STAT462-20S2 (C) Semester Two 2020

Data Mining

15 points

Details:
Start Date: Monday, 13 July 2020
End Date: Sunday, 8 November 2020
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Friday, 24 July 2020
  • Without academic penalty (including no fee refund): Friday, 25 September 2020

Description

Data Mining

STAT318 and STAT462 are courses in statistical learning and data mining, suited to anyone with an interest in analysing large datasets. The courses 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

Pre-requisites

Subject to approval of the Head of School.

Timetable 2020

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Monday 13:00 - 14:00 A3 Lecture Theatre 13 Jul - 23 Aug
7 Sep - 18 Oct
Lecture B
Activity Day Time Location Weeks
01 Friday 13:00 - 14:00 Rehua 009 13 Jul - 23 Aug
7 Sep - 18 Oct
Computer Lab A
Activity Day Time Location Weeks
01 Tuesday 11:00 - 12:00 Ernest Rutherford 464 Computer Lab 13 Jul - 23 Aug
7 Sep - 18 Oct
03 Tuesday 12:00 - 13:00 Ernest Rutherford 464 Computer Lab 13 Jul - 23 Aug
7 Sep - 18 Oct

Course Coordinator / Lecturer

Blair Robertson

Lecturer

Jennifer Wilcock

Textbooks / Resources

G. James, D. Witten, T. Hastie and R. Tibshirani, An Introduction to Statistical Learning with Applications in R. (2014) Springer

Recommended reading:
T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, Data Mining, Inference, and Prediction. (2013) Springer.

Indicative Fees

Domestic fee $989.00

* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.

For further information see Mathematics and Statistics.

All STAT462 Occurrences