FIT3152 · Data analytics

Overview

There has been an explosion in the quantity and variety of data collected and routinely analysed by government, business and society at large over recent years. This has been described by some social commentators as the rise of "big data" and and the analysts and practitioners who investigate this data as "data scientists." This unit will introduce you to the analysis of big data and the role of the data scientist. Techniques covered include data management and transformation, visual analysis, social network analysis, statistical learning, clustering and natural language processing. You will be introduced to these methods using open source industry standard software. Data and case studies will be drawn from diverse sources. The general principles of analysis, investigation and reporting will be covered. You will be encouraged to critically reflect on the data analysis process within your own domain of interest.

Areas of study: Advanced computer science Business information systems Computational science Data science IT for business

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Demonstrate the ability to transform real world problems into ones that can then be solved using data analytics techniques;
2. Cleanse and prepare data for analysis;
3. Analyse large data sets using a range of statistical, graphical and machine-learning techniques;
4. Validate and critically assess the results of analysis;
5. Interpret the results of analysis and communicate these to a broad audience.

Workload

Applied sessions are scheduled from week 2 to week 12. Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.

Student experience, not official rules. Nothing here changes what the Handbook says.