This course offers an introduction to open and data science combining a pragmatic approach (initiation to programming using python language) with a reflexive perspective. We will follow the different steps of data processing (from data collection to their visualization).
Applied exercises will enable students to learn about programming so as developing a critical thinking of the technical and socio-political stakes undertaking these practices (Science Technologies Studies approach).
The aim of this course is not to train engineers but to give technical autonomy to PhD students with their digital research. They will be prepared to solve data-driven research projects by expressing their need, contributing to open communities, and working with developers, data scientists, computer engineers, project managers, product owners, etc.
The course will be split in two modules, that can be validated separately.
Module 1 (Jan 31st, Feb 1st): First step into open and data sciences: two days to manage your digital research environment ( Digital dip )
These two days will give you a better understanding of your digital research environment and help you to manage your Phd project with open and data science practices.
Step by step, we will open together the “black box” of your computer, take the control of the shell, learn how to structure your working documents and discover free and open source softwares that fits with your needs in terms of open and data sciences. This practical introduction to data-driven research comes with its own context in background. You will be shown critical perspectives on major changes in today’s research produced by the digital world (open access, open data data driven research, digital methods, etc.).
Requirements: No requirements at all. For a fruitful session, list all technical needs related to your digital research workflow you have faced during your PhD or think you will encounter.
Module 2 (February 4th-6th): Practicing open and data sciences : initiation in programming and the basics of data-driven research ( Digital dive )
After crossing the gate and opening the “black box” of your computer, these three days are designed to let you, in practice discover all the open ingredients to start successfully open and data sciences project. Understanding the basics of programming means understanding both how to use it and what it is about (automatisation of algorithmic processes).
You will experiment it by learning at your own pace with simple exercises in Python on a dedicated online learning platform. Moreover, you will learn how to organise your daily research workflow in an open environment (agile methods, free and open source softwares, international and online cooperation, team management, etc.) Along this session, illustrations of open sciences issues, sociopolitical stakes and data sciences challenges – that you might encounter on your research projects – will be given as critical lightning.
Requirements: Validation of the first module or basic understanding of computer organisation, knowledge of daily tasks using the shell, previous installation made during the module 1: python, git, a code editor (Atom, SublimText)