STAT462-24S2 (D) Semester Two 2024 (Distance)

Data Mining

15 points

Details:
Start Date: Monday, 15 July 2024
End Date: Sunday, 10 November 2024
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 28 July 2024
  • Without academic penalty (including no fee refund): Sunday, 29 September 2024

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.

      Biculturally competent and confident

      Students will be aware of and understand the nature of biculturalism in Aotearoa New Zealand, and its relevance to their area of study and/or their degree.

      Globally aware

      Students will comprehend the influence of global conditions on their discipline and will be competent in engaging with global and multi-cultural contexts.

Prerequisites

Subject to approval of the Head of School.

Timetable 2024

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Wednesday 11:00 - 12:00 Jack Erskine 031 Lecture Theatre
15 Jul - 25 Aug
9 Sep - 20 Oct
Lecture B
Activity Day Time Location Weeks
01 Thursday 12:00 - 13:00 Rehua 005
15 Jul - 25 Aug
9 Sep - 20 Oct
Computer Lab A
Activity Day Time Location Weeks
01 Monday 13:00 - 14:00 Ernest Rutherford 464 Computer Lab
15 Jul - 25 Aug
9 Sep - 20 Oct
02 Tuesday 15:00 - 16:00 Jack Erskine 248 Computer Lab
15 Jul - 25 Aug
9 Sep - 20 Oct
03 Friday 09:00 - 10:00 Jack Erskine 001 Computer Lab
15 Jul - 25 Aug
9 Sep - 20 Oct
04 Friday 12:00 - 13:00 Online Delivery
15 Jul - 25 Aug
9 Sep - 20 Oct
Tutorial A
Activity Day Time Location Weeks
01 Friday 11:00 - 12:00 A5 Lecture Theatre
15 Jul - 25 Aug
9 Sep - 20 Oct

Course Coordinator

Heyang (Thomas) Li

Lecturer

Philipp Wacker

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 $1,074.00

* 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 .

All STAT462 Occurrences