SDSC3002Data Mining

What you will study

Course overview

Study foundational data-mining techniques, data preparation and classical algorithms, apply them in Python, develop data-driven models, and interpret and communicate results.

Official PDF, page 2 · Abstract

Course content

Data representation and similarity; cleaning and preprocessing; association-rule mining; classification; clustering; and applied data-mining case studies.

Official PDF, page 6 · Keyword Syllabus

Intended learning outcomes

View learning outcomes

CILO	Intended learning outcome	Weight	DEC
1	Understand data and data-preprocessing methods.	20%	—
2	Become familiar with classical clustering and classification algorithms.	30%	—
3	Use software such as R or Python to implement data-mining algorithms and conduct analysis.	30%	—
4	Interpret data-mining results.	20%	—

Official PDF, page 2 · Course Intended Learning Outcomes (CILOs)

Before you take this course

Check prerequisites, precursors and corequisites separately, together with equivalence and exclusion conditions. “and” means conditions must be met together; “or” marks alternatives. Read combined conditions in the groupings shown in the original text below.

Prerequisites

SDSC2001 Python for Data Science and either MS2602 Statistical Inference or SDSC2102 Statistical Methods and Data Analysis.

Official PDF, page 1 · Prerequisites

Precursors

Nil

Official PDF, page 1 · Precursors

Corequisites

Complete official information for this field is not available.

Additional course requirements

Complete official information for this field is not available.

Equivalent courses and conditions

Nil

Official PDF, page 1 · Equivalent Courses

Exclusive courses and conditions

Nil

Official PDF, page 1 · Exclusive Courses

Assessment and passing the course

Assessment

Continuous assessment: 60% (test 40%, laboratory report/participation 20%); examination: 40% (2 hours). The source requires at least 30% of the maximum examination mark to pass.

Assessment breakdown

Assessment task	Weight	Generative AI	Remarks and restrictions
Test	40%	Not stated in source	Not stated in source row
Laboratory report / participation	20%	Not stated in source	Not stated in source row

Official PDF, page 4 · Assessment Tasks/Activities

Passing requirements

At least 30% of the maximum mark for the examination.

Official PDF, page 4 · Remark