• Offered by School of Computing
  • ANU College ANU College of Engineering Computing & Cybernetics
  • Classification Transitional
  • Course subject Computer Science
  • Academic career PGRD
  • Mode of delivery In Person

Note: Non-DADAN/MADAN students wanting to enrol in the non-standard session offerings are required to seek approval from their Program Convener.

This course teaches introductory programming within a problem-solving framework applicable to data science. There is an emphasis on designing and writing small programs to solve data science problems that include data processing, data manipulation and data visualisation tasks. Testing and debugging are seen as integral to programming for data science. The course will also teach how to effectively use popular data science libraries for data analysis and manipulation. It will provide skills for tackling the `messiness' of real-world computer systems, libraries and their different versions, and data with a particular focus on solving problems using knowledge available on the Web. The course will be taught using the Python programming language. It will also require students to work collaboratively on software programs using the Git version control system and DevOps tools.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Describe basic data types, operators, functions and the flow of execution in Python
  2. Articulate appropriate Web queries to retrieve existing solutions to programming problems
  3. Apply fundamental programming concepts, using the Python high-level general-purpose programming language, to solve data processing problems
  4. Critically implement fundamental data structures in Python for data cleaning, indexing, querying, sorting, aggregating and merging operations
  5. Appraise the fundamentals of some of the most widely used Python packages for data processing and related data processing problems
  6. Use a version control, task management and continuous integration system to enable group interactions and collaborative coding
  7. Develop data processing programs that read, transform, analyse and deploy/visualise data
  8. Generate project reports and package and document Python programs for demonstration purposes

Other Information

Students not enrolled in programs GCDE, DADAN, MADAN or CADAN, who wish to enrol must apply for a permission code by contacting CECC Student Services (studentadmin.cecc@anu.edu.au). Approval will only be given for exceptional cases of need, not cases of convenience, and only after approval from the student's program convenor that the need is justified.

Students will need to install the following software programs in order to successfully complete this course:

  1. Anaconda Python (https://www.continuum.io/downloads)
  2. PyCharm IDE Community Edition (https://www.jetbrains.com/pycharm/)
  3. Git Client (https://git-scm.com/downloads)

Indicative Assessment

  1. Programming assignments (50) [LO 1,2,3,4,5,6,7]
  2. Online software challenges (20) [LO 1,4]
  3. Group Software Assignment (30) [LO 4,5,6,7,8]

The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.


Approx 130 hours

Inherent Requirements

Not applicable

Requisite and Incompatibility

To enrol in this course you must be enrolled in: Graduate Certificate of Applied Data Analytics or Graduate Certificate of Data Engineering or Graduate Diploma of Applied Data Analytics or Master of Applied Data Analytics. You are not able to enrol in this course if you have successfully completed COMP1730 or COMP6730 or COMP1040.

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Tuition fees are for the academic year indicated at the top of the page.  

Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees

Student Contribution Band:
Unit value:
6 units

If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.

Where there is a unit range displayed for this course, not all unit options below may be available.

6.00 0.12500
Domestic fee paying students
Year Fee
2024 $4980
International fee paying students
Year Fee
2024 $6360
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

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There are no current offerings for this course.

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