Training: Write and Format Your Thesis with LaTeX and Overleaf

*Please sign up to attend both meetings if possible*

This course aims to give attendees an introductory overview of how to write and format a thesis, or any other piece of written work, with LaTeX and Overleaf. By the end of this course you will have written your first LaTeX document, and will have a good introductory knowledge of basic functions of LaTeX and Overleaf.

The training is provided in 2 meetings of two hours’ duration each. Meetings will be a mix of video presentations and hands-on exercises. The last 15-20 mins of class will be used for troubleshooting real project issues, so if you are already working with LaTeX, bring along your own work.

Previous knowledge of LaTeX is not required.

* Please ensure to bring your own laptop / device to course meetings. *

Topics covered:

  • What is LaTeX? And what is Overleaf?
  • Why use them?
  • Writing your first piece of LaTeX
  • The preamble of a document
  • Adding a title, author and date
  • Bold, italics and underlining
  • Adding images
  • Captions, labels and references
  • Creating lists in LaTeX
  • Adding Footnotes
  • Basic Formatting
  • Abstracts
  • Paragraphs and newlines
  • Chapters and Sections
  • Creating tables
  • Adding a Table of Contents
  • Bibliographies with BibLaTeX

 

Digital Scholarship Centre

Digital Scholarship Centre, 6th floor

Main Library 

University of Edinburgh 

Edinburgh EH8 9LJ

You might be interested in

image of people drinking coffee

CDCS May Fika

An illustrative collage with & symbol and a maths graph

Linear Mixed Effects Modelling

An illustrative collage with & symbol and a historical item

Getting Started with Bayesian Statistics

image of head

CDCS Digital Research Prizes Award Ceremony

An illustrative collage with & symbol and an old photograph

Building Personal and Project Websites

An illustrative collage with & symbol and old graphs

Getting Started with Regression in R

An illustrative collage with & symbol and an old photograph

Explainable Machine Learning (XAI)