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Data-driven Life Sciences course 2024

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Date: 27 August - 1 October 2024

Timezone: Stockholm

Welcome to the Data-driven Life Sciences 2024 course, where you will explore the intersection of data science, artificial intelligence, and life sciences to drive innovation and discovery. This fully online course culminates in an in-person hackathon, fostering a vibrant community that gathers the DDLS and SciLifeLab members.

The 6 modules aim to introduce learners to computer-driven life sciences, covering application areas in data-driven life sciences. Guest lecturers (DDLS Fellows, SciLifeLab fellows, and SciLifeLab facility training providers) will teach topics including technologies and analysis of data sets from proteomics, transcriptomics, biomolecular structure, molecular dynamics simulations, and various imaging techniques. These modules present, analyze, and discuss models of biological phenomena and related scientific breakthroughs based on such data analysis.

Register through this link

Contact: Wei Ouyang: weio@kth.se

Keywords: data driven, AI

City: Online

Country: Sweden

Prerequisites:

As prerequisites for the course, we recommend that you have a look at the following resources:

  • Please have a look at the SciLifeLab Data-Driven Life Science (DDLS) initiative website to understand what data-driven life sciences are, and how Sweden is investing in this area. Focus in particular on the concept of the data life cycle, which is central in this class.
  • We will use Python as the main programming language in the computer lab, so please make sure you know the basics of Python.

For the computer lab, you will need a computer with internet access, and make sure you have the following set up:

  • Install the latest browser, e.g. Chrome
  • Register a Google account for the Google Colab access and use the Google Drive
  • Register a ChatGPT account (Note: No need to subscribe to the paid version of ChatGPT, using the free version is sufficient for this course)
  • Register a Github account for versioning of the code

Learning objectives:

  • Describe the field of data-driven life sciences
  • Present an overview of various application areas
  • Provide examples of applications and their associated analysis methods
  • Apply statistical and machine learning analysis to biological data sets
  • Formulate models of biological phenomena
  • Present and review scientific literature in computer-driven life sciences
  • Reflect on the ethical consequences of data-driven life sciences
  • Practice good data management, including collection, handling, sharing, and analysis

Event types:

  • Workshops and courses

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