MSBAi Instructor Course Talking Points

Instructor-approved responses to three positioning questions for each MSBAi course. Approved and submitted by Amber Glynn (aglynn1@illinois.edu), 2026-06-24. For use in recruiting, admissions, and program-positioning discussions.

Three questions answered for each course:

1. What does the course cover?
2. What do students build?
3. Why does it matter for careers?

Fall 2026

BADM 554 — Enterprise Database Management

Instructor: Vishal Sachdev

What does the course cover? BADM 554 is the first course of MSBAi: 8 weeks of database fluency for working professionals pivoting toward analyst work. Students learn SQL, schema design, and ETL on real enterprise data; no prior SQL required. Each week pairs a stakeholder conversation with a pre-AI / AI-mediated / post-AI workflow that treats AI as a thinking partner, not a shortcut.

What do students build? Teams of three adopt a stakeholder and a real dataset in Week 1, then spend 8 weeks building a portfolio GitHub repository: a dimensional warehouse, an ETL notebook ingesting from a live public data source, an AI Attribution Log, and a cloud-hosted version of the warehouse with a queryable URL. Each student defends the team’s work in an individual oral examination at Week 8.

Why does it matter for careers? SQL and schema judgment are baseline. What separates hireable analysts is talking to a non-technical stakeholder, working with AI without being replaced by it, and shipping a defensible artifact another person can audit. BADM 554 builds those three skills explicitly.

BDI 513 — Data Storytelling

Instructor: Ron Guymon

What does the course cover? The course begins by introducing basic visualization design principles. After evaluating and using exploratory visualizations, we turn our attention to explanatory visualizations used to tell a memorable, impactful story. Live sessions focus on principles, practicums focus on creating visualizations using Python and AI.

What do students build? Learners will generate a data story using a self-identified dataset (personal trajectory or external domain interest). The core deliverable is not a chart, but a comprehensive, narrative presentation (e.g., interactive dashboard, infographic, scrollytelling page). The story must move beyond basic visualization to provide genuine analytical insight and persuasive conclusion. During the last week, learners practice presenting their data stories and evaluating others’ data stories.

Why does it matter for careers? Once a researcher or a practitioner has completed the analyses of their data, they may assume that it is a simple process to communicate their findings to relevant stakeholders, but this is almost always an incorrect assumption. Proper data communication and storytelling begins even before data are analyzed and there are proven strategies to better connect the story behind and from the data to relevant stakeholders, especially within the context of business practice. This course will focus on helping learners better position themselves to successfully tell the persuasive story flowing from their data.

FIN 550 — Big Data Analytics in Finance

Instructors: Xing Gao & Mathias Kronlund

What does the course cover? FIN 550 is an 8-week course on predictive analytics for business decisions, where we use financial markets as our working laboratory. These markets offer great data for learning big-data techniques and testing models. No prior finance background is required; the course builds these foundations from the ground up. The material progresses from core financial and statistical concepts to modern machine learning, including event studies, factor models, Lasso, and XGBoost. Students learn to create a full predictive pipeline on real market data: how to measure returns and risk, how prices respond to news and corporate events, how to extract signals from both numerical and text data, and how to judge whether a signal is genuine. AI tools are integrated into the course to boost learning and enable quick prototyping of models and strategies.

What do students build? Student teams build a common deliverable for investment management analysts: a complete data analytics pipeline for an investment strategy based on real market data. This begins with a predictive signal or strategy students choose — a hypothesis about what might forecast returns — followed by finding, downloading, and cleaning data. Students then turn this data into what the model needs and measure its risk-adjusted performance, using models from classical finance to machine learning. These strategies can apply to stock markets (equities) or to other asset classes such as credit or real estate. To ensure robustness, they test whether the strategy holds up on data the model has never seen and across different time periods. They present and defend the work in a proposal and a final presentation, plus four short exercises.

Why does it matter for careers? As AI makes models and sophisticated data pipelines ever easier to create and run, employers increasingly reward analysts who can assess the performance and robustness of these models and steer them towards higher quality. FIN 550 develops that judgment: selecting a credible signal, validating it rigorously, and guarding against pitfalls such as look-ahead bias, survivorship bias, overfitting, and data mining. Students also learn to communicate results to a skeptical audience, stating clearly what works, what does not, and why. They use AI as a partner, not a substitute. The methods transfer well beyond finance to credit, real estate, text analytics, consumer behavior, and operations. Graduates can translate an ambiguous question into a defensible, evidence-based decision.

Spring 2027

BADM 590 — Quantum Approaches for Decision Making (4 credits, combined course)

Instructors: Nathan Yang (Part 1: Quantum Cognition, Weeks 1–4) / Abhijeet Ghoshal (Part 2: Quantum Computing, Weeks 5–8)

Official combined course description (confirmed 2026-06-10): This course pairs two quantum frameworks for working alongside AI, each running four weeks. In the first half, you use the mathematics of quantum probability — no physics or quantum hardware required — to model why human judgment systematically departs from the classical rationality of AI systems, learning to diagnose order effects, framing effects, and conjunction fallacies, then applying an audit methodology to AI-assisted decisions in your own organization. In the second half, you learn quantum computing from the ground up — core concepts, the underlying math, and the algorithms that solve complex business problems like optimization and route planning — building and testing hands-on solutions every week. AI tools accelerate exploration throughout. Portfolio artifact: An executive-ready decision audit and a quantum optimization solution.

Instructor talking points — Part 1: Quantum Cognition (Nathan Yang)

What does the course cover? Quantum cognition uses the mathematics of quantum probability — no physics, no quantum hardware — to model how human judgment systematically departs from classical rationality. The course is built on one observation: AI systems are classically rational; the humans who use and manage them are not, and that asymmetry is where most AI deployments fail. Students move from the empirical paradoxes classical decision theory cannot explain to a framework that explains them coherently, and from there to an audit methodology they apply to AI-assisted decisions in their own organizations.

What do students build? Each student conducts a four-week audit of a decision process from their professional experience, producing an executive-ready deliverable modeled on a worked example (e.g., the Wendy’s 2024 dynamic pricing controversy), with a Python pipeline that runs at three tiers — synthetic data, LLM-as-respondent prototyping, and real-respondent fieldwork. Students adapt the pipeline in Jupyter notebooks versioned in GitHub, then defend their findings against the standard objections a sophisticated colleague will raise.

Why does it matter for careers? Most AI deployments that fail in the wild are failing on the human side of the handoff — order-dependent judgments, framings that construct rather than reveal preferences, bundle meanings that don’t decompose. Classical decision theory and bias catalogs name these phenomena one at a time; quantum cognition gives them a single structural account, with falsifiable predictions and a vocabulary managers can actually use. Graduates leave able to diagnose where an AI deployment is hitting these patterns and design the handoff differently — a capability that classical frameworks, however sophisticated, do not currently teach.

Instructor talking points — Part 2: Quantum Computing (Abhijeet Ghoshal)

What does the course cover? This course introduces quantum computing from the ground up — starting with the core concepts and the math needed to understand how quantum computers work. It then moves into quantum algorithms and how they can solve complex business problems like route planning and investment optimization. The course wraps up by exploring the current state of quantum hardware and what the future looks like for companies looking to adopt this technology.

What do students build? Students get hands-on experience every week, building their own solutions for problems inspired by real-world decision-making scenarios. They learn to use Qiskit, a popular Python package for quantum computing, to develop and test these solutions. AI plays a central role throughout these activities, helping students explore, experiment, and deepen their understanding as they work through each challenge.

Why does it matter for careers? Quantum computing skills are extremely rare in the job market, and companies are already racing to get ahead — from Wall Street firms like Goldman Sachs to retailers like Walmart. Organizations urgently need business professionals who understand how to apply quantum methods to real decisions, not just engineers. This course provides that edge early, while the talent gap is still wide. The skills gained also strengthen broader quantitative abilities in AI, optimization, and analytics — making graduates more valuable across many roles.

BADM 558 — Big Data Infrastructures

Instructor: Ashish Khandelwal

Talking points not yet submitted. See ACTION_ITEMS.md.

BADM 590 — Agentic AI

Instructor: Vishal Sachdev

Talking points not yet submitted. See ACTION_ITEMS.md.

Summer 2027

BADM 557 — Business Intelligence

Instructor: Gautam Pant

What does the course cover? The course teaches students to build BI systems by starting with the business problem instead of the dataset. They move from a strategic or operational framework to the constructs it points to. Following this, they identify the data, measurements, and the validation process, leading to the decision inputs. Large language models and embeddings let students measure latent constructs in text that older methods could not capture, while graph methods do the same for network data. Students build and deploy a custom BI application using real financial and corporate data.

What do students build? Students build and deploy a BI application using real financial and corporate data. By the end, students know how to take a business framework, translate it into validated measures, and turn those measures into decision inputs generated by a BI app.

Why does it matter for careers? Managers and analysts can sometimes struggle to understand if the number produced by a system or model measures what the decision actually needs. In this course, you learn to defend a measure, expose where an AI model fails as an instrument, and turn a messy question into an input a decision-maker can trust. You also walk out with a deployed application built on real corporate data. That combination carries well into both analyst and managerial roles.

Fall 2027

BADM 576 — Data Science and Analytics

Instructor: Zilong Liu

What does the course cover? Building on the supervised machine learning foundations introduced in FIN 550 (Big Data Analytics in Finance), BADM 576 focuses on advanced machine learning methods and the transition from predictive models to production-ready analytics systems. Students learn advanced ensemble methods, clustering, dimensionality reduction, NLP, time series forecasting, neural networks, and deep learning. The course also introduces ML operations (MLOps), helping students understand not only how to build models, but also how to deploy, monitor, evaluate, and improve them in real-world business environments.

What do students build? Teams of three build a production-ready analytics system over eight weeks. Starting with a business problem and real dataset, teams develop advanced machine learning models incorporating techniques such as forecasting, clustering, NLP, and neural networks. They then package their solution into a deployable application with model documentation, testing, fairness analysis, and monitoring plans. Individual weekly assignments help students build the technical skills needed to contribute to the team project and demonstrate mastery of advanced machine learning methods.

Why does it matter for careers? Organizations increasingly need professionals who can move beyond building predictive models to delivering analytics solutions that create business value. BADM 576 develops the skills required to build, evaluate, deploy, and maintain machine learning systems in real-world environments. Students gain experience with modern analytics workflows, AI-assisted development, model governance, and deployment practices while building a portfolio-quality project that demonstrates their ability to apply advanced analytics and machine learning to complex business problems.

BADM 550 — Practicum

Instructor: Vanitha Virudachalam

What does the course cover? The practicum is the culmination of the MSBAi experience. Rather than introducing new content, the practicum gives students the opportunity to refine, synthesize, and showcase the skills they have developed throughout the program. Throughout this course, we will guide them in translating coursework into evidence of professional capability.

What do students build? In the first four weeks, students select their four strongest projects from prior courses and transform them into professional artifacts for an online portfolio. In the next four weeks, they choose one of those projects to further develop, integrating skills drawn from across the program to deepen its scope and sophistication. Alongside these artifacts, students produce reflective deliverables that connect their MSBAi skills to the value they will bring as employees, and they develop an elevator pitch to support their transition into industry.

Why does it matter for careers? Candidates who can demonstrate real ability, not just list credentials, will stand out in the analytics job market. This practicum has students integrate skills across different domains and produce polished deliverables that demonstrate those skills directly to potential employers. Students leave the program with a curated portfolio of real work, a clear narrative for articulating their value, and the confidence to present it, turning the degree into a competitive advantage from day one.