42050 SAS Predictive Business Analytics

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2025 is available in the Archives.

UTS: Information Technology: Computer Science
Credit points: 6 cp

Subject level:

Postgraduate

Result type: Grade and marks

Requisite(s): 31250 Introduction to Data Analytics OR 32130 Fundamentals of Data Analytics OR 48 credit points of completed study in spk(s): C04379 Master of Business Analytics (Extension)

Description

Predictive business analytics is used to predict unknown future events by analysing historical business data and plays a critical function in data-driven business. This subject covers the theoretical foundations of predictive business analytics. A series of demonstrations and exercises are used to reinforce concepts and analytical approaches to solving business problems. Students engage with case studies to guide them through all steps of the predictive analytical life cycle, from problem understanding to model deployment, through data preparation, feature selection, model training and validation, and model assessment. Model Studio is the pipeline flow interface in SAS Viya that enables students to prepare, develop, compare, and deploy advanced analytics models. Students learn to develop and train supervised machine learning models to make better decisions using big data. The SAS applications used in this course make machine learning and predictive analytics possible with a limited amount of programming or coding. Upon completion, students are prepared to sit the optional SAS certification exam, “SAS Certified Specialist: Machine Learning Using SAS Viya”.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1.	Explore and transform business data into analytic-ready features for predictive modelling. (D.1)
2.	Design and implement predictive models for predicting unknown future events in business environment. (C.1)
3.	Evaluate and deploy predictive models in business production environment. (D.1)

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):

Design Oriented: FEIT graduates apply problem solving, design thinking and decision-making methodologies in new contexts or to novel problems, to explore, test, analyse and synthesise complex ideas, theories or concepts. (C.1)
Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)

Contribution to the development of graduate attributes

Engineers Australia Stage 1 Competencies

This subject contributes to the development of the following Engineers Australia Stage 1 Competencies:

1.2. Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
1.3. In-depth understanding of specialist bodies of knowledge within the engineering discipline.
2.2. Fluent application of engineering techniques, tools and resources.
2.3. Application of systematic engineering synthesis and design processes.
3.3. Creative, innovative and pro-active demeanour.

Teaching and learning strategies

This subject will be delivered as a combination of interactive lectures (1.5 hours per week) and tutorial sessions (1.5 hours per week). Lectures will discuss the theoretical foundation of predictive analytics techniques intertwined with interactive demonstrations. Preparation content such as online readings or videos will be provided before lectures to give the background of each week’s topic. Following each week’s lecture, there will be a tutorial session providing a series of hands-on exercises to reinforce the concepts discussed in lectures.

Non-assessed quizzes will be made available in UTSOnline for students to do self-assessment and reflection about the learning of each week. Forums in UTSOnline will also be used for students to discuss with each other as well as asking questions to the teaching staff.

Throughout the semester, students are assessed by a combination of graded group assignments and presentations. The assessment provides diagnostic feedback to students on how they are progressing. Students are also encouraged to discuss draft work with teaching staff before submission.

Content (topics)

Topic 1: Getting Started with predictive analytics and SAS environment
Topic 2: Data Preparation and Algorithm Selection
Topic 3: Decision Trees and Ensembles of Trees
Topic 4: Neural Networks
Topic 5: Support Vector Machines
Topic 6: Model Deployment and Evaluation

Assessment

Assessment task 1: Data explortion

Objective(s):	This assessment task addresses the following subject learning objectives (SLOs): 1 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1
Type:	Report
Groupwork:	Group, group and individually assessed
Weight:	30%
Length:	15 pages max in an 11 or 12-point font.

Assessment task 2: Predictive modelling

Objective(s):	This assessment task addresses the following subject learning objectives (SLOs): 2 and 3 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1 and D.1
Type:	Report
Groupwork:	Group, group and individually assessed
Weight:	50%
Length:	25 pages max in an 11 or 12-point font.

Assessment task 3: Oral presentation

Objective(s):	This assessment task addresses the following subject learning objectives (SLOs): 1, 2 and 3 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1 and D.1
Type:	Presentation
Groupwork:	Group, group and individually assessed
Weight:	20%
Length:	Oral presentation of 15 minutes per group.

Minimum requirements

To pass this subject, students must achieve an overall mark of 50% or greater.

Required texts

1. Machine Learning Using SAS Viya handbook provided free of charge by SAS

2. Data mining techniques for marketing, sales, and customer relationship management Linoff, Gordon S., and Michael JA Berry, Wiley, Third Edition 2022

Recommended texts

1. Machine Learning, Thomas M. Mitchell, McGraw-Hill, 1997.
2. The Elements of Statistical Learning, T. Hastie, R. Tibshirani, and J. Friedman, Springer, 2009.
3. Pattern Recognition and Machine Learning, Christopher Bishop, Springer, 2006.