MSBA 738 Master Syllabus

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MSBA 738: Data Mining for Business Analytics Master Syllabus

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Course Description

Data mining—the art of extracting useful information from large amounts of data—is of increasing importance in today’s world. The amount of data flowing from, to, and through enterprises of all sorts is enormous, and growing rapidly. Businesses are trying to make effective use of the abundance of data to which they have access: to make better predictions, better decisions, and form better strategies. This course will introduce students to data mining problems and tools to enhance managerial decision-making. The students will learn how to ask the right questions and how to draw inferences from the data by using the appropriate data mining tools. The students will acquire hands-on experience on applying data mining methods using data mining software. Overall, the course will enable students to approach business problems data-analytically, envision data mining opportunities in organizations, and follow up on ideas or opportunities that present themselves.

Course Objectives

  • Introduce the steps involved in data mining, from goal definition to model deployment.

  • Discuss data preparation techniques. 

  • Examine supervised learning methods, such as classification and prediction. 

  • Examine unsupervised learning methods, such as clustering.

Grading and Assessment

Grade Distribution Grading Scale
A/A- >=90% Articles Quizzes 10%
B+/B/B- 80% to <90% Class Participation 10%
C 70% to <80% Homework Assignments 40%
F below 70% Final Exam 40%

The split between +/- scores will be determined by the instructor based on the clustering of scores. 

Instructions for all assignments will be posted on Canvas. Completed written assignments should be submitted via Canvas only. 

Articles Quizzes
For the weeks designated in the schedule, you will be responsible for reading several articles and cases about applications of data mining in businesses. On each of those weeks, there will be an online quiz during the week based on the articles. You should also be prepared to discuss the articles in class. Reading the articles will help you get ideas on how data mining methods are applied to solve real-life business problems. 

Class Participation
You will be expected to participate in class discussions and complete in-class exercises. Each week we will discuss relevant articles and/or work through small exercises in class. You will be evaluated based on your involvement in these and other discussions in class. You are encouraged to discuss your own work experience when relevant to the material being covered in class. You are also encouraged to ask questions in class.

The following factors will contribute positively to your participation score: (i) Arriving before the start of class and staying till the end, (ii) Listening actively to the instructor and peers, (iii) Asking good questions and responding to questions asked to the class, (iv) Actively working on practice problems, (v) Neither dominating the conversation nor being too quiet, and (vi) Exhibiting a good sense of humor. 

The following factors will contribute negatively to your participation score: (i) Arriving after the start of the class and/or leaving before the end (ii) Lack of involvement, silence, detachment, or disinterest (iii) Distracting others by surfing the web, emailing, texting (iv) Not listening actively, (v) Not working on practice problems, and (v) Leading the discussion into unrelated topics. 

10% of the class participation points will be awarded for completing the pre-course assignment on time.

Homework Assignments
These will primarily consist of problem sets designed to give you valuable practice and enhance your understanding of the concepts covered in class. 

Final Exam
This will be an individual assignment due by the time and date designated in the schedule. 

Semester Grade
Your semester grade will be assigned based on the total points earned on the assignments described above; no extra credit will be available. You can request a review of the grade earned in an assignment within a week following the assignment of grades. After that period no grade will be revised.

Course Schedule

Week Topics Assignments*
1 Overview of Data Mining and Business Analytics Pre-course
2 Data Preparation Quiz 1
3 Prediction Models HW 1 due
4 Prediction Models Quiz 2
5 Classification Models HW 2 due
6 Classification Models Quiz 3
7 Decision Tree Models HW 3 due
8 Cluster Analysis HW 4 due

Final Exam

*HW assignments will be due by 7 pm on the designated dates. Quizzes (online) should be completed by 7 pm on the designated dates.

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