Data Capture and Preparations G (11520.1)

Please note these are the 2026 details for this unit
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Data Capture and Preparations G
Available teaching periods Delivery mode Location
View teaching periods Online self-paced
On-campus
 Bruce, Canberra
EFTSL Credit points Faculty
0.125 3 Faculty Of Science And Technology
Discipline Study level HECS Bands
Academic Program Area - Technology Graduate Level Band 2 2021 (Commenced After 1 Jan 2021)
Band 3 2021 (Commenced Before 1 Jan 2021)
A core skill of a data scientist is to capture, extract and clean data. Real world data often come from various data sources, in various formats and are unorganised. This unit introduces students to the concepts and techniques a data scientist employs in the early stages of data analysis process. This unit will provide hands-on experience in capturing data from sensors, collecting data from public information as well as working with existing data sets using real-world examples. Such data may be temporal or spatial, ordinal or categorical, embedded in documents or files. Students will learn how to import and clean the data, which usually involves multiple, often complicated, steps to convert data from its raw format to a clean format that greatly facilitates the later stages of the data analysis. This is known as data wrangling.

Learning outcomes

After successful completion of this unit, students will be able to:
1. Capture and analyse real-world data utilising sensors;
2. Apply appropriate techniques for capturing data from existing sources;
3. Import data from different sources into R;
4. Convert data from one format to another one in R;
5. Employ suitable techniques for tidying data; and
6. Develop a sound understanding of text mining methods in R.


Graduate attributes

1. UC graduates are professional - employ up-to-date and relevant knowledge and skills
1. UC graduates are professional - use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems
1. UC graduates are professional - communicate effectively
2. UC graduates are global citizens - think globally about issues in their profession
2. UC graduates are global citizens - adopt an informed and balanced approach across professional and international boundaries
2. UC graduates are global citizens - understand issues in their profession from the perspective of other cultures
2. UC graduates are global citizens - make creative use of technology in their learning and professional lives
2. UC graduates are global citizens - behave ethically and sustainably in their professional and personal lives
3. UC graduates are lifelong learners - reflect on their own practice, updating and adapting their knowledge and skills for continual professional and academic development
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 - evaluate and adopt new technology


Prerequisites

None.

Corequisites

11516 Introduction to Data Science G

Incompatible units

None.

Equivalent units

None.

Assumed knowledge

Working knowledge of discrete mathematics, algebra and numerical analysis.
Availability for enrolment in 2026 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
2025 Bruce, Canberra Semester 1 03 February 2025 Online self-paced Dr Raul Fernandez Rojas
2025 Bruce, Canberra Semester 1 03 February 2025 On-campus Dr Raul Fernandez Rojas
2025 Bruce, Canberra Semester 2 28 July 2025 Online self-paced Dr Raul Fernandez Rojas
2026 Bruce, Canberra Semester 1 16 February 2026 On-campus Dr Raul Fernandez Rojas
2026 Bruce, Canberra Semester 1 16 February 2026 Online self-paced Dr Raul Fernandez Rojas
2026 Bruce, Canberra Semester 2 10 August 2026 Online self-paced Dr Raul Fernandez Rojas
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

There is no required textbook for this unit. However, the following are recommended readings that a re a companion for the concepts taught in this unit.

Recommended:

"R in Action – Data analysis and Graphics with R"  by Robert Kabacoff
"R for Data Science" by Garrett Grolemund and Hadley Wickham
"Practical Data Science with R" Second Edition by Nina Zumel and John Mount

A limited number of copies of these books are also available from the UC Library.

Submission of assessment items

Extensions & Late submissions

Assignments are meant to be individual work, although talking a problem over with another student or tutor is considered one reasonable way of learning. However, the actual submitted assignment must be the student's own 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 own work are unlikely to pass the exam (and therefore the unit).

Assignments will be submitted electronically through the Unit Website interface on UC LearnOnline. For the two assignments, students need to submit their files as a ZIP file via the unit's UC LearnOnline site.

Assignment submissions will be assessed for addressing the specific requirements of each assignment, as stated in the assignment descriptions. Assignment submissions will receive a numerical mark, which together in their entirety with the other assessment items (tests, exam) define a student's final grade as outlined in this section.

Extensions: Extensions must be applied for before the due date. 

Students can apply for an extension to the due date for submission of an assessment item on the grounds of illness or other unavoidable and verifiable personal circumstances as described under "Acceptable Grounds for Approval" at http://www.canberra.edu.au/student-services/examinations/alternative-exams. Documentary evidence will be expected in order that an extension be granted. Applications need to use the above mentioned Assignment Extension form.

It should be noted that such documentation will be considered but will not guarantee that the application will be successful. The Unit Convenor will decide whether to grant an extension and the length of the extension.

Responsibility for understanding

If there is any doubt with regard to the requirements of any particular assignments 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 responsibility of students to ensure that they are correctly enrolled in the unit and that the tutor and Student Administration have their correct contact details.

Special assessment requirements

All assessment items will receive a numerical mark. The final grade will be determined as a weighted average of the individual assessment items.

To be awarded a particular grade in DCP G, students must meet the following requirements as set out below. All grades are conditional upon the following minimum requirements:

  • Minimum 50% of available marks in the total mark (Assingments + Quizzes + Early Assessment)

Grade

Assignments + Quizzes + Early Assessment

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

The unit convener reserves the right to question students orally on any of their submitted work for moderation and academic integrity purposes.

 

Supplementary assessment

Students who miss any assignment due to illness or other acceptable grounds (see link below) should submit an Assignment Extension Form accompanied with a doctor's certificate stating why the student was not able to complete the assessment on time or other required evidence should be given to Student Central (Bldg. 1) as soon as possible - generally within 3 days of the deadline. 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 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  12x 2h = 24h
Preparation (lectures, computer labs, reading, unsupervised activities) 12x 3h = 36h
* Assignment 1 – Data capture using sensors . = 23h
* Assignment 2 – Web scraping . = 23h
* Early Assessment . =   5h
* Online Quizzes (incl. preparation) . = 15h

Total                                                                                                   150 hours

Participation requirements

Research has established that there is a strong correlation between engagement and success in higher education. With this in mind, we encourage you to actively participate in all online module activities to enhance your learning opportunities. Unless specifically stated in the unit outline, there is no mandatory attendance requirement. However, you may elect to attend these, as they allow you to ask 'real time' questions to develop your understanding for the relevant assessment tasks.

Required IT skills

Fundamental R programming concepts, usage of Windows computers.

In-unit costs

2x USB thumb drives and consumables

Work placement, internships or practicums

Not applicable to this unit

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. 

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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