About this course: 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.
Created by: University of Illinois at Urbana-Champaign
Taught by: John C. Hart, Professor of Computer Science
Department of Computer Science
| Basic Info |
Course 1 of 6 in the
Data Mining Specialization.
|
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
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.
6 readings
Graded: Orientation Quiz
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.
15 videos, 2 readings
Graded: Week 1 Quiz
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...
11 videos, 3 readings
Graded: Programming Assignment 1
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.
8 videos, 3 readings
Graded: Programming Assignment 2
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...
9 videos, 1 reading
Graded: Week 4 Quiz
FAQs
How It Works
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
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University of Illinois at Urbana-Champaign
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
Pricing
| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
Ratings and Reviews
I really enjoyed this course. It is a very good source for learning how to represent numerical and relational data in a way that conveys information effectively. I also used this course as a platform to learn javascript in order to solve the assignments using the amazing library D3.js.
Good course. The section on graphs could use more detail on how the layout algorithms affect usability of data visualizations.
this is my first course from coursera and its very experance after taken this course
i an very bringnner this course help me lot to take further path towords the data science and data mining
thanks sir
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Very nice overview for the subject. Great introduction that focuses for every kind of professional interested in data visualization (eg.: programmers and designers, analysts and decision makers). I really appreciate the projects and the peer-review model. It was an amazing challenge and I definitely will take the sequence of Data Visualization courses. Thanks Coursera :)