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CS412

CS412 (Introduction to Data Mining) is a 3/4-credit-hour course that satisfies the Technical Electives requirement for ECE majors and satisfies and Advanced Computing Elective for CEs. It is offered in both fall and spring semesters.

Content Covered

  • Data management/preprocessing and data warehousing
  • OLAP technology and data cubes
  • Classification and clustering
  • Data set algorithms
  • Pattern Mining

This class starts off with basic data management/preprocessing techniques and builds into Data Warehousing. Warehousing techniques are covered in some detail, and the advantages of setting these up are also compared to other ways of storing data. Then the course digs deeper into OLAP technology such as data cubes, where the different ways of parsing the data cube to use stored information, as well as optimizations to these algorithms, are covered in great detail. It then proceeds with frequent pattern mining, classification and clustering. Many algorithms for each of these are covered in great detail, and they are compared to understand exactly why some algorithms are better suited for some data sets. These are all widely used techniques beneficial to anyone dealing with data analysis. The latter half of the class, which includes pattern mining, classification and clustering, is more interesting, but the level of difficulty was also much higher.

Prerequisites

CS225 (Data Structures) is the only listed prerequisite for this course, and it is definitely required to understand the many algorithms covered in this course; a good understanding of data structures is important to be able to do well in this class. Any probability/statistics courses would be a great help too - usually the ones required by the curriculum are more than enough - MATH461 (Probability Theory), STAT400 (Statistics and Probability I) or ECE313 (Probability with Engineering Applications). These skills help to keep up with the course content.

When to Take It

This course is offered in both fall and spring semesters. Typically, it is taken during one's Junior/Senior year, particularly when exploring the various fields of Computer Science to figure out what is of interest. It is useful for those interested in the databases/information sciences. The course is offered online for those enrolled in the online MCS program or the off-campus MCS program.

Course Structure

The workload really depends on how interested you are in the course. If you like the material and the field, you would probably spend a lot more time on it, but if not, a minimum weekly requirement lies between 4-5 hours of keeping up-to-date with the readings & doing homework. There is a lot of reading involved, and not keeping up can really take its toll during the exams. There is only one MP, typically in the student's choice between C++ or Java, so knowing how to program in one of those languages is essential. There are three problem sets, each spanning about 3 weeks. These also require reading the textbook and keeping up with lectures.

Instructors

In the fall semester, the course is typically taught by Professor Arindam Banerjee. Other instructors during the various spring/online semesters include Professor Jiawei Han, a highly acclaimed professor in the field of Data Mining, Professor Ruby Tahboub, and Professor Hanghang Tong.

Life After

This class leads the way into Data analytics and Data Mining. Those interested can consider going on to take CS512 which requires a project as well. This class is considered part of both the Databases track and the Artificial Intelligence track, so students who enjoy it may want to consider taking other database classes such as CS411 (Database Systems) and CS410 (Text Information Systems) or other AI/ML classes such as CS440 (Artificial Intelligence) and CS446 (Machine Learning).