times that don't coincide with the start and end times of a regular semester at ACC.
The resources listed above are simply examples of the many free online resources on Matplotlib that you will find with an Internet search.
The following web pages were developed specifically for this course. They provide many visualization programming examples and exercises designed to help you learn how to create data visualizations using Matplotlib.
You are encouraged to study these programming examples and exercises in parallel with your study of data visualization. All of the homework assignments for this competency will deal with some aspect of data visualization.
The pages listed above were developed using Jupyter Notebook in its interactive mode and then downloaded as static HTML files for inclusion in this course material. If you are unfamiliar with the format of Jupyter Notebook, a quick (approximately 19 minutes) tour of the following three videos will teach you everything you need to know to understand the format of the pages listed above.
Assessments such as assignments, quizzes, and tests will be administered through Blackboard. Some of the free online resources may also include graded assessments such as exercises and tests. You are encouraged to take advantage of those exercises and tests to enhance your ability to learn and retain the material. However, grades and credits associated with those resources will not be integrated into your grade for this course. Your grade for this course will be based solely on your grades on assignments, quizzes, and tests administered by your ACC instructor through Blackboard.
A set of review questions for this competency is provided here. The questions are similar to the questions that you will find on the test for this competency. Therefore, it is strongly recommended that you study the material until you thoroughly understand the material covered by those questions.
Author: Prof. Richard G. Baldwin
Affiliation: Professor of
Computer Information Technology at Austin Community College in
Austin, TX.
File: Visualization.htm
Revised: 04/24/18
Copyright 2018, Richard G. Baldwin
-end