In today's AI-powered business landscape, data fluency is essential for senior managers and entrepreneurs. This course provides hands-on experience in data management, AI-driven analytics, and developing AI-powered business solutions.

The first half of the course focuses on SQL for data ingestion, structuring, and retrieval. Students will gain expertise in managing corporate and publicly available datasets, preparing them for data-driven decision-making. In the second half, students transition to AI-driven analytics in Python, applying AI techniques to extract insights and automate processes. Generative AI tools, such as the ChatGPT API, will assist in coding, enabling the rapid development of AI-driven applications. By the end of the course, students will develop a working prototype of an AI-powered startup, integrating data management, analytics, and generative AI to solve business challenges.

Designed for MBA students pursuing leadership or entrepreneurial roles, this course equips graduates to navigate the modern technology-powered business landscape. No prior programming or analytics experience is required.

Computer systems

Database systems (mySQL)

ER Model

Data warehousing

Data governance

Case studies

Data Definition Language

Data Modification Language

SQL in MySQL

SQL in Python

Covers content from the first half of the course

Take home (due in 12 hours) and open book/notes/web/ChatGPT

Class time will be used as an extended office hour

Coding basics (Note: most of this content will be provided as asynchronous content to be completed at your own pace)

Python basics I (IDE, variables, data types, console I/O)

Python basics II (conditions, loops, lists, strings, dictionaries, files - csv, json, excel)

Error handling and debugging

Functions

Handling Data (Pandas)

Python & SQL

Application Programming Interface (API)

Data Visualization (plotly)

Large Language Models (LLM)

Python code geenration (e.g.,  ChatGPT)

Generative AI (e.g., OpenAI) API

Generative AI applications

Unsupervised Machine Learning (customer segmentation)

Supervised Machine Learning (prediction models)

Intro to RPA

RPA using Python

RPA using UIPath