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OAFP 4920 - Introduction to Data sciences: programming

Type d'enseignement : Elective

Semester : Autumn 2017-2018

Number of hours : 24

Language of tuition : English

Pre-requisite

No technical pre-requisite is required. Students will be guided step by step. This course is designed to overcome the first reluctance for technical environment often perceived as a « black box ». An exercise platform will allow the student to advance at his own pace.

Course Description

This course offers an introduction to data science combining a pragmatic approach (initiation to programming in social sciences using python language) with a reflexive perspective. Each session 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 to develop 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 students. They will be prepared to dialogue with developers, data scientists, computer engineers, project managers, product owners, etc. in order to lead collaborative projects involving data science.

Teachers

  • DE QUATREBARBES, Constance (Agent contractuel /Entrepreneur d'Intérêt Général / Architecte des données)
  • GRUSON-DANIEL, Célya (Ingénieure de recherche)

Pedagogical format

The session will mix several formats : technical and reflexive lighting talks combined with exercies on platform and students project monitoring.

Course validation

Students will complete a series of programming exercises as well as an individual or group project. The notes will cover: • completion of programming exercises (20%) • presentation of the final project (20%) • completion of the final project (60%): • technical (50%) quality and rigor of the implementation

Workload

Exercises between sessions (1h) Individual or group project preparation (1h)

Required reading

  • Rogers, R. Digital methods. MIT press. 2013
  • Gitelman, L. Raw data is an oxymoron. MIT Press. 2013

Additional required reading

  • Schrock, A. R. Civic hacking as data activism and advocacy: A history from publicity to open government data. New Media & Society. 2016
  • Jeffrey Elkner, Allen B. Downey et Chris Meyers. How to Think like a Computer Scientist.2013 http://interactivepython.org/runestone/static/thinkcspy/index.html
  • Goëta, S. et Davies, T. The Daily Shaping of State Transparency: Standards, Machine-Readability and the Configuration of Open Government Data Policies. Science & Technology Studies. 2016 https://sciencetechnologystudies.journal.fi/article/view/60221