We are living in the age of big data. A huge volume of data of various types are generated at a greater speed than ever before. It's estimated that 2.3 trillion gigabytes of data are generated each day, 2.9 millions of emails are sent every second, and connected cars produce 25,000 megabytes of data per hour. To benefit from this huge amount of data, it is necessary to analyze the data and understand it, or in other words, to "mine" the data. This course introduces students to the process and main techniques in data mining, including basic visulization and exploratory analysis, predictive modeling, descriptive modeling, and pattern mining approaches.

Course Logistics

  • Time:
    Tuesday & Thursday 4:30-5:45pm

  • Location:
    WANG 2599

  • Instructor:
    Ming Yin

  • Office Hour:
    Wednesday 4-5pm

  • Course Email: datamining.purdue@gmail.com

What you will learn?

This course will help you...

  • understand the basic data mining process and identify key elements of data mining algorithms
  • recognize different types of data mining tasks
  • implement and apply basic algorithm and standard models
  • understand how to evaluate performance, as well as formulate and test hypotheses