Course Detail (Course Description By Faculty)

• become fluent in the language of “data science and AI”,
• learn how to effectively communicate uncertainty,
• cultivate the ability to craft compelling narratives grounded in data,
• introduce students to data analytics for informed decision-making in uncertain environments,
• develop skills in analyzing and exploring large datasets,
• build and interpret (predictive) models with confidence.

Designed for students preparing for careers in data-driven environments, the course emphasizes practical concepts and tools commonly used by data scientists in business contexts. Rather than
focusing on coding, the course prioritizes data storytelling–the ability to interpret, analyze, and communicate insights from data.

Each lecture features the analysis of two to three real-world datasets, demonstrated live in class. Examples include consumer database mining, internet and social media tracking, asset pricing,
network analysis, sports analytics, and text mining.

The curriculum spans topics from classical statistics (e.g. hypothesis-driven decisions), data science (dimensionality reduction) to modern machine learning techniques (e.g deep learning). It
also explores cutting-edge advancements in generative AI. The course puts a particular emphasis on the analysis of text data in the context of both small and Large Language Models (LLM) that
form a basis of popular text-generating systems. Techniques covered include large-scale testing and false discovery rates, modern regression and model choice, machine-learning based classification,
network analysis, language and topic models, principal components, clustering, Bayesian analysis, deep learning, transformers and attention.

By the end of the course, students will be equipped to perform machine-supported intelligent data analysis and communicate findings effectively.

OPTIONAL PREREQUISITES
Students might benefit most from this course if they have had prior exposure to basic concepts in probability, such as random variables and normal distributions. That said, these foundational topics
are reviewed/introduced during the course, so students without a formal background in statistics can still succeed–though they may experience a steeper learning curve early on.

This course might appeal to students who have already taken Business Statistics (BUS 41000) or Applied Regression (BUS 41100). Another possibly useful prerequisite is Data Analysis with R
and Python (BUS 32100) and Artificial Intelligence (BUS 32200).

Cannot enroll in BUSN 41215 if 41201 taken previously.

This course is designed for those with a strong interest in hands-on data analysis using real-world datasets, rather than purely abstract conversations.

POSSIBLE POSTREQUISITES
This course includes the key concepts and tools that data scientists find valuable in business environments, and it is also designed to act as a primer for continued study.
The course may be a useful prerequisite into deep-dive courses and/or field-specific variants such as Machine Learning in Finance (BUS 35137), Machine Learning (BUS 41204), Generative
Thinking (BUS 32210), Data Science for Marketing Decision Making (BUS 37105), Data-driven Marketing (BUS 37103), Causal Inference in Business Applications (BUS 41207).

Grades will be determined by homework (20%), a take-home midterm exam (45%), and a final project (35%). Late assignments, exams, or projects, will not be accepted.