STAT462-22S1 (D) Semester One 2022 (Distance)

Data Mining

15 points

Details:
Start Date: Monday, 21 February 2022
End Date: Sunday, 26 June 2022
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 6 March 2022
  • Without academic penalty (including no fee refund): Sunday, 15 May 2022

Description

Data Mining

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

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

Prerequisites

Subject to approval of the Head of School.

Course Coordinator

Gabor Erdelyi

Assessment

Assessment Due Date Percentage 
Assignments (x3) 60%
Final examination 40%

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 $1,017.00

* 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