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INFO304 Advanced Data Science

Computational methods for visualising, transforming, modelling and assessing data to allow informed decision making, prediction and knowledge construction.

Data Science and Data Analytics are a fundamental aspect of all business decision making. This paper will give the student a solid foundation in the concepts and methods for this field. Emphasis will be made on how this relates to business processes and the use of modelling and visualisation in supporting and delivering decision making.

Paper title Advanced Data Science
Paper code INFO304
Subject Information Science
EFTS 0.15
Points 18 points
Teaching period Semester 2 (On campus)
Domestic Tuition Fees (NZD) $1,141.35
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

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INFO 204
INFO 324
Schedule C
Arts and Music, Commerce, Science

Teaching staff

Associate Professor Peter Whigham

Paper Structure

The paper is presented as a series of lectures (two per week), a tutorial (one per week) and a laboratory session (one 2-hour session per week). This will allow the conceptual approaches to be presented and explored (lectures and tutorial) followed by hands-on experience using the R programming environment (lab). Assessment will be written and programming assignments.


"An Introduction to Statistical Learning", by G.James, D. Witten, T. Hastie & R. Tibshirani (available online through the Library)

Course outline
View the most recent Course Outline
Graduate Attributes Emphasised
Lifelong learning, Communication, Critical thinking, Information literacy.
View more information about Otago's graduate attributes.
Learning Outcomes
  • Identify the activities of prediction, optimisation, and adaptation that exist within a business process;
  • Assess the suitability of data sources with respect to the requirements of modelling and decision making;
  • Apply a range of suitable methods to perform prediction and modelling for a range of data types within the context of Data Science and Analytics.

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Semester 2

Teaching method
This paper is taught On Campus
Learning management system

Computer Lab

Stream Days Times Weeks
Attend one stream from
A1 Monday 14:00-15:50 28-34, 36-41
A2 Tuesday 14:00-15:50 28-34, 36-41


Stream Days Times Weeks
A1 Monday 10:00-10:50 28-34, 36-41
Tuesday 10:00-10:50 28-34, 36-41


Stream Days Times Weeks
A1 Wednesday 15:00-15:50 28-34, 36-41