DACT 2026–2027 syllabus

Data Analysis and Computational Thinking

Course 202600297 | 3 EC | Quartile 1

DACT is a beginner course for first-year Industrial Engineering and Management students. No prior programming experience is required.

What this course prepares you to do

Industrial engineers often receive problems with incomplete goals, imperfect data, and competing constraints. In DACT, you learn to turn such a problem into a computational solution that another person can inspect, test, and improve.

The guiding question is: How can you turn an unclear operational problem into a computational result that another person can inspect, test, and use responsibly?

You begin by defining problems and designing algorithms. You then use Excel and Python to analyse data, implement those algorithms, test their behaviour, and communicate what the results can and cannot support.

Where authoritative information lives

  • This website contains stable course explanations and the released learning materials.
  • Canvas contains announcements, weekly releases, task and bonus rules, submissions, feedback, and confirmed changes.
  • TimeEdit contains precise scheduled dates, times, and rooms.
  • OSIRIS contains the official study-unit record and registration data.
  • The 2026–2027 BMS regulations contain the applicable programme and examination rules.

Course facts

Course
202600297, Data Analysis and Computational Thinking
Programme
BSc Industrial Engineering and Management
Term
Quartile 1, academic year 2026–2027
Credits
3 EC, corresponding to 84 hours of study
Prerequisites
None. The course begins with computational problem formulation.
Instructor
Breno Alves Beirigo
Delivery
Monday and Friday are 105-minute in-person sessions. Tuesday is a self-study day, and a room is booked for 105 minutes so that you can study with classmates. You may complete the remaining independent study on any suitable day and in any suitable location.

Planned dates

These dates summarize the intended course sequence. Check Canvas and TimeEdit before relying on a date, time, or room.

Course introduction
31 August 2026
Live orientation and first problem launch

Examination review
20 October 2026
Integration and focused practice

Written examination
21 October 2026
Check Canvas and TimeEdit for final arrangements

Resit
4 November 2026
Uses the same intended content blueprint

Canvas publishes the final examination instructions. TimeEdit publishes the precise times and rooms.

How the course works

Table 1 shows when each weekly activity takes place and which sessions require attendance in person.

Table 1: Weekly course schedule
Day What you do Scheduled study
Monday Join the module launch, make an initial attempt, and study the learning route 105 minutes in person
Tuesday Study independently; use the booked room if you want to work with classmates 105-minute room booking
Friday Complete and hand in the paper exercise, discuss the closed Canvas quiz, and ask the TAs for help 105 minutes in person

Complete the readings, Canvas quiz, and programming exercises during independent-study time. The quiz is available by Wednesday and must be completed before the Friday session. Programming exercises are due before the next Monday session. You may distribute this work across the week. The learning guide explains how to use worked examples, inline checks, hints, solutions, debugging feedback, and exercise identifiers.

What you will learn

By the end of the course, you should be able to:

  1. Frame a computational problem using its intended user or decision, inputs, outputs, state, constraints, assumptions, and acceptance criteria.
  2. Decompose a problem and represent an algorithm using precise language, pseudocode, a flowchart, state traces, or examples.
  3. Prepare, analyse, check, and visualize tabular data in Excel.
  4. Read, trace, write, test, and adapt small Python programs using values, expressions, functions, conditions, loops, and collections.
  5. Choose and process suitable simple representations for sequences, records, and persistent CSV data.
  6. Build a small reproducible simulation, export fixed-schema results, and inspect them in Excel.
  7. Identify syntax, runtime, and logic errors and select normal, boundary, invalid, and extreme tests.
  8. Organize a modest complete program using clear function responsibilities and explicit data flow.
  9. Review unfamiliar or tool-generated code through prediction, tracing, testing, simplification, and revision.
  10. Communicate assumptions, limitations, and the evidence supporting a computational result.

Assessment

The written examination determines 100% of your course grade. Weekly quizzes, paper exercises, and programming exercises can add at most 0.5 points to your final course grade.

Written examination

Table 2 gives the weighting of the 2 examination components within the planned 3-hour examination.

Table 2: Written examination blueprint
Component Weight
Multiple-choice questions 60%
Open question 40%
Total 100%

The pass threshold in the official study-unit record is 5.5. Generative AI is not permitted during the examination.

The examination covers terminology, algorithms, pseudocode, Python tracing, Excel interpretation, function behaviour, conditions, loops, collections, files, simulation, debugging, test selection, program design, and evaluation of proposed or tool-generated solutions.

Python is the reference language. Questions may use precise pseudocode when they assess program design rather than Python syntax.

Practice and bonus points

Each weekly bonus activity contributes separately:

  • Canvas quiz: The quiz is available by Wednesday and must be completed before the Friday session. Canvas grades it automatically, and its score contributes to the bonus. The closed questions are discussed in class on Friday.
  • Friday paper exercise: Complete and hand in the paper during the 105-minute Friday session. A completed submission earns a binary completion bonus. Attendance alone does not earn bonus credit, and there is no makeup.
  • Programming exercises: Work independently at any suitable time. They are due before the next Monday session, and completed exercises earn bonus credit.

Canvas publishes the released activities, deadlines, and conversion to bonus points. Together, the activities can add at most 0.5 points to the final course grade.

How the modules fit together

The seven modules form three stages:

  1. Frame problems and evidence (Modules 1–2). You define computational problems, design algorithms, and use Excel to prepare, analyse, check, and communicate tabular data.
  2. Build executable solutions (Modules 3–5). You translate algorithms into Python, organize decisions in functions, and use loops to build a reproducible simulation.
  3. Integrate and assure results (Modules 6–7). You process records and files, then test, validate, and improve a complete simulation-based solution.

Formal Python programming begins in Module 3. Excel is taught in Module 2 and reused later for checking and communication. The module pages in the course navigation show the current chapter sequence and released material.

Workload

Table 3 accounts for the 84 study hours associated with the course’s 3 EC.

Table 3: Course workload allocation
Activity Hours
7 Monday module launches 12.25
Self-paced learning across 7 modules 43.75
7 Friday practice and support sessions 12.25
Examination review 1.75
Focused examination preparation 11.00
Written examination 3.00
Total 84.00

The independent-study allocation includes reading, worked examples, Canvas quizzes, programming exercises, debugging, and review.

Required materials

  • The DACT course materials for Python, Excel, guided study, and practice.
  • A laptop capable of running the course Python environment and Microsoft Excel.
  • Microsoft Office with Excel for Module 2 and later checking and communication tasks.

A tested browser or notebook fallback is provided when installation problems would otherwise block learning.

Policies

Attendance, participation, and access

Attendance is not graded. Live sessions use individual work, peer comparison, TA questions, feedback, and revision. Friday paper exercises earn bonus credit only when they are completed and handed in during the session.

You may participate by speaking, writing, drawing, adding to a whiteboard, testing an idea, asking a prepared question, or explaining a result. Speed, eye contact, and spontaneous speaking are not measures of understanding.

There is no makeup for the Friday paper exercise. The written examination must be completed through an official examination or resit opportunity.

Contact the teaching team early if a course format, tool, room, schedule, or communication route creates a barrier. Formal teaching and examination facilities follow the applicable UT and BMS process.

Deadlines and formal arrangements

Use Canvas for tasks, quiz and programming-exercise deadlines, bonus rules, and examination instructions. Use TimeEdit for scheduled times and rooms.

Contact your study adviser promptly when personal circumstances affect an examination or formal requirement.

Academic integrity

Submit work and explanations that accurately represent your own decisions and contribution. Cite sources, collaborators, reused code, and tools when required.

Do not present unverified generated output as evidence, share answers from a restricted task, fabricate results, or allow another person to complete an individual check or examination for you.

The applicable BMS regulations and UT guidance on referencing and plagiarism remain authoritative.

Use of generative AI

Each task uses 1 of 4 labels:

  • No AI: complete the task without generative-AI assistance. The written examination uses this label.
  • Human first: make your own prediction, representation, or plan before using AI.
  • AI permitted: use bounded assistance, disclose material use, verify the result, and remain able to explain it.
  • AI expected: comparing or auditing generated output is part of the task.

The task label controls. Never upload confidential, personal, partner, assessment-secure, or licensed data to an unapproved service.

Communication

Use Canvas announcements and the DACT module for information that applies to the whole class. Use the shared Canvas route for questions whose answers may help others. Email the instructor for individual or private matters.

Accessibility and support

Contact the teaching team early when you need a usable route through a course activity. The IEM study adviser is the primary programme contact for disability, chronic illness, mental-health conditions, personal circumstances, and formal facilities.

In an urgent emergency, call 112.