Finding your Programming Language for Data Analytics and Quantitative Research: R, Python and Julia

Throughout your PhD, you’ll see many training courses that you may want to attend which use R, Python, or other programming languages like Julia; most of these courses will require you to install these languages beforehand. Maybe you have already decided you want to do all of your analysis and data tidying in a programmatic way using code, so that it is reproducible and can be published in open science repositories, but don’t know where to start or what programming language might work best for you. Installing and configuring new software can be a frustrating and isolating experience, especially if you’re completely new to programming. If you’re finishing your PhD, you might be thinking about learning how to apply some of your skills analysing data in different software in order to improve your career prospects.

At this event, we’ll spend the morning together installing and configuring R, Python, and Julia and some associated Integrated Development Environments. After lunch, there will be three short workshop-presentations you’ll be able to follow along with that demonstrate some of the differences between the three languages when analysing real data. The aim of the course is to help you get over the initial stumbling blocks that all new users of programming languages for social research face with a lot of support and minimal frustration.

By the end of the day, you will have set up your personal computer in such a way that you will ready to jump into any specific data analytics or advanced quantitative research training, regardless of the programming language it’s delivered in. You’ll also have had the chance to meet and network with other researchers at similar stages in their PhD, or with similar experiences of using programming languages to analyse data.

“It’s absolutely great to be walked through these languages and programs, and everything has been explained very well to just the right level of detail for an intro day. I was afraid I would have to learn these alone so I’m very glad to have the help.” – Feedback from a previous participant

Please note that you must bring your own laptop to this event. It is strongly recommended that you also bring a charger and charging cable.

You do not need any prior experience of programming or data analysis to join this course, and you do not need to install anything in advance. PhD students in their first year doing quantitative research projects will find this course particularly relevant.

Outcomes

Participants will:

• Install and configure programming languages and Integrated Development Environments that will cover most software pre-requisites for training offers from the WRDTP and beyond;
• Gain some familiarity with the syntax, strengths, and weaknesses of each programming language;
• Meet and connect with other doctoral researchers who are embarking on similar journeys with statistical programming.

Contributors

• Dr Calum Webb is a Lecturer in Quantitative Social Science and Deputy Director of Advanced Data Analytics at the Sheffield Methods Institute, University of Sheffield. He is a long-time R user and somewhat reluctant Python user, and has recently started picking up the Julia programming language. His background is in sociology and social policy, and has had to develop his statistical programming skills as a social scientist first and foremost, so is familiar with the barrier to entry!
• Dr Jacob Macdonald is a Lecturer in GIS and Spatial Analysis and Director of Advanced Data Analytics at the School of Geography and Planning, University of Sheffield. Jacob has a background in spatial statistics, econometrics, data visualisation, machine learning and capturing measures of the urban environment through remote sensing and secondary administrative data sources. He teaches courses in both R and Python, with an emphasis on promoting open science.