FIT9059 is a choice of foundation unit for the Master of Data Science (MDataSci) (non-cognate entry) and a core unit in the for those non-cognate entries doing the Data Analytics stream, beginning in 2016.

17/08/2016: Admin adding reason for change - CE introduced one hour of tutorial in addition to the existing pracs. Currently we are running two hours of pracs per week. The idea is that students _mostly_ work on concrete coding problems, i.e. working on putting the theory that they have learned in the lectures and out of class into practice. However, for many topics they also need more support for the theory (e.g. working through examples of complexity calculations, reasoning about particulars of divide and conquer implementations, designing a DP solution, etc.) This is much better done in a tutorial style, and there isn't sufficient time in 2 hours of lectures to do this. The net effect is that the so-called pracs are currently a mix of tutorials and pracs. The more theoretical aspects need to split out into dedicated tutorial classes so that prac classes can be dedicated to actual coding exercises. In the tutorials demonstrators will work interactively with their group through detailed example collections. (Change has been discussed with and is supported by David Albrecht and Wray Buntine)

09/02/2016: Admin adding reason for change - specified Mac Labs in Special Timetabling Requirements under Workloads.

28/09/2015 - minor wording change to learning outcomes as per email from GPC Chair Judy Sheard.

24/9/2015: Changes made by Ann Nicholson to align the structure (exam/non-exam assessment breakdown, and workload/classes) with standards for Masters units. Also added explicit mention of the Python programming language.

28/4/2021 - Unit disestablished at FEC 2/21 Item 6.2

FIT9059 is a choice of foundation unit for the Master of Data Science (MDataSci) (non-cognate entry) and a core unit in the for those non-cognate entries doing the Data Analytics stream, beginning in 2016.

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

1. design and implement simple algorithms in Python;
2. implement basic data types such as stacks, queues, lists, trees, priority queues, heaps and hash tables;
3. compare and evaluate different algorithms and implementations of basic data types;
4. calculate best case and worst case big-O algorithm complexity.

020109 Algorithms

This unit introduces students to problem solving concepts and techniques fundamental to the science of programming. It covers data types; data structures; algorithms; algorithmic complexity; and recursion. Detailed topics include analysis of best and worst-case time complexity; data structures such as lists, stacks, queues, binary search trees, graphs and heaps; hashing; sorting algorithms; searching algorithms; and graph algorithms. Students will implement these algorithms and data structures in the Python programming language.

On-campus

Examination (3 hours): 50%; In-semester assessment: 50%

Minimum total expected workload equals 12 hours per week comprising:

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

• Two hour lectures
• One hour tutorial
• Two hour laboratory

(b.) Additional requirements (all students):

• A minimum of 7 hours of personal study per week in order to satisfy the reading, lab and assignment expectations.

2x1 hour lectures requires Mac labs for 1x2 hr practical lab tutorial only require flat room (but with projector)

FIT

FIT9131 or any equivalent introductory programming unit