• Class Number 4468
  • Term Code 3330
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Dr Brian Parker
    • Dr Dan Andrews
  • Class Dates
  • Class Start Date 20/02/2023
  • Class End Date 26/05/2023
  • Census Date 31/03/2023
  • Last Date to Enrol 27/02/2023
SELT Survey Results

This course teaches introductory programming, fundamental programming language and computer science concepts, and computational problem solving illustrated with applications common in science and engineering, such as simulation and data analysis, visualisation and machine learning models. The course does not require any prior knowledge of programming, computer science or IT. There is an emphasis on designing and writing correct programs: testing and debugging are seen as integral to the programming enterprise.

Learning Outcomes

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

  1. Design and write programming code to solve practical problems of a scientific or engineering nature.
  2. Ability to read, test and debug small computer programs.
  3. Advanced ability to use key python libraries for data processing and visualisation.
  4. Advanced understanding of widely-used algorithms and data structures, and their computational complexity.
  5. Advanced understanding of design approaches used in scientific pipelines, including data abstraction and array-based and object-oriented programming.
  6. Advanced understanding of algorithm design paradigms, such as dynamic programming, and their scientific applications.
  7. Understand and apply principles of high code quality.
  8. Communicate effectively to both specialist and non-specialist audiences about data processing problems in writing and verbally.

Research-Led Teaching

Examples in class will be based on the lecturers' current research in computational biology and other scientific programming

Required Resources

Students are assumed to have achieved a level of knowledge of mathematics comparable to at least ACT Mathematics Methods or NSW Mathematics or equivalent. No programming, Computer Science or IT experience or skills are required.

There are no prescribed texts. We recommend:

"Think Python: How to think like a computer scientist" (2nd Edition) by Allan Downey.

 Available from http://greenteapress.com/wp/think-python-2e/, or in paperback (O'Reilly, 2015; ISBN-13: 978-1491939369; ISBN-10: 1491939362).

Whether you are on campus or studying remotely, there are a variety of online platforms you will use to participate in your study program. These could include videos for lectures and other instruction, two-way video conferencing for interactive learning, email and other messaging tools for communication, interactive web apps for formative and collaborative activities, print and/or photo/scan for handwritten work and drawings, and home-based assessment.

ANU outlines recommended student system requirements to ensure you are able to participate fully in your learning. Other information is also available about the various Learning Platforms you may use.

Staff Feedback

Students will be given feedback in the following forms in this course:

  • written comments
  • verbal comments
  • feedback to whole class, groups, individuals, focus group etc

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

Class Schedule

Week/Session Summary of Activities Assessment
1 Introduction; functional abstraction
2 Data types; functions
3 Control flow: branching and iteration
4 Sequence data type part 1
5 Code quality, debugging, testing; strings and more sequences
6 Data science 1; Mutable sequence data types
7 Functions part 2; Files and IO
8 Dictionaries and sets; classes
9 Computational Complexity
10 Errors and exception; Data science 2: numpy
11 Data science 2: pandas; dynamic programming
12 Exam revision

Tutorial Registration

On Wattle

Assessment Summary

Assessment task Value Learning Outcomes
Homework 1-5 (15%) 15 % Homework 1-5
Project assignment (35%) 35 % Project assignment
Final exam (50%) 50 % Final exam

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details


ANU has educational policies, procedures and guidelines , which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Integrity Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

The ANU is using 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. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.


Required for labs with homework assessment.


One final exam.

Assessment Task 1

Value: 15 %
Learning Outcomes: Homework 1-5

Homework 1-5 (15%)

Students need to submit homework solution AND attend the following lab to explain their homework with a tutor in order to get marks. If you don't attend the lab for this discussion (without an approved deferral), you will get zero marks even if you submitted the homework. If you didn't submit a homework and still attend the following lab to present some solutions for assessment by a tutor, you may still get partial marks up to a maximum of half of that homework mark.

HW1- 2%, HW2- 2%, HW3- 3%, HW4- 4%, HW5- 5%.

Assessment Task 2

Value: 35 %
Learning Outcomes: Project assignment

Project assignment (35%)

The assignment has two components: an implementation (python code) (95%) with a written report (5%). The code component and written report must be done individually by each student. Collaboration, or any sharing of solutions or parts of solutions, between students is not allowed.

Assessment Task 3

Value: 50 %
Learning Outcomes: Final exam

Final exam (50%)

The examination duration is 3 hours and 10 minutes. This consists of 3 hours writing time, and an extra 10 minutes to handle downloading files, uploading files, and checking that you have submitted what you intended to submit. comp6730 will have additional more advanced theory and programming questions compared with comp1730, based on advanced topics covered in the lectures in the last 4 weeks.

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.

The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.

The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.


The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

Online Submission

You will be required to electronically submit all your homework, project assignment and final exam on Wattle.

Hardcopy Submission


Late Submission

Individual assessment tasks may or may not allow for late submission. Policy regarding late submission is detailed below:

  • Late submission not permitted. If submission of assessment tasks without an extension after the due date is not permitted, a mark of 0 will be awarded.

Referencing Requirements

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

Dr Brian Parker

Research Interests

Computational Biology, Machine Learning, Scientific Programming

Dr Brian Parker

By Appointment
Dr Dan Andrews

Research Interests

Dr Dan Andrews

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions