Getting Started

This section contain essential information for getting up and running for the classes

When and Where

Teaching sessions will be E3A, Tuesday mornings 8 - 12 in building 358, room 060a (exercises: Also room 045) and the general schedule will be:

  • 08.00 - 08.30 Recap of key points from last weeks exercises
  • 08.30 - 09.00 Introduction to theme of the day
  • 09.00 - 12.00 Exercises

Please note, at DTU we do not use “the academic quarter”, this means that 8 am, means that class will commence at 8 am

Class Rules & Expectations

What I Expect From You (the Students):

  1. Active Participation
    • Come prepared to engage with both coding and biological data science concepts.
    • Ask questions, curiosity drives learning.
  2. Effort Over Perfection
    • Mistakes are part of programming and science. Document them, learn from them, and keep iterating.
    • Show your reasoning process, not just the final result.
  3. Collaboration & Respect
    • Support each other in learning R, teaching your peers is one of the best ways to learn yourself.
    • Respect diverse perspectives and backgrounds.
  4. Professional Conduct
    • Meet deadlines unless unforeseen circumstances arise (in which case, communicate early).
    • Uphold academic integrity: your code should be your own work unless group collaboration or AI use is explicitly stated.

What You Can Expect From Me (the Course Responsible):

  1. Clarity & Structure
    • I will explain concepts step by step and connect theory to practice.
    • I will provide clear instructions and transparent guidelines and expectations.
  2. Accessibility & Support
    • I will make myself available during class and on Slack for questions.
    • I will provide constructive feedback to help you grow.
  3. A Safe Learning Environment
    • No question is “too simple”
    • I will foster a respectful, inclusive space for all students.
  4. Practical Relevance
    • I will use real-world biological datasets where possible.
    • I will highlight how R skills connect to research and professional opportunities.

Logging onto Cloud Server

First, make sure you have a working DTU account, either as a student id or employee initials (e.g. PhD-students or postdocs) and that your multi factor authentication is functional. In this course we will be using a cloud server infrastructure to perform our work.

Please click here to test your access the cloud server: R for Bio Data Science Cloud Server

Slack as Class Communication

To streamline class communication, we will be using Slack. The aim is to facilitate getting you help fast and efficiently from classmates, the TAs, and myself. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Slack. Each teaching lab will have its own dedicated Slack channel. Note: You can also make group specific Slack channels!

Please join the Slack workspace by clicking here

Setting up a GitHub account

Prior to class, please go to GitHub and setup and account, shouldn’t take long. During the registration process you can set up a student account, which will give you additional benefits, including GitHub Pro. In order to get it, you need to use your DTU email when setting account and select Apply for your GitHub student benefits when asked during the registration process. You’ll be then asked to apply for GitHub Student Developer Pack, which will require uploading your student id photo. The process of confirming a student account may take up to a few days (however, it can be almost instantaneous). In the meantime you can already use your free account. Free account should be sufficient for this class, so if you don’t want to set up a school account, you don’t need to.

Please state your GitHub username in this google sheet.

We will then invite you to the Github organisation for this year’s course. This is where you will learn how to do collaborative data science.

Group Formation

The backbone of this course is modern collaborative data science and as such group work and active participation herein is mandatory. Furthermore, It is important that course participants prioritise to be present during classes as the course design is based on student-student interaction in an active learning environment. Therefore, students will work in groups of 4-5 students.

Please click here and fill in the group formation sheet

Pre Course Questionnaire

It is important for the teaching team to understand the class composition.

Please click here and fill in this brief and 100% anonymous questionnaire