Course Outline

On this page

1. Faculty Information
2. Course Description and Objectives
3. Program Learning Goals and Outcomes
4. Prerequisites
5. Course Materials
6. Deliverables
7. Class Participation
8. Grading and Course Deliverables
9. Session Details
10. Religious Accommodations
11. Academic Accommodations
12. Academic Integrity Policy
13. AI Policy
14. Classroom Conduct Expectations
15. Attendance
16. Missed Work and Late Submissions
17. Laptop and Technology
18. Conflict Navigation Services
19. Communication
20. Dates to Note

• Course name and ID: OIM6301-01, Programming for Business Analytics
• Meeting time: Wednesday, 9:30 AM to 12:00 PM ET, 9/09 to 12/16, 2026
• Location: Olin Hall 225
• Credits: 3
• Program: MSBA core

Faculty Information#

• Name: Dr. Zhi Li
• Office: Babson Hall 216D
• Email: zli@babson.edu
• Office hours: Thursdays, 4:45 PM to 5:45 PM, Babson Hall 216D. Drop in, no appointment needed. For another time, in person or online, email me or use the booking link in the Canvas course.
• Peer tutor: Danyal Tariq runs group sessions on Mondays, 3:00 PM to 4:00 PM on WebEx, and on Wednesdays, 3:30 PM to 4:30 PM in Olin Hall 125 and on WebEx. The WebEx link is in the Canvas course.

Required 1:1 meeting. Complete one 15-minute meeting with me during the semester, in person or online. It counts toward your participation grade. Book it whenever you are ready.

Course Description and Objectives#

This course introduces programming for business analytics through two languages: SQL, for querying and managing structured data in relational databases, and Python, for acquiring, transforming, analyzing, and presenting data. You will learn to interrogate a dataset you did not create, to design and build a small relational database, and to produce analytical work that another person can open, run, and verify.

The course is organized around how analytical work is now done. An AI agent can write the query, run the analysis, and produce the chart, and it often does so competently. Directing that work takes a foundation in four areas: programming in Python, databases and SQL, Git, and how AI models and agents work. You will also learn what data and tools already exist to build on, and work with an agent to turn a question of your own into something a person outside this course can open and use. You will check the numbers at every stage of that work.

You will write Python and SQL throughout the semester, well enough to read what an agent produces, judge whether it is correct, and change it deliberately.

So that expectations are set plainly: this course does not require you to write a complete program from an empty file. It requires reading Python and SQL written by other people and by AI, judging whether it is correct, and writing enough of it independently to understand what is being read. Certain exercises are designated to be completed by hand, without AI assistance, and each states the reason.

By the end of this course, you will be able to:

• Complete a small data analysis end to end with an AI agent, from acquiring the data to a published product someone else can open
• Find data and tools to build on, and choose the ones that fit a question of your own
• Read Python and SQL well enough to explain what a piece of AI-written code does before running it, and to locate where an answer went wrong
• State the grain of a table or a result, and predict whether a join will duplicate rows
• Check a number you did not compute, and state what the check would have caught
• Identify the assumptions inside an analysis that were never stated, and judge whether they fit the decision at hand
• Turn a vague business request into an analytical specification: metric, grain, window, comparison, and the decision it informs
• Direct a coding agent that can see your data, and audit what it returns
• Explain what a language model is doing when it answers you, and why an agent that can run code against your data behaves differently from one that cannot
• Use Git and GitHub so that your process is checkable against your claims

Program Learning Goals and Outcomes#

This course addresses the following MSBA program learning goals.

• Tools and Methods. Learn to integrate tools and analytical methods to maximize the value of data in an organizational context. You'll be equipped to select appropriate tools and interpretive methods to ensure that the right approach is used at the right time.
• Enterprise Strategy. You'll understand the role that advanced analytical techniques can play in achieving goals within an organization. Learn to take into account the strengths and limitations of various analytical approaches, as well as the various consequences of strategic decisions.
• Communication Skills. You'll be equipped to not only understand the results from advanced analytical techniques, but to also summarize them appropriately for a variety of audiences. Learn to make a logical and persuasive case regarding the value analytics in particular business situations.
• Teamwork and Collaboration. You'll value diverse perspectives and skills within a team and you'll be equipped to work collaboratively in analytics projects.

Tools and Methods is formally assessed in this course, as part of the MSBA program's Assurance of Learning process. The measurement is the database project together with its oral sign-off: you design a schema, build it with declared keys, answer business questions against it, and then account for your own figures aloud. Note what the goal asks for, which is the selection of an appropriate approach and the judgment of when it is appropriate. That is what is assessed, and it is a different thing from knowledge of a particular product.

Enterprise Strategy is addressed through the five-part specification, which requires you to name the decision an analysis informs before it is worth running, and through the sign-off, which asks whether an analytical approach fits the question to which it was applied. Communication Skills is developed in the memo and in the walkthrough. Teamwork and Collaboration is developed in class, where exercises are frequently completed in pairs and you are expected to help one another, and in the sessions given over to reviewing and critiquing each other's work. Those three are developed in this course and formally assessed elsewhere in the program. Every graded deliverable here is individual, so nothing in this course measures collaboration.

Entrepreneurial Thinking and Acting is assessed elsewhere in the program.

Prerequisites#

You need to be comfortable with a personal computer: using a web browser, navigating and editing files, and installing software. No prior programming experience is required, and none is assumed.

Course Materials#

No textbook is required. Course materials will be published on the course website and indexed in Canvas.

Required tools, and what each is for:

Babson provisions a Claude API key for the AI agent used in this course. No tool in this course requires you to spend money.

Setup instructions are on the course website.

Deliverables#

Every session produces work. It has to be built in class and committed before you leave, and I check that it is present and that it was committed during class hours. Those commits are not graded on quality; quality is graded in the projects below.

Every due date in this section is on the schedule.

Mini projects. Each one is a notebook in your repository.

A database project runs in two parts. You choose a scenario, design an entity-relationship diagram, build the database with declared keys, and answer business questions against it. This is the largest deliverable in the course and it is where the MSBA Tools and Methods goal is assessed, so it ends with an individual oral sign-off in class on your own database.

A final project. You choose the domain and the question, and you build something a person who is not in this course can open and use. That is the standard, and it is deliberately more than a notebook: the product has to take input from whoever is using it, query real data, return an answer that changes with the input, state its assumptions on the screen, and live at a public URL that stays up. It runs in three stages: a proposal, then the product, then the walkthrough in class. The proposal deadline sits before Thanksgiving so that you enter the break with a settled question and can build over it.

How you build and publish it is your decision, and the grade does not depend on which you chose. It depends on whether somebody outside this course can open it and use it, and on whether you can explain it.

Every deliverable that states a number must carry a section titled "How I know these numbers are right", with at least one independent verification, and a check that runs. A sentence claiming you verified something is a claim. A cell that recomputes the figure another way and fails when the two disagree is evidence. The section has its own line in the rubric.

None of this is taught as a separate unit. Each check arrives in the session that needs it: recomputing a figure a second way, checking it against a subtotal you already know, looking for duplicate keys before a join, asking whether the order of magnitude is plausible for this business, and holding back data whose answer you already know so you can predict it and compare.

Every project asks the same question, and you have to answer it in the notebook:

Pick one piece of AI output you did not accept as-is. What did it give you, what did you change, and how did you know? Point to the commit or the cell.

If the agent got it right the first time: what did you do to verify that?

AI is open on the projects. The exercises designated to be completed by hand are the in-class ones, and each states its reason.

How work is submitted#

Most work is submitted as a URL, usually to a GitHub repository, with Canvas holding the link. Each assignment states what it expects. Uploaded files, screenshots, and code pasted into a text box are not accepted, because none of them can be re-run or checked. Commit and push as you work; a single commit containing an entire project shows nothing about how it was built.

Class Participation#

Participation comprises your work during class, committed from the session, together with the required one-to-one meeting. It measures whether the work happened, and it involves no judgment about the quality of what you built. Sufficient allowance is built into the measure that ordinary absence, illness, or an accommodated conflict carries no penalty here and requires no documentation from you.

Checkpoints. At six points during the semester I check that your session artifacts are present, were committed during class hours, and are published where the checklist says they must be. The checklist for each checkpoint is posted before it closes, and that checklist is the rubric.

Lightning talks are optional in this course and earn a bonus point. Once during the semester you may take four minutes at the start of a session: two minutes on something you built, broke, or found, followed by two minutes of discussion that you run yourself, closing with a question your classmates can disagree about. A talk adds one point to your final grade. Only one talk counts, and your final grade is capped at 100. Nobody loses anything by declining to give one. They are optional because this course already requires two oral turns: the database sign-off and the final walkthrough.

On being called on. I will ask students to explain their own work, in class, on a shared screen.

Grading and Course Deliverables#

Projects comprise the mini projects, the database project with its oral sign-off, the final project, and the final walkthrough. The weighting among them is set during the semester and posted with each assignment. Due dates are on the schedule. These percentages may be adjusted during the semester, with notice, and never downward on work you have already completed.

Where your grade is actually decided. Checkpoints and participation confirm that the work happened, and anybody who does the work earns close to full marks on both. What distinguishes one submission from another is the design quality of your database, the memo that explains your final project, and the two oral turns.

The two oral turns are graded separately from the artifacts they concern. The database sign-off and the final walkthrough both ask you to open your own work and answer a question about it. State your prediction aloud before you touch the keyboard, and leave your agent open throughout. What this measures is whether you hold an expectation of your own. I will demonstrate the format in class before the first one, so that the standard is known in advance.

Session Details#

(Readings and assignments are detailed further in Canvas. Dates may be adjusted in response to the pace of the class.)

Religious Accommodations#

Students are expected to review the Course Outline and notify faculty as early in the semester as possible of potential conflicts between course requirements and religious observances. In such an event, the faculty member will provide reasonable accommodations that do not unduly disadvantage the student.

Academic Accommodations#

Any student who may need accommodation(s) based on the impact of a disability should contact the Department of Accessibility Services (DAS) as early in the semester as possible. Accessibility Services staff may be reached by email at accessibility@babson.edu, by phone at 781-239-4100, or in Park Manor West, 1st Floor. Accessibility Services staff will coordinate reasonable academic accommodations for eligible students.

Academic Integrity Policy#

Academic integrity is essential to the mission of Babson College and vital to your success as a student. Our standards as an institution value community, original scholarship, and a collaborative environment that encourages each student's intellectual growth. Violations of academic integrity interrupt a student's ability to learn and produce individual work while also diminishing the achievements of the entire College. As students, you are responsible for reading, knowing, and understanding the College's policies regarding academic integrity. Below is a non-exhaustive list of potential violations:

• Cheating
• Fabrication
• Facilitating Academic Dishonesty
• Plagiarism
• Participation in Academically Dishonest Activities
• Unauthorized Collaboration

The Graduate Student Handbook outlines all the policies in detail as well as the potential outcomes for academic integrity violations.

What this means in a course built around AI. Using an agent to produce your analysis is expected here and is not a violation. Handing in a number you cannot account for is. If you are asked to open your analysis and cannot explain what a query does, why a row count changed, or what your own verification checked, that is treated the same as submitting someone else's work as your own. Collaboration with classmates is encouraged: discuss questions, help each other debug, read each other's analyses. Copying another student's work without attribution or understanding is not that.

AI Policy#

You are expected to use AI in this course. That is the subject of the course.

The line is responsibility. An agent can produce the analysis; it cannot be responsible for whether the number is right, whether the question it answered was the one you asked, or whether you can explain either. Those stay with you, and they are what is graded.

Every assignment states its own AI guidance, because it differs by task. Four levels are used, and each part of an assignment is labeled with one:

• By hand. Do it yourself, without AI, so that you can read the AI version later. Used for one short exercise most sessions, five to eight minutes, and the reason is stated every time.
• AI writes, you check. This is the normal mode. Generate freely, then read every line, and be ready to explain any of it.
• You direct, you verify. This applies to the projects. Decompose the question, give the agent the schema and the data dictionary, check the result against something independent, and record what you did not accept as-is.
• You choose the question. This applies to the final project. The analysis can be delegated; deciding what is worth asking cannot.

If an assignment does not specify, ask. When in doubt, be transparent about what you used and how.

Three rules apply throughout:

Your agent must be able to see your data. Pasting code into a chat window means the model cannot check a column type or a row count, so it guesses, and guessing is what produces plausible wrong code.

Be careful with data. Do not paste anything personal or private into an AI tool; you cannot control where it goes. If you would not post it publicly, do not give it to an agent. Everything this course works with is public sample data, so the question does not arise here, but it will in your work. For general use outside this course, Microsoft Copilot through your Babson login (copilot.microsoft.com) is a Babson-secured option.

Expectations differ by course. Faculty set their own rules, and following the wrong one can become an academic integrity issue. Ask when you are unsure.

Classroom Conduct Expectations#

The Babson College Community Code of Student Conduct aims to promote a positive living and learning community for students at Babson. Integrity, civility, and respect for the community, in and out of the classroom, are recognized by Babson College as core values.

Babson's classrooms are spaces where students, faculty, and staff come together to engage in rigorous learning supported by integrity, respect, trust, fairness, and ownership. Every member of this learning community, including your fellow students, instructors, staff, and guest speakers, contributes to and deserves to participate in a productive environment where they can engage and learn free from disruption.

The Code's guidance on civil discourse asks students to listen actively, acknowledge others' perspectives, and address disagreements based on the structure and content of an argument or with alternative evidence. Disagreement and vigorous debate are a vital part of intellectual development and are always welcome. Disruptive Behavior, Harassment, and Bias, as defined in the Community Code of Student Conduct, are not.

These expectations apply to all course-related interactions: class sessions, assignments, online or remote participation, electronic communications, discussion boards, group projects, and course evaluations. Conduct inconsistent with these expectations, including conduct that disrupts a session or limits others' ability to benefit from it, may result in removal from the session and referral to the Office of Community Standards.

This restates existing College expectations. It does not create new policy.

Attendance#

Most of the work in this course is done in class, so an absence cannot be made up independently. Tell me if you will be away. Every session's notebook is published on the course website, so the material is always available.

Babson expects instructors to provide reasonable accommodation for religious observance and for intercollegiate athletic competition. Notify me as early in the semester as possible and we will agree how the missed work is covered.

For any other circumstance that keeps you away, including illness, contact me in advance where possible. Enough allowance is built into the participation measure that an ordinary absence carries no penalty and requires no documentation.

Missed Work and Late Submissions#

Deadlines are firm. Late submissions lose 10 percent of the assignment's points per day or partial day, to a maximum of four days, after which the grade is zero. If circumstances will prevent you from meeting a deadline, contact me before it passes. Extensions are considered case by case and require advance notice.

Laptop and Technology#

Bring a laptop to every session. It may be Babson-issued or your own, and it has to be current and in working order. Nearly every session involves hands-on work, and what you build has to be committed during class.

Conflict Navigation Services#

Conflict, especially when working in groups, is a normal and expected part of life, and ideally it is an opportunity to strengthen relationships and address concerns that would otherwise go unspoken. If you experience interpersonal conflict in this course, I encourage you to explore the College's Conflict Navigation Services as a resource.

Communication#

The primary channels are the course website, for content, Canvas, for announcements and grades, and GitHub, for your work. For any private matter, email me at zli@babson.edu with the course number in the subject line, for example "OIM6301: Question about Mini Project 2".

You can expect a reply within 24 hours on weekdays. Email received over the weekend is answered the following Monday.

Dates to Note#

Please refer to the registrar's academic calendar for official dates.