An online course from MIT which seeks to teach and involve potential students in the concept of Data Mining. It is designed to teach how to recognize patterns and making predictions from an applications perspective. The style of learning centered around cases and easy-to-use software. This module contains materials (syllabus, lecture materials, assignments, calendar) for a course about data mining which evolved from the disciplines of statistics and artificial intelligence. The course combines methods and materials from each of these fields in order for students to engage with algorithms for data mining.
Type of Material:
This is an online course that is part of the MIT OpenCourseWare: Massachusetts Institute of Technology. A Creative Commons license is available at Patel, Nitin. 15.062 Data Mining, Spring 2003. (MIT OpenCourseWare: Massachusetts Institute of Technology), http://ocw.mit.edu/courses/sloan-school-of-management/15-062-data-mining-spring-2003 (Accessed 12 Aug, 2014). License: Creative Commons BY-NC-SA
Recommended uses could include all disciplines for college students, including use in the following classes: 1. Market research 2. Consumer Behavior 3. Professional Selling 4. Strategic Management.
This entire module could be used to help instructors develop their own course about data mining. Parts of the module could be used in statistics courses.
Browser. A Chinese translation of this site is available by clicking on a link on the course home page.
To actually do the activities on the site, students would need XLMiner, an Excel add-in available at http://www.xlminer.com. Students also need Resampling Stats available at http://www.solver.com/xlminer-data-mining and SAS Enterprise Miner.
Identify Major Learning Goals:
The author states the learning goal as follows: To develop an understanding of the strengths and limitations of popular data mining techniques and to be able to identify promising business applications of data mining. Students will be able to actively manage and participate in data mining projects executed by consultants or specialists in data mining. A useful takeaway from the course will be the ability to perform powerful data analysis in Excel.
Target Student Population:
This module is from a graduate level course taught in 2003. As such, I would suggest that it be used in graduate level courses or those courses where students have an advanced statistics background.
Prerequisite Knowledge or Skills:
As stated above, a strong statistics background is needed before delving into the data mining topics, particularly because this course seems to be so application-oriented.
The course is an excellent overview of Data Mining with the all important hands-on component to teach the students exactly how to utilize the information and make informed decisions.
Depending on the professor and the student population, it might be a difficult assignment.
Potential Effectiveness as a Teaching Tool
When executed properly, this could be an invaluable learning experience. For an instructor that is in need of an outline, calendar, assignments, lecture materials, and project ideas for a data mining course, this site seems to have a solid base of information.
The only concern is the capability of the professor and the students.
Ease of Use for Both Students and Faculty
The syllabus is well planned and the lecture notes, assignments, and study materials seem appropriate. The layout, organization, and presentation of materials is very clear.
It seems that the instructor would need some substantial training in the three software programs required for this course to handle the large amounts of data. The module states "Instructions on using the software will be provided in recitations" so this would pose a hurdle for instructors without existing training.
Other Issues and Comments:
This is an excellent source for universities and students interested in exploring the field of data mining.
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