Introduction
This course introduces learners to data structures and algorithms for constructing efficient computer programs based on Python. This course will cover the principles of algorithmic analysis and Artificial Intelligence (AI) algorithms. Learners will develop analytical skills to assess application requirements and design efficient algorithms for specific tasks. Through exploring essential concepts of data abstraction and algorithmic thinking, learners will gain the skills needed to approach common challenges in programming and software development. Key concepts such as growth rates, nodes, trees, graphs, sorting and searching play a vital role in the study of data mining and artificial intelligence. The Greedy Technique will be introduced and AI algorithms such as classification will also be discussed This course is ideal for students pursuing a degree in a related field who require the understanding of data structures and algorithms as part of their coursework, as well as for beginner programmers and professionals in technical roles seeking to acquire basic skills in data structures and algorithms using Python.This course is credit-bearing (3AU) and stackable to
- Specialist Certificate in Foundations of Electrical and Electronic Engineering.
- Part-time Bachelor of Engineering in Electrical and Electronic Engineering course. (The part-time Bachelor of Engineering in Electrical and Electronic Engineering (EEE) will be discontinued from Academic Year 2026-2027 (i.e., August 2026). As a result, the progression pathway from the Specialist Certificate to the part-time Bachelor of Engineering in EEE will no longer be available.)
At the end of the course, learners will be able to:
1. Analyse the complexity of algorithms to assess the efficiency of different approaches to problem-solving.
2. Analyse application requirements and select the appropriate data structure for the task.
3. Design and implement efficient algorithms in Python for given applications.
4. Solve problems systematically and effectively through careful consideration of the data structures, data abstraction, and algorithmic paradigms that best fit the requirements.
5. Be equipped with the essential knowledge to study advanced courses in data mining and artificial intelligence.
1. Analyse the complexity of algorithms to assess the efficiency of different approaches to problem-solving.
2. Analyse application requirements and select the appropriate data structure for the task.
3. Design and implement efficient algorithms in Python for given applications.
4. Solve problems systematically and effectively through careful consideration of the data structures, data abstraction, and algorithmic paradigms that best fit the requirements.
5. Be equipped with the essential knowledge to study advanced courses in data mining and artificial intelligence.
The topics covered are valuable for careers in machine learning and data analytics.
Standard Course Fee: S$3204.6
| BEFORE funding & GST | AFTER SSG funding (if eligible under various schemes) & 9% GST | |
| SSG Funding Support | Course Fee | Course Fee Payable |
| Singapore Citizen (SC) and Permanent Resident (PR) (Up to 70% funding) | $2,940.00 | $961.38 |
| Enhanced Training Support for SMEs (ETSS) | $2,940.00 | $373.38 |
| Singapore Citizen aged ≥ 40 years old SkillsFuture Mid-career Enhanced Subsidy (MCES) (Up to 90% funding) | $2,940.00 | $373.38 |
- NTU/NIE alumni may utilise their $1,600 Alumni Course Credits for each course. Click here for more information.
- Learners can utilise their SkillsFuture Credits for these courses.
- Singaporeans aged 40 years and above are able to use their SkillsFuture Credit (Mid-Career) top-up of $4,000 to offset the course fees after SSG funding.
