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INFO324 Adaptive Business Intelligence

The techniques of data science used to produce predictive and adaptive decision support techniques with particular emphasis on prediction, optimisation and search methods.

All businesses make decisions based on an understanding of their historical, current and predicted environments. Adaptive Business Intelligence introduces methods for supporting robust decision making in a business context, leading to graduates with the confidence to visualise, understand and predict relevant aspects of a business process. Given the future of improving business processes is to understand what has happened and what will happen, INFO 324 delivers some fundamental skills that all business graduates need.

Paper title Adaptive Business Intelligence
Paper code INFO324
Subject Information Science
EFTS 0.1500
Points 18 points
Teaching period First Semester
Domestic Tuition Fees (NZD) $1,018.05
International Tuition Fees (NZD) $4,320.00

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BSNS 106 and 18 200-level INFO points and 18 further 200-level points
Recommended Preparation
BSNS 102 or one STAT paper
Schedule C
Arts and Music, Commerce, Science
Teaching staff
Convenor and Lecturer: Associate Professor Peter Whigham
Paper Structure
The paper covers three main themes:
  • Visualisation of data
  • Model building for prediction and analyses
  • Business applications
All text books are available online via Blackboard. A number of text books are offered. The student is directed to particular chapters as required.
Course outline
View the course outline for INFO 324
Graduate Attributes Emphasised
Interdisciplinary perspective, Scholarship, Communication, Critical thinking, Information literacy, Self-motivation.
View more information about Otago's graduate attributes.
Learning Outcomes
  • Ability to identify the activities of prediction, optimisation and adaption that exist within a decision-making context
  • Be confident in applying methods to understand and critically assess these activities

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First Semester

Teaching method
This paper is taught On Campus
Learning management system

Computer Lab

Stream Days Times Weeks
Attend one stream from
A1 Tuesday 14:00-15:50 9-15, 18-22
A2 Thursday 14:00-15:50 9-15, 17-22


Stream Days Times Weeks
L1 Monday 12:00-12:50 9-15, 17-22
Tuesday 09:00-09:50 9-15, 18-22


Stream Days Times Weeks
T1 Wednesday 13:00-13:50 9-15, 17-22