AI & Big Data: Opportunities and Business Insights

View All Courses

AI & Big Data: Opportunities and Business Insights

An introduction to the evolution, core technologies, and real-world frontiers of artificial intelligence and big data in business intelligence.

Applicants: 1

¥680

Enroll

Time commitment:

1-2 hours/week, 1-2 weeks

Pace:

Self-paced

Subject:

Management Science

Language:

English

Level:

Introductory

Topic (s):

E-commerce / Artificial Intelligence (AI) / Data

About this course

This course explores the frontiers and future prospects of artificial intelligence by examining its development through representative lenses such as big data-driven modeling, e-commerce applications, and the rise of multi-agent systems. Students will gain insights into the evolution of modern AI technologies, including large models and intelligent agents, and their transformative impact across domains.

What you’ll learn

Understand how data evolved into a key production factor.
Learn about the emergence and role of large AI models.
See how big data enables intelligent decision-making.
Get familiar with AI-powered recommendation in e-commerce.
Be introduced to the concept of LLM-based agents.
Learn how multi-agent systems simulate social interactions.

About the instructor(s)

Kai Wang

Associate Professor at Antai College of Economics and Management of Shanghai Jiao Tong University

Syllabus

Session
1

AI in the Age of Big Data - Foundations and Challenges

This session introduces the data-centric foundation of modern AI. We explore how the rise of big data — across text, graph, image, and video modalities — drives the emergence of large models. Key challenges such as data privacy, quality, and societal impacts are discussed alongside representative applications in industry.
Session
2

Artificial Intelligence for E-commerce

This session focuses on the challenges of applying AI to large-scale, heterogeneous, and dynamic e-commerce environments. We explore how transactional behaviors can be modeled using graph structures and how techniques like graph neural networks (GNNs) and large language models (LLMs) are leveraged for tasks such as recommendation, risk detection, and decision optimization.
Session
3

LLMs and Frontier Applications

This session explores the rise of large language models (LLMs) and their expansion into frontier use cases across domains. We examine how LLMs power zero-shot reasoning, personalized services, and real-time interactions. We also introduce multi-agent systems built on top of LLMs for complex tasks such as video understanding, medical QA, and social simulation.