STAT318-19S1 (C) Semester One 2019

Data Mining

15 points

Details:
Start Date: Monday, 18 February 2019
End Date: Sunday, 23 June 2019
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Friday, 1 March 2019
  • Without academic penalty (including no fee refund): Friday, 10 May 2019

Description

Parametric and non-parametric statistical methodologies and algorithms for 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

i) 15 points from STAT200 to STAT299 and ii) a further 15 points from STAT200 to STAT299 or COSC200-299 or any other relevant subject with Head of School approval.

Course Coordinator / Lecturer

Blair Robertson

Lecturers

Heyang (Thomas) Li and Nicholas Ward

Assessment

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


You cannot pass this course unless you achieve a mark of at least 16/40 in the final exam.

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 $764.00

International fee $4,000.00

* 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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