Exam

Make sure you are familiar with the rules concerning the use of Artificial Intelligence (generative AI) in teaching and exams at DTU

Design

The exam consists of 3 components:

  1. A group project, where all members are responsible for all parts of the project. This is handed in as a code base on the course GitHub repository
  2. An oral group presentation of the project, where all group members, as per DTU rules on oral exams, must be physically present
  3. A two hour multiple choice quiz (MCQ) exam, where general course learning objectives are examined

See course description at DTU course base for further description

Deadlines and Dates

  1. Project code base and presentation must be completed at the latest 23:59 on the day before lab 13. Note, you cannot edit anything on the GitHub repository after this deadline!
  2. The oral group presentation will be on lab 13 of the course, see DTU Learn calender for dates
  3. The MCQ is placed according to the DTU exam schedule

Content

The Group Project

  • Be sure that everyone understands ALL code in the project as all group members are responsible for ALL code
  • The aim of the project is to cover the entire course cycle, so be sure to align your project
  • Be sure to read Project Description
  • Questions? Make sure to consult the Project FAQ
  • Final check? Be sure to consult the Project Checklist

The Presentation

  • The format is 10-slides-in-10-mins on the clock
  • Everyone in the group must be physically present and present a part of the project
  • Expect a few overall questions regarding the decisions you have made throughout your project
  • IMPORTANT: The final presentation in HTML-format must be zipped and uploaded to DTU Learn before deadline, so we can check the GitHub version is identical

The MCQ

  • Duration: 2-hour individual multiple-choice exam
  • Format: In 2024, the exam contained 100 questions, expect a similar number. Each question will be short and have four possible answers, with only one correct answer, and you can select only one per question.
  • Focus: Tests your conceptual and applied understanding of the key elements of the course, especially those covered in Labs 1-9.
  • Approximate content balance:
    • ~60% Core Tidyverse skills: Data wrangling, data visualization, and functional programming
    • ~20% Reproducibility and reporting: RStudio projects, Quarto, and workflow organization
    • ~20% Collaboration and software engineering: Git/GitHub, R packages, and Shiny applications
  • Question style:
    • A mix of conceptual questions (definitions, understanding of principles)
    • Applied reasoning questions (“What would this code snippet output?”)
    • No code writing required, focus is on interpreting and reasoning about code or workflow logic
    • Aids: All aids are allowed (Your lab-notes, scripts, textbooks, Quarto documents, etc.), but NO open internet access.
  • Goal: To assess your understanding of the fundamental principles, tools, and workflows of modern bio data science using R, focusing on comprehension and interpretation, not rote memorization.

For the official exam description, see the course base for 22100 or 22160

If any aspects of the above is not clear, please reach out to the teaching team!