MSBA 511: Data Mining for Business Analytics
University of San Diego | Spring 2025: 3 Units
COURSE OVERVIEW
Course Description
In today’s data-driven world, organizations often find themselves rich in data but lacking actionable insights. This course explores the field of data mining, which focuses on extracting meaningful insights from data to support quantitative decision-making. You will get a comprehensive introduction to key data mining concepts and techniques, with an emphasis on their practical application. While the technical details of methodologies will be covered as needed, the primary focus will be on how to effectively use these techniques in real-world scenarios using Python, one of the most popular tools for data analysis and mining. By the end of the course, you will not only develop an appreciation for the vast opportunities within the field of business analytics but also acquire the skills and knowledge needed to leverage these opportunities effectively.
Course Details
Dates: 3-Feb-2025 - 19-May-2025
Day/Time: Mon 4:00PM - 6:50PM
Location: KCBE-104
Instructor Information
Instructor: Chris Young, MBA
Email: dcyoung@sandiego.edu
Office Hours: Mon 3:00-4:00PM in KCBE-218 or by appointment
Course Learning Outcomes
At the conclusion of the course, you should be able to:
- Understand the principles and concepts of data mining, including its role in business decision-making.
- Explore key data mining techniques such as classification, clustering, and association rule mining.
- Gain proficiency in applying data mining algorithms using Python.
- Analyze and interpret data mining results to provide actionable business insights.
COURSE MATERIALS
Required Textbook
The textbook Data Mining for Business Analytics: Concepts, Techniques and Applications in Python, by Galit Shmueli, Peter C. Bruce et al. (Nov 2019) is required for this course and can be purchased on Amazon here. If you purchase from another online retailer, be sure to purchase the Python edition (ISBN-10:1119549841) as the textbook is published for a variety of statistical software tools. The textbook also has a supplemental website with a variety of Python resources and datasets.
Course Resources
- All lecture slides, resources, and assignments are provided on this website or GitHub repository.
- All homework and programming assignments will be submitted on Canvas/Gradescope.
- All course announcements will occur through Canvas.
Required Technology
This is a data mining class and we will be primarily using Python and Jupyter Notebooks throughout the course. Below is a list of software that you will need to install on your computer.
- Anaconda Distribution of Python and other tools
- A plain text editor such as VS Code is highly recommended
COURSE CONTENT & POLICIES
Grade Distribution
| Assignment | % of Total Grade | 450 Total Points |
|---|---|---|
| 6 Conceptual Homework Assignments | 20 | 90 (15 each) |
| 4 Programming Assignments | 18 | 80 (20 each) |
| 1 Hackathon | 9 | 40 |
| 1 Group Project | 18 | 80 |
| 2 Exams | 36 | 160 (80 each) |
Please be aware of the following acronyms:
- HW = Conceptual homework assignments completed in a document format
- PA = Programming assignments completed in Python/Jupyter Notebook
Grading Scale
| Final Grade | % of 500 Points |
|---|---|
| A | >=93% |
| A- | 90-92.99% |
| B+ | 88-89.99% |
| B | 83-87.99% |
| B- | 80-82.99% |
| C+ | 78-79.99% |
| C | 73-77.99% |
| C- | 70-72.99% |
| D+ | 68-69.99% |
| D | 63-67.99% |
| D- | 60-62.99% |
| F | <60% |
Assignment and Exam grades are released typically a week after the submission date. It is your responsibility to check your grade and reach out if you believe there is a problem. Final Grades are not rounded up.
Conceptual Homework
There are eight (8) conceptual homework assignments that focus on understanding key concepts and solving problems step-by-step by hand, rather than relying on programmatic solutions. While you are allowed to use Python/AI tools to support your learning during homework, it is essential to ensure you fully understand the reasoning and process behind the solutions. AI will not be permitted during exams, so developing your ability to work through problems independently is critical for success.
Tip
Use these assignments as an opportunity to practice and solidify your understanding, so you’re well-prepared for exam-style questions.
Programming Assignments
There are four (4) homework assignments consisting of problem-solving and Python based programming assignments. The purpose is to provide students with the opportunity to apply and practice the concepts and skills learned in the lectures and demos. Homeworks may be difficult and require you to demonstrate a deeper understanding of the material.
Case Study and Group Project
There is one (1) case study and group project that you will be able to collaborate with 1 other classmate. For the case study, you will choose from a list of the cases in the textbook and apply the relevant concepts that we have learned. For the group project, you will pick your own dataset and identify the problem statement and prepare your own analysis and presentation of your findings at the end of the semester.
Exams
Both exams are open-note and completed by yourself with no collaboration/communication with any other students. You will not be able to use a computer during exams. No late exams are permitted, except for extenuating circumstances. Please reach out as early as possible if you know something will prevent you from attending class on exam dates.
Class Attendance
The goal is to make lectures worth your while to attend. Some class dates may only consist of a lecture but the vast majority of classes will involve hands-on practice with Python and Jupyter Notebooks. Regular attendance is very important to your success in this course.
Late Submission Policy
There is a 24-hour grace period for all homework and programming assignments with NO late penalty. Assignment submissions will NOT be accepted after the grace period. This policy is intended to be a safety net in case you experience any difficulties submitting your assignment on time. Do not view the grace period as the true due date for the assignment. If you miss an assignment due date, it is likely that you are not managing your time effectively and will need to adjust your planning and study habits. Please note that any excuse for not submitting assignments on time will not be accepted AFTER the 24-hour grace period. If you have extenuating circumstances, you must contact the professor BEFORE the assignment due date.
Regrade Policy
The intent of the regrade policy is protect students from serious issues in grading. Email the professor within 72 hours and provide evidence for why your answer is correct and merits a regrade (i.e. a specific reference to something said in a lecture, the readings, or office hours). Make sure you confer with your team first on any group completed project and submit one regrade request after your team comes to a consensus.
SCHOOL RESOURCES
Academic Honesty Statement
USD’s policy on academic integrity is expressly integrated into this course. (Please consult https://www.sandiego.edu/conduct/the-code/rules-of-conduct.php to review this policy.) Any deviation from the standards of this policy may result in a grade of “F” for the course. Because most of the work in this course must be your own, any unauthorized assistance will be considered a violation of the academic integrity policy. If you have questions about the propriety of your work or other participants’ conduct concerning this course, I am readily available to offer an interpretation of this policy.
Disability Statement
It is University of San Diego policy not to discriminate against qualified students with a documented disability in its educational programs, activities or services. If you have a disability-related need for accommodations in this class, contact the Student Affairs office for assistance.
General Student Conduct
The University of San Diego School of Business expects its students to conduct themselves in a professional manner at all times. Its students are generally individuals who are preparing for career employment. An integral part of their career and professional development is the expectation that they will conduct themselves during the educational processes in the same manner as will be expected in an employment situation. The University of San Diego Student Code of Rights and Responsibilities is published online at https://www.sandiego.edu/conduct/the-code/.
Food Insecurity & Pantry
https://www.meetatusd.com/toreros-against-hunger
The goal of Toreros Against Hunger at the University of San Diego is to serve as occasional food relief for University of San Diego students experiencing food insecurity while actively decreasing the amount of food going to waste on campus.
Food insecurity broadly defined is “the state of being without reliable access to sufficient quantity of affordable, nutritious food.” Indicators of food insecurity include skipping meals and/or cutting the size of meals due to lack of financial resources, experiencing hunger but not eating and/or the inability to afford balanced meals.
https://www.sandiego.edu/sustainability/initiatives/social-justice.php
Counseling Center
https://www.sandiego.edu/counseling-center/
We strive to facilitate students' personal growth and enhance their academic success through accessible, culturally congruent clinical and outreach services. We work in collaboration with other Wellness and university departments and community partners.
A counselor-on call is available to consult about after-hours urgent psychological concerns at all times. The counselor-on call can be reached by calling 619-260-4655 (24 hours a day, 7 days a week). Please contact the Department of Public Safety to access emergency services (x2222 on any campus telephone, otherwise call 619-260-2222).
The 24-hour San Diego Access and Crisis Line (1-888-724-7240) also offers crisis intervention, information, and referrals.https://www.sandiego.edu/conduct/the-code/rules-of-conduct.php
Course Evaluations
An online evaluation will be made available to you near the end of this course. Your timely and considered feedback is valuable to us and an important element of your learning experience.
Notice
This syllabus is subject to change based on the needs of the class; I will make sure to notify you through an announcement on Canvas.