FIT5211 is a Level 5 version of FIT9059 and will be offered in the Master of Data Science as an elective unit.

26/09/2016 - Admin: updated prerequisites to reflect new programming unit as per email confirmation from CE and added prohibition.

3 April 2017: Adding reasons for Change on behalf of Chief Examiner. Examination hours updated to meet new University Examination Procedures. Effective S2 2017.

May 2017: Removed "or equivalent introductory programming unit" from prerequisite statement as students specifically need python programming and the statement suggests any introductory programming is satisfactory. Discussed with CE Bernd Meyer.

No change in any requirement. Just made the requirement for a gap between lectures explicit since otherwise central timetabling will allocate x1 hours back to back, effectively turning it into a 1x2 hour lecture.

29/06/2018 - Admin: adding FIT9131 as a prerequisite in Callista (only) to facilitate students with Java background access to allocate to the Java to Python transition workshop attached to FIT5211 in Allocate+ for Semester 2, 2018.

10/12/2018 - Admin: remove FIT9131 as this was only a temporary measure.

24/9/2019: Admin - adding 10 minutes reading time to the overall exam duration as per University requirements.

28/09/2020 - Admin: Update to include new assessment and teaching approach fields as per Handbook requirements.

19/11/2020: Admin - this unit was disestablished at FEC 5/20 on 12/11/2020 as it is no longer required.

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

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 (2 hours and 10 minutes): 50%; In-semester assessment: 50%

Minimum total expected workload equals 12 hours per week comprising:

1. Contact hours for on-campus students:
   • Two times 1 hour lectures
   • One hour tutorial
   • Two hour laboratory
2. 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)

The two 1 hour lectures need to be on different days so that students have time to work in between.

FIT

FIT9133