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Programming for Data Science G (11521.1)

Level: Graduate Level
Credit Points: 3
HECS Bands:

Band 2 2013-2020 (Expires 31 Dec 2020) Band 2 2021 (Commenced After 1 Jan 2021) Band 3 2021 (Commenced Before 1 Jan 2021)

Faculty: Faculty of Science and Technology
Discipline: Academic Program Area - Technology


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As a result of the Australian Government's and or the ACT Government’s directives requiring physical distancing and restrictions on movement because of the COVID-19 pandemic, you may find that learning activities and/or assessment items in some units you are studying have changed. These changes will not be updated in the published Unit Outline but will be communicated to you via your unit’s UCLearn(Canvas) teaching site. The new learning activities and/or assessment items will continue to meet the unit's learning outcomes, as described in the Unit Outline.

New learning activities and/or assessment items are available on your unit's UCLearn(Canvas) teaching site. Please contact your Unit Convener with any questions.

Unit Outlines

To view your Unit Outline, click View to log in to MyUC and access this information, or visit your unit's online teaching site.

  • Semester 2, 2020, ON-CAMPUS, BRUCE (192333) - View
  • Semester 2, 2019, ON-CAMPUS, BRUCE (192447) - View

If a link to your Unit Outline is not displayed, please check back later. Unit Outlines are generally published by Week One of the relevant teaching period.


Beyond the foundational knowledge of using R for data science, data scientists need to be proficient in valuable programming tools and languages that form the basis of advanced analysis and modelling techniques. Using real-world data sets and a problem-solving framework, students in this unit will learn industry-standard programming skills and best practises for data organisation, data manipulation, data interrogation and data modelling relevant to data science. Students will develop a sound understanding of technical programming in one or more languages and related computational modelling and analysis tools. This includes skills in software engineering, such as writing correct code, working with version control tools, testing and debugging. A particular emphasis will be on tackling real-world data science problems as found in industry.

Learning Outcomes

After successful completion of this unit, students will be able to:

1. Learn fundamentals of programming in Python and/or other relevant languages;

2. Develop a sound understanding of the most common libraries and tools;

3. Learn how to connect to large databases;

4. Demonstrate strong skills in good software engineering practices, such as writing correct code, testing and debugging, based on industry standards and best practices;

5. Learn how to use version control tools;

6. Critically reflect on available computational tools for solving data science problems; and

7. Successfully demonstrate practical skills in developing solutions to data science problems.

Assessment Items

Contact Hours

Four hours of problem-based learning activities, interactive workshops and practical work in laboratory classes on campus per week.





Assumed Knowledge

Working knowledge of discrete mathematics, algebra and numerical analysis.

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