Accessibility Skip to Global Navigation Skip to Local Navigation Skip to Content Skip to Search Skip to Site Map Menu

INFO424 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 424 delivers some fundamental skills that all business graduates need.

Paper title Adaptive Business Intelligence
Paper code INFO424
Subject Information Science
EFTS 0.1667
Points 20 points
Teaching period First Semester
Domestic Tuition Fees (NZD) $1,256.92
International Tuition Fees (NZD) $5,151.03

^ Top of page

Prerequisite
BSNS 102 or STAT 110
Restriction
INFO 324
Limited to
MA, MBus, MCom, MSc, MAppSc, MBusDataSc, BA(Hons), BAppSc(Hons), BCom(Hons), BSc(Hons), PGDipAppSc , PGDipArts, PGDipCom, PGDipSci, PGCertAppSc
Contact
peter.whigham@otago.ac.nz
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
Textbooks
All textbooks are available online via Blackboard. A number of textbooks are offered. The student is directed to particular chapters as required.
Course outline
View the most recent Course Outline
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

^ Top of page

Timetable

First Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Blackboard

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

Lecture

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

Tutorial

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

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 424 delivers some fundamental skills that all business graduates need.

Paper title Adaptive Business Intelligence
Paper code INFO424
Subject Information Science
EFTS 0.1667
Points 20 points
Teaching period First Semester
Domestic Tuition Fees Tuition Fees for 2018 have not yet been set
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

^ Top of page

Prerequisite
BSNS 102 or STAT 110
Restriction
NFO 304, INFO 324
Limited to
MA, MBus, MCom, MSc, MAppSc, MBusDataSc, BA(Hons), BAppSc(Hons), BCom(Hons), BSc(Hons), PGDipAppSc , PGDipArts, PGDipCom, PGDipSci, PGCertAppSc
Contact
peter.whigham@otago.ac.nz
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
Textbooks
ÔÇ£An Introduction to Statistical LearningÔÇØ, by G.James, D. Witten, T. Hastie & R. Tibshirani. This textbook is available online through the library.
Course outline
View the most recent Course Outline
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

^ Top of page

Timetable

First Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Blackboard

Computer Lab

Stream Days Times Weeks
Attend
A1 Thursday 14:00-15:50 9-13, 15-22

Lecture

Stream Days Times Weeks
Attend
L1 Monday 12:00-12:50 9-13, 15-22
Tuesday 09:00-09:50 9-13, 15-22

Tutorial

Stream Days Times Weeks
Attend
T1 Wednesday 13:00-13:50 9-13, 15-16, 18-22