FIT5149 · Applied data analysis

Overview

This unit aims to provide you with the necessary analytical and data modeling skills for the roles of a data scientist or business analyst. You will be introduced to established and contemporary Machine Learning techniques for data analysis and presentation using widely available analysis software. They will look at a number of characteristic problems/data sets and analyse them with appropriate machine learning and statistical algorithms. Those algorithms include regression, classification, clustering and so on. The unit focuses on understanding the analytical problems, machine learning models, and the basic modeling theory. You will need to interpret the results and the suitability of the algorithms

Areas of study: Advanced data analytics Data science

Offerings

Assessment

The Handbook lists an examination for this unit.

This unit has threshold mark hurdles. You must achieve at least 40% of the available marks in the final scheduled assessment, at least 45% in total for in-semester assessments, and an overall unit mark of 50% or more to be able to pass the unit. If you do not achieve the threshold mark, you will receive a fail grade (NH) and a maximum mark of 45 for the unit.

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

Requisites

Learning outcomes

1. Analyse data sets with a range of statistical, graphical and machine-learning tools;
2. Evaluate the limitations, appropriateness and benefits of data analytics methods for given tasks;
3. Design solutions to real world problems with data analytics techniques;
4. Assess the results of an analysis;
5. Communicate the results of an analysis for both specific and broad audiences.

Workload

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.