2026/27 · Official catalogue edition
The effective term printed in this PDF is Semester A 2025/26。
This outline is still being checked. Before registering, confirm entry and assessment requirements against the official document linked below.
Offering record for 2026/27：Semester B 2026/27 · Catalogue source ↗
The PDF Effective Term is separate from the 2026/27 catalogue year and current offering observation.
What you will study
- Course overview
- This course aims to develop students’ ability to formulate, analyse and solve statistical or engineering problems using software tools, such as R. The goal of the course is to train students to become effective modellers who can build sound models to solve statistical or engineering problems in various functional areas.
Official PDF, pages 1–2 · Abstract
- Course content
- Keyword Syllabus Introduction to statistical modelling tools Definition of statistical modelling. Types of statistical models. Introduction of features and functions of the software tools. Examples on the above features and functions. Data Analysis using software tools Features of data in engineering contexts using statistics. Data analysis to support decision making. Extracting information from available data. Visualization and data processing using software tools. Real world problems Introduction of real world problems in different areas. Modelling of those problems. Applying the software tools to solve those problems. Analysis of modelling processes Plots examination.Sample size determination. Interpretation of results. Various topics in applied multivariate analysis such as multiple linear regression, logistic regression, parametric and non-parametric statistical models and longitudinal analysis.
Official PDF, pages 3–4 · Keyword Syllabus
- Intended learning outcomes
View official learning outcomes and conditions
Course Intended Learning Outcomes (CILOs) CILOs Weighting (if DEC-A1 DEC-A2 DEC-A3 app.) 1 Describe the processes for modelling, statistical x x or engineering problems, and analyzing data 2 Formulate the statistical models for the real x x problems 3 Implement the models using software tools x x x 4 Verify the results obtained from software tool x x x models, and communicate the analysis and findings in layman's terms A1: Attitude Develop an attitude of discovery/innovation/creativity, as demonstrated by students possessing a strong sense of curiosity, asking questions actively, challenging assumptions or engaging in inquiry together with teachers. A2: Ability Develop the ability/skill needed to discover/innovate/create, as demonstrated by students possessing critical thinking skills to assess ideas, acquiring research skills, synthesizing knowledge across disciplines or applying academic knowledge to real-life problems. A3: Accomplishments Demonstrate accomplishment of discovery/innovation/creativity through producing /constructing creative works/new artefacts, effective solutions to real-life problems or new processes.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
- EE3001 Foundations of Data Engineering
Official PDF, page 1 · Prerequisites
- Precursors
- Nil
Official PDF, page 1 · Precursors
- Corequisites
- The reviewed official PDF and catalogue page do not state this item.
- Additional course requirements
- Complete official information for this field is not available.
- Equivalent courses and conditions
- EE2203 Modelling Techniques
Official PDF, page 1 · Equivalent Courses
- Exclusive courses and conditions
- Nil
Official PDF, page 1 · Exclusive Courses
Assessment and passing the course
- Assessment
- Continuous assessment: 70%; Examination: 30%; Examination duration: 2 hours
Assessment breakdown
Assessment task Weight Generative AI Remarks and restrictions Tests (min.: 2)CILO 1, 2, 3, 4 35% No Not stated in source row Assignments (min.: 3)CILO 1, 2, 3, 4 35% Yes Not stated in source row 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) 1, 2, 3, 4 35 - No 2 Assignments (min.: 1, 2, 3, 4 35 - Yes 3) Continuous Assessment (%) 70 Examination (%) 30 Examination Duration (Hours) 2 Additional Information for ATs Remark: To pass the course, students are required to achieve at least 30% in course work and 30% in the examination. # may include homework, tutorial exercise, project/mini-project, presentation - Passing requirements
- At least 30% in coursework and 30% in the examination.
Official PDF, page 3 · Additional Information for ATs
Credits, duration and edition details
- Title in this edition
- Modelling Techniques
Official PDF, page 1 · Course Title
- Credits in this edition
- 3
Official PDF, page 1 · Credit Units
- Course duration
- One Semester
Official PDF, page 1 · Course Duration
- 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. EE3211 · Open the original university PDF ↗
Source · University course information ↗ · Checked 2026-10-19T07:45:20Z