Accueil > Writing a master thesis the digital way: tools and methods for bibliographic and data management

OBME 2140 - Writing a master thesis the digital way: tools and methods for bibliographic and data management

Type d'enseignement : Seminar

Semester : Autumn 2018-2019

Number of hours : 24

Language of tuition : English

Pre-requisite

No prerequisite. The course includes an initiation to the R language that does not imply any previous coding skill.

Course Description

This course aims to introduce a set of actionable tools and methods for the redaction of a master thesis. It will be more specifically focused on new emerging digital practices that ease significantly the implementation of core tasks. A first series of five sessions will be devoted to reference research. They will cover the use of general and academic search engines, the creation of structured bibliography with Zotero, the reuse of free-licensed content and the writing of state of the art synthesis. Six sessions will bear upon the management and analysis of databases and corpora, building on the the principles of "tidy data" as implemented in the R language and the Tidyverse extensions. A last focus will be given to text mining techniques as a way to map a corpus of scholar references

Teachers

LANGLAIS, Pierre-Carl (Post-doc projet ANR Numapresse)

Pedagogical format

While the course will include general presentations of tools and methodologies, a stronger stress will be put on practical implementation through group work assignments and collective workshop

Course validation

The evaluation will be centered on:
*Two small-group works with practical assignments: managing a Zotero Library (due 4th session), constituting a corpus from web sources (12th session)
*Two individual works: write an article section of Wikipedia (6th session) and clean a messy dataset (9-10th session).
Every work will count for 25% of the final note.

Workload

All sessions will mix introduction to tools and immediate implementation and trial. Courses requirements focuses on “real-life” situations (such as “cleaning” a dataset), that students are likely to meet while writing they master thesis or afterwards in professional life.

Required reading

  • Hadley Wickham, “Tidy Data”, Journal of Statistical Software, vol. 59, n°10, 2014
  • Julia Silge & David Robinson, Text mining with R, A Tidy Approach, O'Reilly, 2018