2026/27 · Official catalogue edition
The effective term printed in this PDF is Semester A 2025/26。
Offering details for 2026/27 have not been checked. Confirm available sections in AIMS before registering.
What you will study
- Course overview and aims
- This course is designed to introduce the students to concepts, impacts, and basic principles of data and information systems. Applied examples will be introduced, analysed, solved. The laboratories of this course will cover introductory probability problems and statistics of real-life data, geometric transformation, and applications of eigenvalues and eigenvectors to data analysis.
- Keyword syllabus
- Introduction to Data and Information Systems Numerical and non-numerical data; Signals and images; Graphs: nodes, edges, adjacency matrix. Basic Computational Engineering and Linear Algebra Introduction to computational engineering and modeling of physical systems; Vectors: inner and outer products; Matrices: matrix multiplications, determinants, inverse of a matrix; System of linear equations; Eigenvector and Eigenvalues; Application to geometric image transformations. Information System and Data Statistics and Probability Introduction to modeling of information and engineering systems using basic statistics and probability; Summarizing data sets: measures of center and measures of variation; Sample space and events, basic laws of probability, conditional probability; Application to engineering problems. Engineering Data Modeling and Random Variables Introduction to Information and engineering systems data modeling; The ideas of summation and integration; probability mass function, probability density function, common random variables (e.g., binomial, Poisson, Gaussian), expectation and variance; Joint distribution functions, independent random variables, covariance, and correlation; Application to data modeling. Basic Engineering Statistical Methods Sampling distributions and basic point estimates, confidence intervals for a population mean; Hypothesis testing: the null and the alternative hypotheses, two types of errors, p values; Application to engineering data analysis.
Official PDF, page 4 · Keyword Syllabus
- Intended learning outcomes
View learning outcomes
CILO Intended learning outcome Weight DEC 1 Explain mathematical concepts on topics of linear algebra, including vectors, matrices, linear equations, etc in engineering applications — DEC-A1, DEC-A2 2 Describe basic statistical methods in engineering applications — DEC-A1, DEC-A2 3 Describe engineering data modeling and random variables — DEC-A1, DEC-A2 4 Analyze data using basic statistics and probability theories — DEC-A1, DEC-A2 Official PDF, page 2 · Course Intended Learning Outcomes (CILOs)
Before you take this course
Check prerequisites, precursors and corequisites separately, together with equivalence and exclusion conditions. “and” means conditions must be met together; “or” marks alternatives. Read combined conditions in the groupings shown in the original text below.
- Prerequisites
- Nil
- Precursors
- Nil
- Corequisites
- This edition of the official syllabus does not state this item.
- Additional course requirements
- This edition of the official syllabus does not state this item.
- Equivalent courses and conditions
- Nil
- Exclusive courses and conditions
- Nil
Assessment and passing the course
- Assessment
- Continuous assessment: 50%; Examination: 50%; Examination duration: 2 hours
Assessment breakdown
Assessment task Weight Generative AI Remarks and restrictions Tests and short testsCILO 1, 2, 3, 4 30% No - Assignments and Lab AssignmentsCILO 1, 2, 3, 4 20% Yes - Official PDF, pages 2–3 · Assessment Tasks / Activities (ATs)
View the original assessment table and instructions
Assessment Tasks / Activities (ATs) ATs CILO No. Weighting (%) Remarks ("-" for Allow Use of nil entry) GenAI? 1 Tests (min. 2) and 1, 2, 3, 4 30 - No short tests 3 EE1004: Foundations of Information Systems and Data Analysis 2 # Assignments and 1, 2, 3, 4 20 - Yes Lab Assignments (total: 5) - Passing requirements
- Remark: To pass the course, students are required to achieve at least 30% in coursework and 30% in the examination. #may include homework, tutorial exercise, project/mini-project, presentation
Credits, duration and edition details
- Title in this edition
- Foundations of Information Systems and Data Analysis
- Credits in this edition
- 3
- Course duration
- One Semester
- Syllabus effective term
- Semester A 2025/26
- Levels printed in the syllabus
- B1, B2, B3, B4 - Bachelor's Degree
Check the official document
Read or download this edition through the university link below. EE1004 · Open the original university PDF ↗
Source · University course information ↗ · Checked 2026-10-18