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Cathie Marsh Institute for Social Research (CMI)

Data Carpentry for the Social Sciences with R

Date: 12-13 December 2019
Time: 10am - 4.30pm
Instructor: Peter Smyth
Level: Introductory
Fee: £390 (£280 for those from educational, government and charitable institutions). 

CMI offers up to five subsidised places at a reduced rate of £60 per course day to research staff and students within Humanities at The University of Manchester. These places are awarded in order of application. 

Humanities PGR students at The University of Manchester can apply for a methods@manchester bursary to help cover their costs. All applications will be considered on a case-by-case basis and applicants will be required to provide a supporting statement from their supervisor. Applications for bursaries must be submitted at least two weeks in advance of the course date; applications submitted after this time will not be accepted. Retrospective applications cannot be made if courses have already taken place or payment has already been made.

Please click here to make a booking. If you are applying for a subsidised place, select the £60 University of Manchester option on the booking form. For queries about methods@manchester bursaries, contact methods@manchester.ac.uk (please note, you must have a confirmed place on the course before requesting a bursary application form). For any other queries about short courses, please contact cmi-shortcourses@manchester.ac.uk.

Please note: this is not guaranteed and is considered on a case by case basis. Please contact us for more information.

Outline

It is no longer possible to conduct meaningful research without access to and the processing of data. The Data Carpentry organisation develops and teaches workshops on the fundamental data skills needed to conduct research.

Data carpentry is not just about what is taught, but equally importantly it is about how it is taught.

Different research domains each have their own sources and formats of data. So, the data skills taught by data carpentry are designed to be domain-specific. The data carpentry curriculum for social scientists was developed at The University of Manchester using a dataset representative of survey data: it is not perfect, it has missing data and typos. This allows us to create lessons which are meaningful to real researchers. Within the workshops, students are shown how to examine the data and where necessary to clean and re-organise it into formats which can more easily be analysed using readily available software such as Excel and OpenRefine. Simple data analysis techniques are demonstrated too and practised by the students using a variety of software tools such as SQL and either R or Python. Each tool is introduced on the assumption that the student has no previous knowledge of the software. 

Data Carpentry workshops are for any researcher who has data they want to analyse, and no prior computational experience is required. This hands-on workshop teaches basic concepts, skills and tools for working more effectively with data.

Objectives

On completion of this course, the participants will be able to:

  • understand problems which can occur in spreadsheets of data
  • create ‘clean’ spreadsheets either from scratch or by reformatting a ‘dirty’ spreadsheet
  • use OpenRefine to clean datasets and perform basic exploratory data analysis on the data
  • use SQL to summarise data and to join different data sources
  • use basic functionality in the R programming language to perform basic EDA (Exploratory Data Analysis) and to create a simple visualisation of data.
  • convert data formats using R
  • understand the importance of documenting work for future use.

Prerequisites

None.

Recommended reading

  • Wickham, Hadley (2014). “Tidy Data” Journal of Statistical Software, August 2014, Volume 59, Issue 10.

About the instructor

Peter Smyth is a Research Associate at The University of Manchester, based in the Cathie Marsh Institute. He has spent 35 years working in IT at various large and small commercial organisations before taking an MSc in Big Data Analytics at Sheffield Hallam University and moving into academia. In his previous roles, he used any convenient programming environment to hand to solve problems. Now he teaches a variety of programming languages to help others to do the same.

He is a qualified Data and Software Carpentry instructor.

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