Introduction to scientific computing and data science

The materials in this repository were compiled for the course "C268/01: Foundations of data-driven health science", developed for the Graduate School of Health Sciences, Aarhus University, Denmark.

Learning objectives

• Summarize how the main components of a computer relate to, and constrain, the act of "computing".
• Describe the basic organisation of a file system, and navigate it using commands in a "terminal".
• Contrast textual and binary files in terms of their contents and find information in both using tools that can be automated.
• Contrast local and non-local computing resources and file systems, and formulate use cases for both.
• Use variables in a programming language (python) and perform simple operations (manipulations) on the information (data) they contain.
• Write a program to extract, collate and preprocess "raw" data for further processing (statistics, visualisation, etc.).

Prerequisites

• a personal computer < 5 years old, with
  • a 64-bit operating system (Windows, Mac or Linux)
  • administrative privileges (password)
  • minimum 20 GB free disk space
• internet access

How to use the materials

Most of the materials are written as jupyter notebooks. Reading and executing these require successful installation of a particular software environment.

You can browse through the materials in static form (i.e. without being able to execute any code) on the GitHub repository-pages.

To get started, go to the overview-notebook and follow the links.

Using Docker image

A way to get the materials in an all ready functioning and prepackaged way is to use the Docker image.

To use the image:

• Install Docker https://docs.docker.com/install/
• Get the Docker image: docker pull meegcfin/scb:latest
• To run the Docker image in a terminal write:

  • docker run -it --rm -p 8888:8888 meegcfin/scb:latest start-notebook.sh

  • This will give a line that looks like this:

    http://localhost:8888/?token=56d666e84b27539b51adf4257dc4ddcb8cadd2

    Copy and paaste that into a web browser and you are ready to run.

  • If you prefer to use Jupyter Lab start the image with:

    docker run -it --rm -p 8888:8888 -e JUPYTER_ENABLE_LAB=1 --rm meegcfin/scb:latest start-notebook.sh