STAT462-18S1 (C) Semester One 2018

Data Mining

15 points

Details:
Start Date: Monday, 19 February 2018
End Date: Sunday, 24 June 2018
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 4 March 2018
  • Without academic penalty (including no fee refund): Sunday, 20 May 2018

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

Prerequisites

Subject to approval of the Head of School.

Course Coordinator

Blair Robertson

Assessment

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

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 $950.00

* 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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