This course aims to give a systematic introduction to data structures and algorithms for constructing efficient computer programs based on Python. Principles of algorithmic analysis will be studied. Emphasis is on data structures and efficient algorithms in the program development process, covering abstract data types, trees, graphs, sorting, and searching. The greedy technique such as Kruskal's algorithm and Dijkstra's algorithm will be introduced. AI algorithms such as classification will also be discussed. Theories will be practiced during tutorial sessions and students will gain substantial Python programming experience.
Academic Units | 3 |
Exam Schedule | Tue Nov 25 2025 00:00:00 GMT+0000 (Coordinated Universal Time) 13:00-15:00 |
Grade Type | Letter Graded |
Department Maintaining | EEE |
Mutually Exclusive | |
Not Available to Programme | REP(ASEN), REP(BIE), REP(CBE), REP(CE), REP(CSC), REP(CVEN), REP(ENE), REP(MAT), REP(ME) |
Index | Type | Group | Day | Time | Venue | Remark |
---|---|---|---|---|---|---|
- | LEC/STUDIO | EELE | MON | 0930-1050 | LT24 | Teaching Wk1-9,11-13 |
LEC/STUDIO | EELE | MON | 0930-1050 | ONLINE | Teaching Wk10 |
0930
1030
1130
1230
1330
1430
1530
1630
1730
IE2108
LEC/STUDIO | LT24
Teaching Wk1-9,11-13
IE2108
32261
TUT | TR+78
Teaching Wk1-9,11-13
IE2108
LEC/STUDIO | ONLINE
Teaching Wk10
IE2108
32261
TUT | ONLINE
Teaching Wk10
IE2108
32262
TUT | TR+69
Teaching Wk1-9,11-13
IE2108
32263
TUT | TR+65
Teaching Wk1-9,11-13
IE2108
32262
TUT | ONLINE
Teaching Wk10
IE2108
32263
TUT | ONLINE
Teaching Wk10
IE2108
32264
TUT | TR+66
Teaching Wk1-9,11-13
IE2108
32264
TUT | ONLINE
Teaching Wk10
IE2108
32265
TUT | TR+64
Teaching Wk1-9,11-13
IE2108
32265
TUT | ONLINE
Teaching Wk10
IE2108
32266
TUT | TR+64
Teaching Wk1-9,11-13
IE2108
32266
TUT | ONLINE
Teaching Wk10
IE2108
32267
TUT | TR+61
Teaching Wk1-9,11-13
IE2108
32268
TUT | TR+61
Teaching Wk1-9,11-13
IE2108
32267
TUT | ONLINE
Teaching Wk10
IE2108
32268
TUT | ONLINE
Teaching Wk10
We would encourage you to review with the following template.
AY Taken: ...
Assessment (Optional): ...
Topics (Optional): ...
Lecturer (Optional): ...
TA (Optional): ...
Review: ...
Final Grade (Optional): ...