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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

Paper title Advanced Data Science
Paper code INFO304
Subject Information Science
EFTS 0.1500
Points 18 points
Teaching period Not offered in 2017
Domestic Tuition Fees (NZD) $1,018.05
International Tuition Fees (NZD) $4,320.00

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

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Timetable

Not offered in 2017

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

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 0.1500
Points 18 points
Teaching period Second 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.

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Prerequisite
INFO 204
Restriction
INFO 324
Schedule C
Arts and Music, Commerce, Science
Teaching staff
Associate Professor Peter Whigham
Textbooks
ÔÇ£An Introduction to Statistical LearningÔÇØ, by G.James, D. Witten, T. Hastie & R. Tibshirani (available online through the Library)
Graduate Attributes Emphasised
Lifelong learning, Critical thinking, Information literacy.
View more information about Otago's graduate attributes.
Contact
Associate Professor Peter Whigham
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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Timetable

Second Semester

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

Computer Lab

Stream Days Times Weeks
Attend
A1 Monday 14:00-15:50 28-34, 36-41

Lecture

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

Tutorial

Stream Days Times Weeks
Attend
A1 Wednesday 13:00-13:50 28-34, 36-41