Introduction to Data Science (11372.1)

Please note these are the 2027 details for this unit
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Introduction to Data Science
Available teaching periods Delivery mode Location
View teaching periods On-campus
 Bruce, Canberra
EFTSL Credit points Faculty
0.125 3 Faculty Of Science And Technology
Discipline Study level HECS Bands
School Of Information Technology & Systems Level 3 - Undergraduate Advanced Unit Band 2 2021 (Commenced After 1 Jan 2021)
Band 3 2021 (Commenced Before 1 Jan 2021)
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 will consolidate your learning and provide the foundations for later study.

This unit may be co-taught with 11516 Introduction to Data Science G.

Learning outcomes

After successful completion of this unit, students will be able to:
1. Develop an understanding of the principles of data science;
2. Demonstrate practical knowledge in basic data preparation;
3. Use appropriate modelling and analysis techniques for simple data science problems;
4. Demonstrate competent skills in using data science technology; and
5. Interpret data analysis results and communicate findings while considering ethical and societal implications.


Graduate attributes

1. UC graduates are professional - communicate effectively
1. UC graduates are professional - display initiative and drive, and use their organisation skills to plan and manage their workload
1. UC graduates are professional - employ up-to-date and relevant knowledge and skills
1. UC graduates are professional - take pride in their professional and personal integrity
1. UC graduates are professional - use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems
2. UC graduates are global citizens - adopt an informed and balanced approach across professional and international boundaries
2. UC graduates are global citizens - behave ethically and sustainably in their professional and personal lives
2. UC graduates are global citizens - communicate effectively in diverse cultural and social settings
2. UC graduates are global citizens - make creative use of technology in their learning and professional lives
2. UC graduates are global citizens - think globally about issues in their profession
2. UC graduates are global citizens - understand issues in their profession from the perspective of other cultures
3. UC graduates are lifelong learners - adapt to complexity, ambiguity and change by being flexible and keen to engage with new ideas
3. UC graduates are lifelong learners - be self-aware
3. UC graduates are lifelong learners - evaluate and adopt new technology
4. UC graduates are able to demonstrate Aboriginal and Torres Strait Islander ways of knowing, being and doing - use Indigenous histories and traditional ecological knowledge to develop and augment understanding of their discipline


Prerequisites

Students must have passed 24 credit points.

Corequisites

None.

Incompatible units

11516 Introduction to Data Science G

Equivalent units

None.

Assumed knowledge

Working knowledge of discrete mathematics, algebra and numerical analysis.
Availability for enrolment in 2027 is subject to change and may not be confirmed until closer to the teaching start date.
Year Location Teaching period Teaching start date Delivery mode Unit convener
2026 Bruce, Canberra Semester 1 16 February 2026 On-campus Dr Ibrahim Radwan
2026 Bruce, Canberra Semester 2 10 August 2026 On-campus Dr Ibrahim Radwan
2027 Bruce, Canberra Semester 1 15 February 2027 On-campus Dr Ibrahim Radwan
2027 Bruce, Canberra Semester 2 09 August 2027 On-campus Dr Ibrahim Radwan
The information provided should be used as a guide only. Timetables may not be finalised until week 2 of the teaching period and are subject to change. Search for the unit timetable.

Required texts

Students are not required to buy a textbook for this unit. However, it is strongly recommended that students use the following resources online and in the Library.

Recommended:

  • "R for Data Science" by Garrett Grolemund and Hadley Wickham (https://r4ds.had.co.nz/)
  • "Practical Data Science with R " by Nina Zumel and John Mount (available at the UC library)

Supplementary:

  • "Modern Data Science with R" by Benjamin S. Baumer and Daniel T. Kaplan
  • "Data Science from Scratch, 2nd Edition" by Joel Grus

Submission of assessment items

Extensions & Late submissions

Assignments are meant to be individual work, although discussing a problem with another student or tutor is considered one reasonable way of learning. However, the actual submitted assignment must be the student's work. Students are expected to familiarise themselves with the University's Student Charter https://www.canberra.edu.au/Policies/PolicyProcedure/Index/200. Experience has shown that students who do not do their work are unlikely to pass the exam (and therefore the unit).

Assignments will be submitted electronically through the Unit Website interface on UC LearnOnline.

Assignment submissions will be assessed to address the specific requirements of each assignment, as stated in the assignment descriptions. They will receive a numerical mark, which, together with the other assessment items (tests, exams), defines a student's final grade as outlined in this section.

Responsibility for understanding

If there is any doubt about the requirements of any particular assignment or assessment procedure, the onus for clarifying the issue rests with the student, who should contact the unit convenor or tutor. Further, it is the student's responsibility to ensure that they are correctly enrolled in the unit and that the lecturer/tutor and Student Administration have their correct contact details.

Artificial Intelligence (AI)

Guided - Students will be guided in how GenAI must/may be used in completing the assessments as detailed in the unit outline and assessment instructions. More detailed information can be found at GenAI and Assessment at UC.

Please refer to each assessment item description on the unit's teaching site to determine where the use of AI and GenAI is permitted (and how so) and where it is prohibited. Any use of AI and GenAI must be appropriately acknowledge and referenced and in accordance with what has been permitted in the assessment description. Please refer to the Referencing GenAI guide and full assessment details in the unit's Canvas teaching site.

Special assessment requirements

To obtain a particular grade in this unit, there must be no outstanding submissions by the end of week 15. The unit convener reserves the right to question students orally on any of their submitted work.

Passing the unit that students have completed the requirements for the Academic Integrity Module Test is compulsory.

All assessment items will receive a numerical mark. The final grade will be determined as a weighted average of the individual assessment items (as described in Section 5a).

To be awarded a particular grade in IDS, students must meet the assignments' overall requirements and requirements set out in the table below. All grades are conditional upon the following minimum requirements:

  • Fulfill the requirements for the AIM test.

Grade

Minimum Percentage

Pass

Minimum 50% of combined weighted marks of all assessment items

Credit

Minimum 65% of combined weighted marks of all assessment items

Distinction

Minimum 75% of combined weighted marks of all assessment items

High Distinction

Minimum 85% of combined weighted marks of all assessment items

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Please note that the unit convenor reserves the right to question students on any of their submitted work for moderation and academic integrity purposes.

Supplementary assessment

Students who miss the final assignment due to illness or other acceptable grounds (see link below) may be able to sit for a deferred examination. A doctor's certificate stating why the student could not sit for the exam or other required evidence should be given to Student Central (Bldg. 1) as soon as possible - generally within 3 days of the examination. See Assessment Policy https://www.canberra.edu.au/Policies/PolicyProcedure/Index/331 and Assessment Procedures https://www.canberra.edu.au/Policies/PolicyProcedure/Index/369 for more details. Students will only be allowed to sit for a deferred examination if there are no outstanding submissions or resubmissions for the assignments required to pass the subject as specified above.

Students must apply academic integrity in their learning and research activities at UC. This includes submitting authentic and original work for assessments and properly acknowledging any sources used.

Academic integrity involves the ethical, honest and responsible use, creation and sharing of information. It is critical to the quality of higher education. Our academic integrity values are honesty, trust, fairness, respect, responsibility and courage.

UC students have to complete the Academic Integrity Module annually to learn about academic integrity and to understand the consequences of academic integrity breaches (or academic misconduct).

UC uses various strategies and systems, including detection software, to identify potential breaches of academic integrity. Suspected breaches may be investigated, and action can be taken when misconduct is found to have occurred.

Information is provided in the Academic Integrity Policy, Academic Integrity Procedure, and University of Canberra (Student Conduct) Rules 2023. For further advice, visit Study Skills.

Learner engagement

Expected Average Student Workload:    * denotes an assessable item

Lectures

12x 2h

=24h

Tutorials / Computer laboratory classes / Unsupervised activities

11x 2h

=22h

Preparation (lectures, tutorials, computer labs, reading)

12x 2h

=24h

* Unit Readiness Quiz

  

=10h

* Assignment 1 (incl. preparation)

  

=20h

* Mid-term Quiz (incl. preparation)

  

=20h

* Final Assignment (incl. preparation)

  

=30h
Total   =150h

Participation requirements

Your participation in both class (lecture, tutorial/computer laboratory classes) and online activities will enhance your understanding of the unit content and, therefore, the quality of your assessment responses. Lack of participation may result in your inability to satisfactorily pass assessment items. Experience has shown that students who do not attend the classes and/or do not engage with the online content will have difficulty in passing the subject.

Required IT skills

Familiarity with using Windows or Mac computers

This unit involves online meetings in real time using the Virtual Room in your UCLearn teaching site. The Virtual Room allows you to communicate in real time with your lecturer and other students. To participate verbally, rather than just typing, you will need a microphone. For best audio quality we recommend a microphone and speaker headset. For more information and to test your computer, go to the Virtual Room in your UCLearn site and 'Join Course Room'. This will trigger a tutorial to help familiarise you with the functionality of the virtual room.

Work placement, internships or practicums

Not Applicable

Additional information

In all cases of absence, sickness or personal problems, it is the student's responsibility to ensure that the unit convenor is informed. The minimum participation requirement must be met in order to pass the unit (regardless of supporting documentation).

It is important that students refer to unit website (through UCLearn – UC's online learning environment) on a regular basis for any variations in the schedule and deadlines for the assessment tasks, which will be announced on the Unit Website. It is also the student's responsibility to ensure that they regularly check their UC email account, as electronic messages (whether via the unit's UCLearn site or directly) will be sent to this account.

The online discussion forum on the unit's UCLearn site is as very useful place for posting questions and students are strongly encouraged to make use of it.

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