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FIT5145 · Foundations of data science

Official Handbook

2026 Handbook6 credit pointsLevel 5Faculty of Information Technology

Last checked: 23 Aug 2026 UTC

Overview

This unit looks at processes, case studies and simple tools to understand the many facets of working with data, and the significant effort in Data Science over and above the core task of Data Analysis. Working with data as part of a business model and the lifecycle in an organisation is considered, as well as business processes and case studies. Data and its handling is also introduced: characteristic kinds of data and its collection, data storage and basic kinds of data preparation, data cleaning and data stream processing. Styles of data analysis and outcomes of successful data exploration and analysis are reviewed. Standards, tools and resources are also reviewed. Basic curation and management are reviewed: archival and architectural practice, policy, legal and ethical issues.

Offerings

CampusTeaching periodMode
MalaysiaSecond semesterTeaching activities are on-campus (ON-CAMPUS)
ClaytonFirst semesterFlexible (FLEXIBLE)
MalaysiaFirst semesterTeaching activities are on-campus (ON-CAMPUS)
ClaytonSecond semesterFlexible (FLEXIBLE)

Assessment

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

#AssessmentTypeWeightHurdle
1Propose a Data Science ProjectWritten15%
2Data Analysis with R (Coding task I)Artefact15%
3Report on the Data Science ProjectWritten30%
4Data Analysis with Tools and Scripting (Coding task II)Artefact40%

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

Requisites

prohibitions

  • ETC5510 — Introduction to data analysis
  • ITI5145 — Introduction to data science
  • ITO5145 — Introduction to data science

Joined by AND.

prerequisite

  • FIT9136 — Algorithms and programming foundations in Python
  • FIT9131 — Programming foundations in Java
  • FIT9133 — Programming foundations in python

Joined by OR.

Learning outcomes

  1. Analyse the role of data in organisations, including curation and management issues;
  2. Apply basic tools for performing exploratory data analysis and visualisation;
  3. Apply basic predictive modelling methods and integrate data analysis results to guide real-world decision making;
  4. Apply basic tools for managing and processing big data;
  5. Identify data resources, data storage, processing requirements, and recognised standards for a data science project;
  6. Recognise and analyse ethical and security challenges inherent to data science.

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.

ActivityDuration
Seminars24 hours
Applied sessions22 hours

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