MBA 738: Data Mining for Business Analytics Master Syllabus
Course Instructor:
Office Number:
Office Hours:
Email:
Course Meeting Times:
Course Website: Canvas
Course Materials
- Required textbook: None.
- Required Software: RapidMiner Studio.
The software can be downloaded for Windows, Mac and Linux systems for free. - Articles and Cases:
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 handson experience on applying data mining methods using a data mining software. Overall, the course will enable students to approach business problems data-analytically, envision data mining opportunities in organizations, and also 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 | |
|---|---|
| A/A- | >= 90% |
| B+/B/B- | 80% to < 90% |
| C | 70% to < 80% |
| F | below 70% |
Split between +/- scores will be determined by the instructor based on clustering of scores.
| Grading Scale | |
|---|---|
| Articles Quizzes | 10% |
| Class Participation | 10% |
| Homework Assignments | 40% |
| Final Exam | 40% |
Articles Quizzes: For the weeks designated in the schedule, you will be responsible for reading several articles 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.
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, e-mailing, 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 consist of primarily problem sets that are designed to give you valuable practice and enhance your understanding of the concepts covered in class. These will be individual assignments due by the times and dates designated in the schedule.
Final Exam: This will be an individual take-home 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. A solid job on all the assignments will be evaluated at the A-/B+ border. To earn an A, performance must go beyond “meets expectations.” You can request a review of any grade within a week following the assignment of grades. After that period no grade will be revised.
Academic Integrity
George Mason is an Honor Code university; all students are responsible for knowing and following the George Mason Honor Code Statement: “Student members of the George Mason University community pledge not to cheat, plagiarize, steal, or lie in matters related to academic work.” In the event of a violation of the George Mason Honor Code, the violating student will be reported to the George Mason Honor Committee. Another aspect of academic integrity is the free play of ideas. Discussions are encouraged in this course, with the firm expectation that all aspects of the class will be conducted with civility and respect for differing ideas, perspectives, and traditions. Please refer to the Academic Standards website for further details. When in doubt (of any kind), please ask the instructor for guidance and clarification.
The recommendations for honor code violations for all graduate students adopted by the School of Business faculty in Spring 2019 are as follows:
| Type of Violation | Recommended Sanction |
|---|---|
| For example: Lying, plagiarizing, cheating, or stealing in academic matters | An F in the class |
| If the violation is considered to be egregious* | An F in the class, and consideration for dismissal from the School of Business |
* Preliminary determination of nature of violation qualifying as “egregious” is made by faculty and referred to the Program Director and Associate Dean of Graduate programs for further consideration if found in violation by Office of Academic Integrity.
MBA Program Learning Goals
- Teaming & Leading: Our graduates will demonstrate the team leadership and interpersonal skills needed to form, lead, and work effectively on diverse organizational teams.
- Analytical Decision Making: Our students will demonstrate the ability to analyze uncertain complex management situations using appropriate tools, techniques and information systems for decision-making.
- Knowledge of Functional Business Disciplines: Our graduates will demonstrate the ability to integrate knowledge from all functional areas of business into a meaningful firm-level perspective.
- Global Understanding: Our graduates will demonstrate a perspective on how businesses operate in the global environment.
- Communication Skills: Our graduates will demonstrate written, oral and presentations skills necessary to explain problems and solutions effectively and persuasively.
- Ethics and Social Responsibility: Our graduates will have a sense of professional and social responsibility in the conduct of managerial affairs.
Learning Disabilities
If you are a student with a disability and you need academic accommodations, please see me and contact the Disability Resource Center (DRC) at (703) 993-2474, at the beginning of the semester. All academic accommodations must be arranged through the DRC.
Other Course Policies
- Attendance: Attendance in class is mandatory. If you are absent, it is your responsibility to find out from a classmate what you missed (both course materials and announcements).
- E-Mail Correspondence: Outside of the designated class time and office hours, e-mail is the easiest and quickest method to contact me. Consistent with federal privacy laws, I do not respond to non-George Mason email ids with confidential information. Your emails to me must include "MBA 738" in the subject and your full name in the body.
- Laptops and hand-held devices: Technology can greatly assist learning, but it can also be a distraction. Laptops or any other hand-held devices should strictly be used for class related activities such as working on in-class exercises, taking notes or following lecture slides.
Course Schedule
| Week | Topics | Assignments* |
|---|---|---|
| 1 | Overview of Business Analytics and Data Mining | 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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