MBA 739: Advanced Data Mining for Business Analytics Master Syllabus
Course Instructor:
Office Number:
Office Hours:
Email:
Course Meeting Times:
Course Website and Other Resources
- Canvas
- Required Book
- Shmueli, Galit, Peter C. Bruce, Inbal Yahav, Nitin R. Patel, and Kenneth C. Lichtendahl Jr., Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons, 2018.
- Optional Book
- Hadley Wickham and Garrett Grolemund R for Data Science in O’Reilly, 2017. Available online here. Also available in O'Reilly Learning Platform (Safari) in George Mason library databases.
- Required Software
- R and RStudio
- RStudio is open-source data science and statistical computing software for desktop and cloud-based applications.
- An instance would be made available in RStudio Cloud to complete tutorials, in-class exercises, and assignments.
- For long-term learning, students would be actively encouraged to download and use the desktop version of R and RStudio. The link for signing up in RStudio Cloud will be available in Canvas.
- R and RStudio
Course Description and Course Objective
The business model of the top companies by market capitalization - Apple, Microsoft, Amazon, Alphabet, and Meta Platforms, relies on one secret ingredient - DATA. Their ability to stay at the top will also be determined by how they "mine" the "data." The analytics revolution is in full swing. Whereas intuition and gut feeling were once acceptable means for the manager to run the organization, this is no longer the case. "In God we trust, all others bring data" has become the global mantra of organizational decision-makers. In MBA 738, the goal was to introduce you to broader concepts.
This course aims to expand your tool kits in terms of methods, introduce you to R, and put those analytics tools (Ensembles, Neural Networks, Text Mining, Association Rules, and Collaborative Filtering) to good use to answer business questions. Given the ever-expanding scope of analytical tools that exist, this class will not be comprehensive. Instead, the goal will be to demystify these techniques, give you the tools you need to think about these problems, and equip you to continue adding to your toolkit on your own.
Grading and Assessment
| Grade Distribution | |
|---|---|
| A/A- | >= 90% |
| B+/B/B- | 80% to < 90% |
| C | 70% to < 80% |
| D | 60% to < 70% |
| F | below 60% |
The instructor will determine the split between +/- scores based on the clustering of scores.
| Grading Scale | |
|---|---|
| Discussion Participation | 10% |
| Individual Assignments | 40% |
| Group Project | 50% |
Instructions for all assignments will be posted on Canvas. Completed written assignments should be submitted via Canvas only.
Discussion Participation: You will be expected to participate in Online discussions and actively seek help during the Weekly meetings. While participation in Weekly meetings is optional, it is highly recommended. In the Weekly meetings, we will discuss relevant materials and/or work through small exercises in class each week. You will be evaluated based on your involvement in these and other discussions. You are encouraged to discuss your own work experience when relevant to the material being covered. You are also encouraged to ask questions in online discussions and Walkthrough sessions.
The following factors will contribute positively to your participation score: (i) Actively participating in online discussions, (ii) Actively participating in the Code Walkthrough sessions, and (iii) Actively working on assignments.
Group Project: Alongside the standard coursework will be a group project which you and your group members will work on. Groups will be randomly assigned by Wednesday of Week 1. Groups will be composed of four or five students. The goal will be for your group to solve a business problem a team member is currently facing. You can choose a business or policy question using secondary data if you cannot use live data due to your employer’s restrictions.
Potential Alternate Data Sources
The purpose of the project is for you to gain hands-on experience in solving a problem using the principles covered in class. More importantly, the goal is not simply to throw analytical tools at a dataset. Instead, the goal is to use your toolkit to develop a corpus of facts that allow you to make actionable recommendations to your boss, team lead, or policymaker.
The Group Project will be comprised of three sets of submissions:
- One Page Project Proposal: This proposal will discuss five key components of your project. It will be due in Week 2 and is worth 5% of your final grade.
- The question you are going to ask.
- The reason why that question is important to the organization.
- How organizational behavior will change as a result of what you find.
- A brief description of the data you intend to use to answer your question.
- The analytical approach you intend to take to answer your question.
- One Page Project Status Report: This will be an updated version of your project proposal incorporating feedback from your original project proposal (provided by me) and your ongoing findings from your project. Much of your analysis should be completed for this submission. This report will be due in Week 5 and is worth 5% of your final grade.
- A data dictionary and any prepared figures/tables should be attached as a technical appendix and properly referenced in the status report. Technical documentation does not count against your page limitation.
- Final submission and Presentation: The final submission will include a 15 min video presentation and a one-page report. The one-page report will be a summary of the question, findings, and strategic recommendations you would make to the organization as a result of your findings. Limitations on your approach and opportunities for future research should be included. Supporting information should be included in a technical appendix and be appropriately referenced. This submission is worth 40% of your grade and is due Week 8.
Individual Assignments: These will consist of 3 courses in DataCamp, problem sets based on the Shmueli book, and other exercises. These assignments are designed to give you valuable practice and enhance your understanding of the concepts covered in class. These are individual assignments and are due by the times and dates designated on the schedule.
Honor Code / 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. Please refer to the Academic Standards website for further details. Please ask the instructor for guidance and clarification when in doubt (of any kind).
The recommendations for honor code violations for all graduate students adopted by the School of Business faculty in 2019 are as follows:
| Type of Violation | First Offense | Second Offense |
|---|---|---|
| Plagiarism, lying, cheating on an assignment, homework, or including other’s work as your own | An F in the class | An F in the class and dismissal from program |
| Egregious Violation [e.g., stealing an exam; passing on confidential course material; cheating on an exam, project, or otherwise violating specified rules for an exam or project; etc.] | An F in the class and dismissal from program | An F in the class and dismissal from program |
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 business areas 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 presentation skills necessary to explain problems and solutions effectively and persuasively.
Learning Disabilities
If you are a student with a disability and 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
- Participation: Participation in the course activities is mandatory.
- 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 739" (or “GBUS 739”) in the subject and your full name in the body.
Course Schedule
| Week | Topic | Chapters in Book | Assignments |
|---|---|---|---|
| 1 | Course Introduction, Data Preparation, and Data Transformations |
Shmueli et al. (2018). Ch. 4 Review MBA 738 Notes Wickham and Grolemund (2016). Ch.3 and 5 |
Week 1 Discussion One - Introductions Week 1 Discussion Two - Project Description and Data Description Week 1 Individual Assignment - Finish DataCamp Courses |
| 2 | Data Visualization |
Shmueli et al. (2018). Ch.3 Wickham and Grolemund (2016). Ch.1 |
Week 2 Individual Assignment - Data Visualization Week 2 Group Assignment - Project Summary |
| 3 | Association Rules and Collaborative Filtering | Shmueli et al. (2018). Ch.14 | Week 3 Individual Assignment - Association Rules |
| 4 | Classification, Numeric Prediction, and Clustering | Shmueli et al. (2018). Ch.5,6,9, and 15 | Week 4 Individual Assignment - Classification, Regression, and Clustering- |
| 5 | Decision Trees and Ensembles | Shmueli et al. (2018). Ch.9 and 13 |
Week 5 Individual Assignment – Decision Trees and Ensembles Week 5 Assignment - Group Project Update Summary Report |
| 6 | Neural Networks | Shmueli et al. (2018). Ch.11 | Week 6 Individual Assignment - Neural Networks |
| 7 | Text Mining | Shmueli et al. (2018). Ch.20 | Week 7 Individual Assignment - Text Mining |
| 8 | Final Project Presentations | N/A | Week 8 Assignment - Group Project Final Presentation |
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