DSC 433/533: 8:00-9:50am Monday/Wednesday, 232 Lillis
Instructor: Iain Pardoe, 474 Lillis (346-3250), e-mail: ipardoe at
lcbmail.uoregon.edu
| Monday | 2-3pm | Also by appointment if you cannot make any of these times |
| Tuesday | 12-2pm | |
| Wednesday | 10-11am |
DSC 330 (business statistics) and DSC 340 (business information systems) or equivalent; business or accounting major (or MBA/grad student).
"XLMiner" software (www.resample.com/xlminer/). If you have Microsoft Excel on a personal Windows computer, you can download this Add-in here. Start XLMiner from the Windows Start menu, or select "Register XLMiner as an Add-in" to enable XLMiner to start automatically when you start Excel. This is a trial version of the software program, which will run for 30 days. You will need a code from the instructor to unlock the software so that you can use it for 6 months: send an email to ipardoe at lcbmail.uoregon.edu, identifying yourself as a student in this course to request the code (only students registered for this course will be sent the code). XLMiner is also available in the LCB Business Technology Center labs. The $40 fee for this software was billed as part of your fee assessment.
"Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management" (2004, 2nd edition) by Berry and Linoff (ISBN: 0471470643). The book is available from the University Bookstore on 13th and Kincaid or from places like Amazon.com.
The use of computer-based collections of data to support business decision-making activities has been commonplace since the early 1970s, and in recent years the digital revolution has seen such data collections increase both in size and complexity. It is often the case that large collections of data, however well structured, conceal implicit patterns of information that cannot be readily detected by conventional analysis techniques.
Such information may often be usefully analyzed using a set of techniques referred to as data mining or knowledge discovery. This course examines the business case for the use of data mining, the activities involved, and the techniques available. In particular we discuss:
The tentative course outline is as follows:
On Mondays we'll have a more traditional lecture-style format, while on Wednesdays we'll use the mobile laptop computer lab for hands-on experience with data mining software (you're encouraged to bring your own Windows-based laptop to class on Wednesdays since there aren't enough LCB laptops for everyone).
Computing is an integral part of this course. We'll use Microsoft Excel software and an add-in called "XLMiner" (see www.resample.com/xlminer/). This should be installed on all the computers in the LCB Business Technology Center and is also available for installation on a Windows personal computer (see above). Information on the use of the Business Technology Center is available at http://lcb.uoregon.edu/btc. Basic familiarity with Excel is assumed, but no prior knowledge of XLMiner is expected. You will receive instruction on software use during class time and via written documents. Information on the use of XLMiner is also available from the software help itself.
All data-sets to be used in the course are available at the course web-site: http://lcb1.uoregon.edu/ipardoe/teaching/dsc433. The web-site contains course announcements, homework assignments, (virtual) handouts, and data for use in assignments. You should get in the habit of checking the course web-site regularly, particularly before doing homework assignments.
You are advised to have a working e-mail address that you check frequently. Announcements are made on the course web-site, and you are informed when there are important new announcements via e-mail.
Class time will be spent discussing various data mining techniques (see "Course Outline" above), with emphasis on demonstrating how to use the techniques with real data. You are expected to attend all class periods; part of your grade will be based on examples and case-studies that we'll discuss in class. You are also expected to keep up with weekly assigned reading of 40 or so pages from the textbook, to complete all homework assignments (individually), to complete a group project (433) or a group project and a group class presentation (533), and to take a final exam. The final exam is scheduled for 10:15-12:15 Thursday December 6; this will be comprehensive and so cover everything from the term.
The amount of work expected for a 4 credit class is 12 hours per week. Class-time accounts for 3 hours 40 minutes, so you should expect to spend at least 8 hours each week reading the text, completing assignments, preparing for class, completing the project(s), and studying for the final exam.
Homework is a required part of the course. There are nine individual homework assignments, all of which count towards the homework portion of your grade. All assignments are graded and require computer work. Assignments are posted at the course web-site a week before they are due. Assignments are due at the beginning of class on Mondays, and graded and returned Wednesday of the same week. Solutions are not posted, although corrections are indicated as far as possible on returned assignments. Late homework is not accepted unless prior permission has been obtained from the instructor. Conscientious completion of all homework assignments is recommended to getting a good grade in this course (see grading below).
Everyone is required to complete a project involving the application of one or more of the specific analytic techniques covered in the course to real-world data from the student's particular business area or background. This application is to be written up as a professional report with a clear, concise description of the business problem, data, analysis, and conclusions, and should be done in a group of two or three. Further details will be made available during the course.
In addition, graduate students only (taking 533), are required to make a 10-15 minute presentation to the class on Monday of dead week. This can either cover the written project or relate to an entirely different topic. There is a wider range of topics available for the class presentation than the written project, since the presentation can also cover textbook topics that we will not formally cover in class (and which are unavailable in XLMiner).
The class will be graded on the A-F scale using the following guidelines:
| Undergraduates (433): | Graduate students (533): |
|---|---|
| Class participation: 100 | Class participation: 100 |
| Homework: 250 | Homework: 250 |
| Project: 300 | Project: 150 |
| - | Class presentation: 150 |
| Final exam: 350 | Final exam: 350 |
A make-up exam will be given only for documented reasons outside your control, e.g. illness supported by a letter from your doctor. Social and vacation conflicts are not acceptable reasons.
The University of Oregon is an equal opportunity, affirmative action institution committed to cultural diversity and compliance with the Americans with Disabilities Act. If you have a documented disability and anticipate needing accommodations in this course, please make arrangements to meet with the instructor soon. Please request that the Counselor for Students with Disabilities send a letter verifying your disability. This syllabus will be made available in alternative formats upon request.
The views and opinions expressed in this page are strictly those of the page author. The contents of this page have not been reviewed or approved by the University of Oregon.
