The complexity, scale, and messiness of biological data require computational tools, but also critical thinking and an understanding of computational science. This course provides a solid but accessible introduction to the mathematical, statistical, and computational foundations needed to handle incomplete data, construct models, and draw meaningful conclusions. Designed as a “bootcamp” for students without a background in computational science, it focuses on developing skills in the languages ​​of data analysis, modeling, and algorithmic thinking. Students learn to quantify variation, adapt models, and structure real-world systems as networks, equations, or code. The course aims to lay the foundation for independent and critical engagement in modern quantitative biology and biotechnology.

The course is based on an interactive and practically oriented pedagogical method. Each lecture is accompanied by a guided computer exercise in Python. The approach reinforces practical skills in parallel with theoretical understanding. Students gradually build up their own reference material and carry out a project where they apply the course concepts to a computational problem in the life sciences.

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

• explain and apply central concepts in probability theory, statistics, modeling and inference relevant to the life sciences.
• translate biological questions into mathematical and computational problems.
• construct, adapt and evaluate models of biological systems using Python.
• analyze simple data structures and apply basic optimization methods.
• communicate quantitative reasoning effectively.

Twelve two-hour sessions, lecture and lab in the same room. Roughly the first 30 minutes is lecture and the rest is hands-on work depending on the session. Python is the course programming language.

The sessions are run flipped: a short reading and that session's lecture video are assigned ahead. Each session has three parts: Read, Watch, Build, which appear in that session's Canvas module.

All twelve sessions are two hours, lecture and lab in the same room.

• Project exhibition: Thu 8 October, 13:00-18:00. Room announced in Canvas and in TimeEdit.
• Self-study (eget arbete): Mon 12 to Wed 14 October
• Exam (TEN1 + REF1): Wed 21 October, 08:00-13:00, U41 and U51
• Re-exam (omtenta): Wed 16 December, 08:00-13:00, M2

Session 5 was moved by KTH Schema on 13 August from 13:00-15:00 in U1 to 15:00-17:00 in H1. Note that H1 is a different building from U1.

TimeEdit is the authoritative schedule. If this course memo and TimeEdit ever disagree, TimeEdit is correct.

Two recommended references, both free online. Neither is required to pass.

• Bohm & Zech, Introduction to Statistics and Data Analysis for Physicists, Verlag Deutsches Elektronen-Synchrotron, 2010. Free download from the DESY library. Page numbers in the reading assignments are the book's printed page numbers; a PDF viewer counting from the cover is offset by 14 (printed p. 55 is PDF page 69).
• Holmes & Huber, Modern Statistics for Modern Biology, Cambridge University Press, 2019. Complete text free at huber.embl.de/msmb. Cited by section number only, as the free edition is a web book with no page numbers. Its code is R and its datasets are Bioconductor packages; in this Python course the text and figures transfer and the code does not. Read the sections, do not run the notebooks.

Readings are assigned as short named sections. Each session's reading is posted in that session's Canvas module closer to the session date.

Bring a laptop or tablet with a browser and wifi to every session, and your phone for the attendance QR code.

All lab work runs in Google Colab, so nothing needs to be installed on your own machine. Python is the course programming language.

You will also need a free GitHub account. Every session deposits work into your own course repository. Public visibility is not a course requirement: you may keep the repository private and grant access to the examiner only, or work in KTH GitLab instead. The assessed object is the repository and its commit history, not its visibility.

Students at KTH with a permanent disability can get support during studies from Funka:

Funka - compensatory support for students with disabilities

If you have been granted compensatory support (Funka), contact the examiner well before the examination so that the arrangements can be made in time. Support that arrives after the exam is booked cannot always be accommodated.

• TEN1 - Written exam, 3.5 credits, grading scale: A, B, C, D, E, FX, F
• LAB1 - Laboratory Work, 1.5 credits, grading scale: P, F
• PRO1 - Project , 2.0 credits, grading scale: P, F
• REF1 - Reference sheet, 0.5 credits, grading scale: P, 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. If the course is discontinued, students may request to be examined during the following two academic years.

How the components combine. They do not get combined, and that is deliberate. LAB1, PRO1 and REF1 are gates: each is pass/fail and all three must be passed for the course to be reported. TEN1 sets the grade. Nothing done in the labs raises or lowers the exam grade, and exam performance does not affect lab status.

Exam (TEN1 + REF1): Wednesday 21 October 2026, 08:00-13:00, rooms U41 and U51.
Re-exam (omtenta): Wednesday 16 December 2026, 08:00-13:00, room M2.
Project exhibition: Thursday 8 October 2026, 13:00-18:00. Room announced in Canvas and in TimeEdit.
Project repository deadline: Sunday 11 October 2026, 23:59.

Permitted aids: the exam is closed-resource except for your own reference sheet (REF1). Deciding what earns a line on that sheet is the revision exercise.

Collaboration. The project is done in groups. The labs and the reference sheet are individual. You may discuss them freely, but the notebook you push and the sheet you bring to the exam must be your own.

The project (PRO1). The project is exhibited rather than presented. On 8 October each group sets up a demo of its work and the class circulates in two rounds, so that every group both exhibits and visits the others. Examiner and teaching assistants assess each exhibit pass/fail on the day, against criteria announced in advance. Students also vote in a small number of named categories; the vote is for recognition only and has no effect on any grade, as grades are decided by the examiner. Each group also submits its repository with a one-page README by 11 October. A stranger should be able to clone it and run it.

LAB1 is assessed on participation. It is recorded two ways: scan the attendance QR code shown in the session, and push that session's notebook to your own course repository. The criterion is that the work has been done; it will not be assessed for accuracy or performance.

The criteria are written against the intended learning outcomes. Grades A to E are all passing grades, separated by the degree to which the outcomes are demonstrated.

Outcome 5 (communicating quantitative reasoning) is examined in PRO1, and the applied half of outcome 4 in PRO1 as well. Both are pass/fail, so neither affects the grade.

A grade of Fx on TEN1 marks that the module can be completed to a passing grade within a limited time. The complementary task is designed from the specific intended learning outcomes you did not reach, so that you can show you have reached them at the lowest passing level.

You have 15 working days from the day the Fx grade is communicated to you through Ladok; after that the complement can no longer be made.

It is your responsibility to initiate the complement. Contact the examiner once the Fx grade appears in Ladok. The complement takes the form of an oral examination on the outcomes you did not reach, arranged individually.

A passing grade on TEN1 may be raised (plussning). Plussning is done at the ordinary examination or at a re-examination occasion. A separate occasion will not be arranged for it. For this course that means the re-exam on 16 December 2026, and you register for it in the same way as for the ordinary examination.

Plussning does not apply to a course that is already part of an issued degree. LAB1, PRO1 and REF1 are pass/fail and so have no grade to raise.

Up to five of the twelve sessions may be missed and compensated. For each one, push the session's notebook and complete the compensation task. You may be asked to answer comprehension questions about your work, and it is up to the discretion of the examiner to decide if the work satisfies the participation requirement for that session.

Missing more than five means LAB1 is not passed in this course offering.

Results for all components and the final grade are registered in Ladok within 15 working days of the examination. For the 21 October exam that means by 11 November 2026.

• 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.

You must honestly disclose any help received and any sources used. Where work is assessed orally, each student must be able to present and answer questions about the work submitted. All members of a group are responsible for the group's work.

You are obliged to read and follow the instructions given about what applies during the examination. Failure to do so may lead to disciplinary sanctions.

Use of generative AI. You may use generative AI in this course. You may generate any code you like, and you must be able to say what every line does and how you would know if it were wrong. The suggested protocol is: attempt it, get stuck, stay stuck for a bounded and deliberate period, then use every tool you like, including asking the machine to explain what you got wrong. The exam is closed-resource except for your own reference sheet. The full course-specific statement is on the Canvas page "Use of generative AI in CB2330".