• Offered by School of Computing
  • ANU College ANU College of Engineering and Computer Science
  • Classification Transitional
  • Course subject Computer Science
  • Academic career PGRD
  • Course convener
    • Dr Sergio Rodriguez Mendez
  • Mode of delivery In Person
  • Offered in Winter Session 2022
    See Future Offerings

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

Note: Non-CADAN/DADAN/MADAN or GCDE students wanting to enrol are required to seek approval from their Program Convener.

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

Information on inherent requirements for this course is currently not available.

Requisite and Incompatibility

You are not able to enrol in this course if you have successfully completed COMP1730/COMP6730 or COMP1040.

Prescribed Texts



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
2022 $4740
International fee paying students
Year Fee
2022 $6000
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.

The list of offerings for future years is indicative only.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.

Winter Session

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
Intensive Course
4225 08 Aug 2022 08 Aug 2022 19 Aug 2022 07 Oct 2022 In Person View

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