This unit has been introduced as part of redeveloping the Bachelor of Computer Science Advanced (Honours) degree in 2018 and replaces FIT1045.

9 July 2019: Update two prerequisites to allow advanced Science students to enrol in FIT1053/FIT1054 as part of their Computational Science sequence. Effective 2020 semester 1. Discussed with CE and course director.

9 Dec 2019: Adding reason for change on behalf of CE. Consolidating the learning outcomes to better reflect the unit outcomes after partial re-design, and to increase usefulness for students who previously tended to be overwhelmed by the large number of learning outcomes. This is the result of a consultation process between different academics involved with the programming courses, and responds directly to a revision of the progression of these units commissioned by ADLT.

17/09/2020: Admin: Update to include new assessment and teaching approach fields as per Handbook requirements.

This is a core unit in the Bachelor of Computer Science Advanced (Honours) degree and replaces FIT1045.

Its purpose is to teach algorithmic problem solving, the design of simple algorithms, understanding of basic data structures, and give an introduction to their implementation in a programming language.

At the completion of this unit students should be able to:

1. translate between problem descriptions and program designs with appropriate input/output representations
2. choose and implement appropriate problem solving strategies
3. analyse the behaviour of programs and data structures
4. decompose problems into simpler problems and reduce unknown to known problems
5. determine the computational cost and limitations of algorithms
6. demonstrate and test the correctness of algorithms

020109

This unit introduces programming fundamentals and the Python language to students. The unit provides a foundational understanding of program design and implementation of algorithms to solve simple problems. Fundamental programming control structures, built in and complex data types and mechanisms for modularity will be presented in Python.

Topics covered will include basic input and output, program control structures, basic data structures and modular program structure. Problem-solving strategies and techniques for algorithm development, iteration and recursion, algorithm efficiency and the limitations of algorithms will be introduced.

Recommended resources

Technological requirements

On-campus

* Active learning

Examination (2 hours): 40%; In-semester assessment: 60%

1. Workshops: - 19% - ULO: 1, 2, 3, 4
2. Tutorial Preparation: - 8% - ULO: 1, 2, 3, 4, 5
3. In-tutorial Tests: - 11% - ULO: 1, 2, 3, 4, 5
4. Assignment: - 22% - ULO: 1, 2, 3, 4, 6
5. Exam: - 40% - ULO: 1, 2, 3, 4, 5, 6

Minimum total expected workload equals 12 hours per week comprising:

(a.) Contact hours for on-campus students:

• Two hours lectures
• Two hours laboratories
• Two hours tutorials

(b.) Additional requirements (all students):

• A minimum of 2-3 hours of personal study per one hour of lecture time in order to satisfy the reading, tute, prac and assignment expectations.

Lectures then tutorials then laboratories in any given week.

Tutorials to be located in spaces that encourage group work, that is, not in computer laboratories when possible.

Students must be enrolled in the Bachelor of Computer Science Advanced (Honours) (C3001) or have achieved an ATAR score of 95 or above and have completed VCE Mathematics Methods or Specialist Mathematics units 3 & 4 with a study score of 25 or MTH1010.