IE2108 Data Structures & Algorithms in Python

Course Provider

School of Electrical and Electronic Engineering (EEE)

Certification

Specialist Certificate

Academic Unit

3

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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 part of:
- Specialist Certificate in Foundations of Electrical and Electronic Engineering (EEE)
- Part-time Bachelor of Engineering (Electrical and Electronic Engineering)

 

Course Availability

  • Date(s): 11 Aug 2025 to 05 Dec 2025

    Time: Lecture (every Tuesday: 1900-2020, Tutorial (every Tuesday): 2030-2150

    Venue: NTU Main Campus

    Registration Closing Date: 08 Jul 2025

    Apply now

Objectives

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

Assessment
  • Quiz
  • Individual Readiness Assessment
  • Tutorial participation
  • Final Examination

Who should attend
  • Learners with a relevant CET part-time diploma issued from local polytechnics.
  • Learners who would like to pursue careers in machine learning and data analytics.

Fees and Funding

Standard Course Fee: S$3,204.60

SSG Funding Support

 Course fee

Course fee payable after SSG funding, if eligible under various schemes

 

BEFORE funding & GST

AFTER funding & 9% GST

Singapore Citizens (SCs) and Permanent Residents (PRs) (Up to 70% funding)

S$2,940

S$961.38

Enhanced Training Support for SMEs (ETSS)

S$373.38

SCs aged ≥ 40 years old
SkillsFuture Mid-career Enhanced Subsidy (MCES)
(Up to 90% funding)

• NTU/NIE alumni may utilise their $1,600 Alumni Course Credits. Click here for more information.

Read more about funding

Recommended Add-Ons

 

COURSE TITLEACADEMIC UNIT
EG2810 Mathematics A4
PH1012 Physics A4
EE2101 Circuit Analysis3

Listed courses are:

  • Credit-bearing and stackable to Specialist Certificate in Foundations of Electrical and Electronic Engineering (EEE) and Part-time Bachelor of Engineering (Electrical and Electronic Engineering).

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