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EP272V Network Analytics and Data-Driven Engineering 7.5 credits

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This project course introduces students to data-driven engineering of networks and cloud systems. Using methods from statistical learning, students will develop and evaluate, for instance, models for prediction and forecasting of Key Performance Indicators (KPIs) and for anomaly detection. The models will be fitted and evaluated using testbed measurements or traces from operational systems. The functions built from these models are designed for real-time execution. To develop the models, tools and packages from data science will be used, e.g., Jupyter notebook, scikit-learn, TensorFlow. The course is structured as two consecutive project blocks. Each block starts with introductory lectures that give background and discuss concepts for the specific project, followed by project execution, writing of a report, and interview.

Information per course offering

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.

Termin

Information for Autumn 2025 Start 27 Oct 2025 single courses students

Course location

KTH Campus

Duration
27 Oct 2025 - 12 Jan 2026
Periods
P2 (7.5 hp)
Pace of study

50%

Application code

10063

Form of study

Normal Daytime

Language of instruction

English

Course memo
Course memo is not published
Number of places

12 - 24

Target group

Non-programme students

Planned modular schedule
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Schedule
Schedule is not published
Part of programme
No information inserted

Contact

Examiner
No information inserted
Course coordinator
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Teachers
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Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus EP272V (Autumn 2022–)
Headings with content from the Course syllabus EP272V (Autumn 2022–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

This is a project course, where the students carry out analysis projects individually or in small groups. The projects use data from real systems, for example operational data from networks or computer clouds.

The course includes:

  • initial lessons about selected machine learning techniques that are used in the projects
  • introduction to the tools that should be used
  • projects completed by the students based on message boards and project meetings
  • preparations before the students' project reports.

The project tasks may be changed from one year to another.

Intended learning outcomes

After passing the course, the student should be able to:

  • model an assignment for network analysis
  • pre-process data and design models for prediction based on machine learning techniques and tools
  • evaluate, interpret and apply the results when possible
  • write report that describes and explains project result.

Literature and preparations

Specific prerequisites

In total 180 higher education credits of which at least 90 higher education credits in computer science, electrical engineering or an equivalent discipline

  • Knowledge in statistics, 6 higher education credits.
  • Knowledge in machine learning, 6 higher education credits.
  • Knowledge in networks and computer systems, 6 higher education credits.
  • Knowledge in Python programming, 6 higher education credits.
  • The upper secondary course English B/6

Literature

You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F

Examination

  • PRO1 - Project work, 7.5 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Examiner

Profile picture Rolf Stadler

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

EECS/Computer Science

Main field of study

This course does not belong to any Main field of study.

Education cycle

Second cycle