Right after I made the decision to make a better living and apply for
MS/PhD track outside China, I signed up for THE specialization on
Coursera provided by UIUC. This is a 6-course specilization and it’s
named Data Mining. First of the series is Data Visualization.
Data Visualization is a course that teaches how to create
visualizations that effectively communicate the meaning behind data to
an observer through visual perception. We will learn how a computer
displays information using computer graphics, and how the human
perceives that information visually. We will also study the forms of
data, including quantitative and non-quantitative data, and how they
are properly mapped to the elements of a visualization to be perceived
well by the observer. We will briefly overview some design elements
for effective visualization, though we will not focus on the visual
design needed to make attractive and artistic visualizations. This
course does not require computer programming, but computer programming
can be used to complete the exercises. The course will conclude with
the integration of visualization into database and data-mining systems
to provide support for decision making, and the effective construction
of a visualization dashboard.
Upon successful completion of this course, you will be able to:
Describe how 2-D and 3-D computer graphics are used to visualize data.
Describe how an observer perceives and processes information from a visual display.
Utilize a wide vocabulary of visualization methods and how best to apply them to different kinds of data.
Decide which design styles and colors work best for different visualization situations.
Visualize data when it is not numerical.
Use techniques for visualizing databases and data mining to help visually sort through massive datasets.
Analyze tasks and build visualization dashboards to provide data to support making a decision.
The course consists of 4 weekly modules that focus on the following.
Week 1: The Computer and the Human
Introduction to visualization
Using computer graphics to display data
The model human processor and Fitts’s law
Human visual perception and cognition
Week 2: Visualization of Numerical Data
Different kinds of visualizations and how best to apply them to data
Basic charts such as bar charts and scatter plots
More advanced visualization techniques, such as streamgraphs and parallel coordinates
Some elements of design and color usage
Week 3: Visualization of Non-Numerical Data
Graphs, networks, and hierarchies
Layout of relational and hierarchical data, such as treemaps
Methods for visualizing high-dimensional data, such as principal component analysis and multidimensional scaling
Week 4: The Visualization Dashboard
Visualizing large datasets
Visualization of databases and data mining results
Visual analytics for decision support
The course is comprised of the following elements:
Lecture videos. In each module the concepts you need to know will
be presented through a collection of short video lectures. You may
stream these videos for playback within the browser by clicking on
their titles or download the videos. You may also download the
slides that go along with the videos.
Quizzes. Week 1 and Week 4 will include a for-credit quiz. You
will be allowed 3 attempts at the quiz per every 8 hours. Each
attempt may present a different selection of questions to
you. Your best score will be used when calculating your final
score in the class. There is no time limit on how long you take to
complete each attempt at the quiz.
Programming assignments. There are two required programming
assignments for the class. The first programming assignment is to
create a visualization of numerical data, and the second
programming assignment is to create a visualization of
non-numerical data (e.g., a network or a hierarchy). For each
assignment, sample data will be provided, but you are encouraged
to find your own data. Your goal will be to present that data in a
visualization that helps the observer to better understand what
the data represents. The programming assignments will be peer
graded based on rubrics that measure how well the course’s methods
have been applied to the visualization of the data.
To do well this week, I recommend that you do the following:
Review the video lectures a number of times to gain a solid understanding of the key questions and concepts introduced this week.
When possible, provide tips and suggestions to your peers in this class. As a learning community, we can help each other learn and grow. One way of doing this is by helping to address the questions that your peers pose. By engaging with each other, we閳ユ獟l all learn better.
It閳ユ獨 always a good idea to refer to the video lectures and chapter readings we’ve read during this week and reference them in your responses. When appropriate, critique the information presented.
Take notes while you read the materials and watch the lectures for this week. By taking notes, you are interacting with the material and will find that it is easier to remember and to understand. With your notes, you閳ユ獟l also find that it閳ユ獨 easier to complete your assignments. So, go ahead, do yourself a favor; take some notes!
- Model human processor