STAT462-25S1 (D) Semester One 2025 (Distance)

Data Mining

15 points

Details:
Start Date: Monday, 17 February 2025
End Date: Sunday, 22 June 2025
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 2 March 2025
  • Without academic penalty (including no fee refund): Sunday, 11 May 2025

Description

Data Mining

This occurrence of the course is for online students only. On-campus students should enrol in the (C) occurrence of this course.

STAT462 is a course in statistical learning and data mining, suited to anyone with an interest in analysing large datasets. This course will introduce a variety of statistical learning and data mining techniques for classification, regression, clustering and association purposes. Possible topics include, classification and regression trees, random forests, Apriori algorithm, FP-growth algorithm and support vector machines. The lectures will be supplemented with laboratory sessions using the statistical software package R.

Learning Outcomes

The courses will:
• introduce statistical learning and data mining
• introduce advanced data analysis techniques for classification, regression, cluster analysis and association analysis
• introduce the use of the statistics software package R

You will be able to:
• describe and conduct appropriate statistical modeling techniques
• be able to interpret the analysis results in such a way that a non-user of statistics can understand
• Use R competently
• Write a scientific and technical report

University Graduate Attributes

This course will provide students with an opportunity to develop the Graduate Attributes specified below:

Employable, innovative and enterprising

Students will develop key skills and attributes sought by employers that can be used in a range of applications.

Prerequisites

Subject to approval of the Head of School.

Course Coordinator

Heyang (Thomas) Li

Assessment

Assessment Due Date Percentage  Description
Assignment 1 15%
Assignment 2 15%
Assignment 3 15%
Quizzes 10% 10 LEARN quizzes (1% each)
Final Examination 45% In order to be able to pass the course, participants also need to achieve at least 40% of marks in the final examination.

Textbooks / Resources

Recommended Reading

Hastie, Trevor. , Tibshirani, Robert., Friedman, J. H; The elements of statistical learning : data mining, inference, and prediction ; 2nd ed; Springer, 2009.

James, Gareth; An introduction to statistical learning : with applications in R ; Springer, 2013.

Indicative Fees

Domestic fee $1,138.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

For further information see Mathematics and Statistics .

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