Visual Analytics for Data Scientists using SAS
Subject code: MA5821:03
To be effective an organisation needs to be able to build its data assets by translating raw operational data into strategic information for decisions, data collaborations, personalising services to customers, for making predictions and for forecasting. A data scientist is one of the key contributors to this type of activity and should be able to identify and work with a range of tools and data structures to ensure organisations stay effective and efficient in a competitive world. In this unit, you will be encouraged to learn skills required to succeed in today’s highly analytical and data-driven economy using robust industry tested software for data science. This subject will introduce students to practical applications and concepts involved in advanced statistical modelling in SAS. Topics include:
- linear modelling with multiple predictor variables that maybe continuous or categorical in nature
- conditional Probability and the odds ratio
- drawing inferences
- checking model diagnostics and model selection
- techniques for coping with data that are temporally or spatially correlated.
Software platform: SAS exclusively 
Learning outcomes
- Demonstrate sound knowledge of the basic principles and theories that underpin advanced statistical modelling methods
- Effectively integrate and execute advanced statistical modelling theories and processes in SAS software to solve authentic problems
- Retrieve, analyse, synthesise and evaluate outputs produced using advanced statistical modelling methods in SAS software
- Critically examine different approaches to advanced statistical problems.
Assessment
Assessment for this course will occur at various times across the seven-week study period. Tasks may include online quizzes, discussion board activity, portfolio development, case studies, reflection, literature reviews presentations and reports.Feedback will be provided to you throughout the study period as well as a final grade at the conclusion of the study period.
This is one of the interdisciplinary subjects studied in the online Master of Data Science.
Please note, course structure and content are subject to change. For information on all course subjects download the course guide.
