The University of Hong Kong Faculty of Business and Economics
HKU Business School logo

Faculty of Business & Economics

The University of Hong Kong
IIMT3642 Course Teaching Site

Managing and Mining the Big Data

A practical course website for HKU undergraduates to explore data preparation, prediction, segmentation, recommendation, and network thinking through applied business cases, visual explanations, and downloadable lecture materials.

Explore Weekly Modules

Course Experience

The site is organized for teaching: students can quickly see what the course covers, how they will be assessed, and where to find each session deck without hunting through folders.

Concept to Application

Each topic starts with a business decision context, then introduces the relevant analytic method and its managerial implications.

Structured Weekly Rhythm

Students can follow a simple rhythm of pre-class reading, lecture slides, worked examples, and follow-up discussion each week.

Career Relevance

The course language emphasizes consulting, product, finance, marketing, and operations use cases familiar to business students.

Business Digest

Daily updates compiled from BBC and Fox News so students can connect analytics ideas to real business reporting and class discussion.

Daily Highlights

Loading today’s lead story...

The digest will populate from the compiled source file after the refresh script runs.

Awaiting first refresh

Refresh Workflow

Run python3 scripts/build_digest.py to compile the latest headlines into the site.

Digest Sources

Setup

The website will render one source card for each configured publisher after the digest JSON has been refreshed.

Weekly Modules

Lecture materials already available in the project are linked below as a teaching-ready course sequence.

Week 1
Introduction to Business Analytics Course framing, business value of analytics, and how data-informed decisions differ from intuition-led choices.
Slides restricted
Week 2
Data Exploration Descriptive patterns, visual summaries, outlier spotting, and the first questions analysts should ask when meeting a new dataset.
Slides restricted
Week 3
Data Preparation Cleaning, transforming, and structuring raw information so downstream models can be interpreted and trusted.
Slides restricted
Week 4
Classification with Decision Trees One session organized into three subsessions covering decision rules, model refinement, and practical interpretation for business decisions.
Subsession 1 restricted Subsession 2 restricted Subsession 3 restricted
Week 5
Naive Bayes, Logistic Regression, and SVM Comparing classification families and understanding when different models are appropriate for different decision settings.
Slides restricted
Week 6
Support Vector Machines Margins, boundaries, and how to explain powerful but less intuitive classifiers in a business-friendly way.
Slides restricted
Week 7
Association Analysis Basket patterns, co-occurrence logic, and the managerial uses of rules in retail, digital commerce, and service design.
Slides restricted
Week 8
Clustering Segmentation, similarity, and how to derive strategically meaningful customer or product groupings from data.
Slides restricted
Week 9
Recommendation Systems Personalization, filtering, and the opportunities and limits of recommending products, content, and experiences.
Slides restricted
Week 10
Network Analysis Connections among people, products, or firms, and how network structure can reveal influence, communities, and diffusion.
Slides restricted
Week 11
Artificial Neural Networks Neural network foundations, business applications, and how layered models can support prediction tasks when relationships are complex.
Slides restricted

Assessment

A balanced structure for undergraduate learning, with space for both quantitative practice and business communication.

Class Participation and Polling 10%
Individual Analytics Reflection 15%
Midterm Quiz 25%
Group Business Case Project 30%
Final Applied Analysis Assignment 20%

Learning Resources

Use these sections to keep expectations clear and reduce repeated student questions during the term.

Lecture decks PDF
Weekly business cases Canvas
Consultation hours In person
Project templates Download
Revision guide Week 11

Teaching Team

The page is ready for real course details to be dropped in before publication on GitHub Pages, Moodle, or an internal HKU hosting setup.

Instructor: Dr. Zhepeng (Lionel) Li

Email: zpli@hku.hk

Office Hours: TBD by appointment

Venue: KK Leung Building, Main Campus

Site Notes

This prototype is designed to feel like a finished course microsite while staying editable as a single HTML file.

Update the title, instructor details, and assessment breakdown to match the actual course outline.

The lecture links currently point to the slide PDFs already included in the project assets.

The layout is responsive and intended to work cleanly on laptop and mobile screens.