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Introduction to Data Science G (11516.1)

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, ONLINE, BRUCE-ONL (203370) - View
  • Semester 2, 2020, ONLINE, BRUCE (203340) - View
  • Semester 2, 2020, ON-CAMPUS, BRUCE (196045) - View
  • Semester 1, 2020, ON-CAMPUS, BRUCE (192329) - View
  • Semester 2, 2019, ON-CAMPUS, BRUCE (192446) - 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

This unit provides a graduate level introduction to the rapidly developing field of data science, which deals with the dramatic growth in the scale and complexity of data that can be collected and analysed. Data and its analysis and modelling underpins all aspects of work and society in the 21st century. Understanding effective and ethical ways of using vast amounts of data (big data) is a significant challenge to science and to society. Applying and developing techniques for data analysis and decision making requires interdisciplinary research in many areas, including algorithmic reasoning, statistics, technology competence, data handling, big data analysis / modelling, pattern recognition / artificial intelligence, enterprise / cloud computing, data visualisation / communication, as well as privacy and security. This unit will introduce you to the core competencies of data science, starting from the philosophy and ethics of data collection and analysis, to data handling, to statistical modelling and machine learning, to scientific data visualisation and communication. Practical exercises, individual study and group work will consolidate your learning and provide the foundations for later study.

This unit will be co-taught with 11372 Introduction to Data Science.

Learning Outcomes

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

1. Demonstrate a solid understanding of the principles of data science;

2. Demonstrate practical knowledge in data preparation;

3. Use appropriate modelling and analyse techniques for data science problems;

4. Demonstrate competent skills in using data science technology; and

5. Communicate results effectively.

Assessment Items

Contact Hours

Two hours of lectures, one hour of tutorials and one hour of computer laboratories on campus per week.

Prerequisites

None.

Corequisites

None.

Assumed Knowledge

Working knowledge of discrete mathematics, algebra and numerical analysis.

Incompatible Units

11372 Introduction to Data Science

Equivalent Units

None.


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