Key Information
About the content
Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for pattern-based classification and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.
Syllabus
- Week 1 - Course Orientation
You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course. - Week 1 - Week 1: The Computer and the Human
In this week's module, you will learn what data visualization is, how it's used, and how computers display information. You'll also explore different types of visualization and how humans perceive information. - Week 2 - Week 2: Visualization of Numerical Data
In this week's module, you will start to think about how to visualize data effectively. This will include assigning data to appropriate chart elements, using glyphs, parallel coordinates, and streamgraphs, as well as implementing principles of design and color... - Week 3 - Week 3: Visualization of Non-Numerical Data
In this week's module, you will learn how to visualize graphs that depict relationships between data items. You'll also plot data using coordinates that are not specifically provided by the data set. - Week 4 - Week 4: The Visualization Dashboard
In this week's module, you will start to put together everything you've learned by designing your own visualization system for large datasets and dashboards. You'll create and interpret the visualization you created from your data set, and you'll also apply te...
Instructors
John C. Hart
Professor of Computer Science
Department of Computer Science
Content Designer
Platform
Coursera is a digital company offering massive open online course founded by computer teachers Andrew Ng and Daphne Koller Stanford University, located in Mountain View, California.
Coursera works with top universities and organizations to make some of their courses available online, and offers courses in many subjects, including: physics, engineering, humanities, medicine, biology, social sciences, mathematics, business, computer science, digital marketing, data science, and other subjects.
The Course is excellent and gives good overview on the concepts and best practices of Data Visualization. The Assignments are quite interesting and help in enhancing your knowledge on the topic. Well Done !!
It's an intro course, so no qualms about that aside from that I missed having some intro d3.js assignments (that could be fully automated).The peer review system is unfortunately the weakest point of this. I got decent enough grades mind you, but it's sensitive to fluctuations in enrollment in a way that makes me weary.
The Course is excellent and gives good overview on the concepts and best practices of Data Visualization. The Assignments are quite interesting and help in enhancing your knowledge on the topic. Well Done !!
Pros:The course was well organized so that an individual can focus on coursework only. There is enough time to complete the homework and quizzes directly relate to the material (i.e. student is not expected to do much reading outside the course videos). The material is informative and accomplishes the goals stated in the beginning of the course.Cons:Some minor things such as not being able to see the programming assignments very well when grading other's work due to Coursera's UI not designed well for that. Also, the collaboration between students is not as good as it was made out to be based on the emphasis that was given to that by Coursera and course organizers. Not sure if Coursera or organizers can do much more than they have already regarding that.
Really nice course, finally got a chance to dig into some big datasets and see for myself what kind of challenges arise by trying to communicate information to users. I found it to be very useful first step toward getting deeper understanding on data mining.
Excellent Course. Give me a good understanding of how to visualize data to make it clear and fit the user's need. And the assignments is also useful. I learned how to use D3 to generate the data using Javascript and D3 library.
