Course detail

Year-class Project

FSI-KRRAcad. year: 2026/2027

The course simulates the execution of a real engineering project in the field of process engineering. Students work in teams on assignments based on real projects from the Institute of Process Engineering and go through the complete project cycle – from an initial vague problem definition and communication with the client, through planning of work packages and budgeting, implementation and interim presentations, to final documentation and defence.

The teaching combines theoretical inputs (statistics, operations research, data analysis), demonstration examples in Python, and systematic teamwork on the project. Classes are organized in blocks and link methodological development with practical project work, regular consultations with “clients,” and iterative peer review.

Language of instruction

Czech

Number of ECTS credits

3

Mode of study

Not applicable.

Guarantor

prof. Ing. Petr Stehlík, CSc., dr. h. c.

Department

Institute of Process Engineering (ÚPI)

Entry knowledge

Students are expected to have prior knowledge in statistical data analysis, data visualization, mass balance calculations, and Python programming. Basic understanding of technical documentation and teamwork is also required. 

Rules for evaluation and completion of the course

The course is completed with a graded credit. Requirements:

  • active participation in class (maximum 2 absences allowed),
  • continuous progress on the project (control milestones),
  • submission and defence of the final report and presentation.

Assessment is based on the weighted average:

  • Project report – 20%
  • Technical solution and code – 50%
  • Oral defence – 30%

Grading A–F follows the scale defined by the course supervisor.

Aims

The course aims to develop students’ project, analytical, and presentation skills in solving a complex engineering task.

  • It teaches students to work in an environment simulating an engineering project and client communication.
  • Students will practice applying statistics, data analysis, and optimization methods to practical problems.
  • The course strengthens competencies in mathematical statistics and operations research (including network flow modelling and scenario-based optimization).
  • It supports teamwork, argumentation, critical thinking, and presentation skills.
  • Students are introduced to project management, work-package planning, and workload estimation.
  • The course guides students toward independent decision-making, documentation, and defence of results.

Study aids

Prerequisites and corequisites

Not applicable.

Basic literature

Individuálně zadaná literatura dle zaměření projektu.

Recommended reading

Individuálně zadaná literatura dle zaměření projektu

Classification of course in study plans

  • Programme N-PRI-P Master's 2 year of study, winter semester, compulsory

Type of course unit

 

Computer-assisted exercise

78 hours, compulsory

Teacher / Lecturer

prof. Ing. Petr Stehlík, CSc., dr. h. c.

Syllabus

Lectures and practical blocks – 78 hours (13 × 6 hours per week); a combination of theory, workshops, and team-based project work. 

Week 1 – Introduction and Project Definition
Students are introduced to course organization, sample project topics, and basics of data work and Python. Teams select a project, conduct initial brainstorming, and prepare a draft problem definition and project proposal.

 

Week 2 – Project Proposal and Data Preparation
Teams build their project proposal and validate it with the client. They also learn the basics of data cleaning and dataset preparation.

 

Week 3 – Cluster Analysis and Stratification
Students learn the principles of clustering and its use in data segmentation and apply the methods to their project dataset.

Week 4 – Time Series and Forecasting
Teams learn to analyse time series, detect trends and anomalies, and build forecasts using their own project data.

Week 5 – Location and Allocation Problems
Students learn optimization fundamentals and experiment with transport problems in Excel and Python.

 

Week 6 – Multicriteria and Scenario Optimization
Students explore scenario design, risk treatment, and multicriteria optimization and build scenario-based models.

 

Week 7 – Review and Advanced Modelling
Teams refine their models while studying network flows, capacity constraints, and additional modelling techniques.

 

Week 8 – Routing and Scheduling
Students learn principles of route planning and scheduling, including TSP/VRP solved with OR-Tools.

Week 9 – First Project Presentation
Teams present their first version of the solution and receive detailed feedback.

Week 10 – Advanced Topic I
An advanced topic tailored to the teams’ specific project needs is covered.

Week 11 – Second Project Presentation
Teams present advanced versions of their solution to the client and receive targeted recommendations.

Week 12 – Advanced Topic II
Final refinements of models, analyses, and interpretations.

Week 13 – Final Project Defence
Final presentation before the committee and submission of complete documentation.