| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
At the conclusion of this subject students should be able to:
1. Get the ability to select and design algorithms.
2. Be competent possessing expertise in building data structures and algorithms for numerical solutions, including searching and optimization.
3. Comprehend the application of computational methods in professional engineering, including in the design, analysis and validations of mechanical systems. |
| 1.The ability to apply the knowledge of math, science, and mechanical engineering. |
| 2.The ability to design and conduct experiments, as well as to analyze the data obtained. |
| 3.The ability to work with others as a team to design and manufacture products of mechanical engineering systems. |
| 4.The ability humanities awareness and a knowledge of contemporary issues, and to understand the impact of science and engineering technologies, environmental, societal, and global context. |
| 5.The ability of continuing study and self-learning. |
| 6.The knowledge of professional ethics and social responsibilities of a mechanical engineer. |
|
|
| Discussion |
| topic Discussion/Production |
| Lecturing |
|
| Internship |
| Study Outcome |
| Quiz |
| Written Presentation |
| Oral Presentation |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Introduction to Data Structures and Algorithms
Course overview, role of algorithms, project briefing |
| Week 2 |
Object-Oriented Programming
Classes, objects, inheritance, modular design |
| Week 3 |
Modules & Algorithm Analysis
Program structure, Big-O notation, efficiency trade-offs |
| Week 4 |
Recursion & Iteration
Recursive vs. loop-based problem solving |
| Week 5 |
Stacks, Queues & Data Management
Core operations, examples in scheduling and buffering |
| Week 6 |
Linked Lists
Lists, operations, comparison with arrays |
| Week 7 |
Trees
Binary trees, traversals, basic balancing |
| Week 8 |
Searching & Sorting
Search methods, sorting algorithms |
| Week 9 |
Graphical User Interface (GUI)
Basics of event-driven programming, simple UI building |
| Week 10 |
Exam
Covers Weeks 1–9: fundamentals, data structures, algorithms |
| Week 11 |
Project Development I
Begin implementation; supervised coding sessions |
| Week 12 |
Project Development II
Continue development; checkpoints |
| Week 13 |
Project Development III
Refinement and testing; peer evaluation |
| Week 14 |
Project Development IV
Short updates, feedback, debugging support |
| Week 15 |
Final Presentation I
Formal presentations and oral defence |
| Week 16 |
Final Presentation II
Remaining presentations, peer review, reflection
|
self-directed
learning |
03.Preparing presentations or reports related to industry and academia.
|
|
| Evaluation |
| Class Participation (30%); Exam (40%); Final Project (30%) |
| Textbook & other References |
| Goodrich, Tamassia, Goldwasser. Data Structures and Algorithms in Python. Wiley. |
| Teaching Aids & Teacher's Website |
| iLearning |
| Office Hours |
| After each lecture |
| Sustainable Development Goals, SDGs(Link URL) |
| include experience courses：N |
|