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Programming for Data Science G (11521.1)

Level: Graduate Level
Credit Points: 3
HECS Bands: 2
Faculty: Faculty of Science and Technology
Discipline: Academic Program Area - Technology

Availability

Possible changes to your unit's learning activities and assessment items

For the remainder of 2020, resulting from Australian Government's directives requiring physical distancing and restrictions on movement because of the COVID-19 pandemic, any exams that are required for assessment in a unit will be online exams. Online exams may also use online proctoring to help assure the academic integrity of those exams. Please contact your unit convener with any questions.

While the University has made efforts to ensure that Unit Outlines reflect a unit’s learning activities and assessment items, any changes to Australian Government directives because of the COVID-19 pandemic may require changes to these during the semester to ensure the safety and well being of students and staff. 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. Any changes made will continue to meet the unit’s learning outcomes, as described in the Unit Outline.

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.

Syllabus

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.

Prerequisites

None.

Corequisites

None.

Assumed Knowledge

Working knowledge of discrete mathematics, algebra and numerical analysis.

Incompatible Units

None.

Equivalent Units

None.


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