• Event
  • Scientific training

Data Carpentry Workshop 2017

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

Start date:

21 November 2017

End date:

22 November 2017

Time:

09h00 - 17h00

Venue:

Earlham Institute

Registration deadline:

17 November 2017

Cost:

£60.00

About the event.

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

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. We will cover data organisation in spreadsheets, data cleaning, SQL, the command line, and R for data analysis and visualisation using examples from biology. Participants should bring their laptops and plan to actively participate. By the end of the workshop, learners should be able to more effectively manage and analyse data and apply the tools and approaches directly to their ongoing research.

Data Carpentry's aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain.

The course is aimed at researchers in the life science and computational science disciplines at all career stages. We particularly encourage students and post-doctoral scientists to attend, but the course is open to everyone.

There are no pre-requisites, and we will assume no prior knowledge about the tools.

Organisers and trainers.

Programme.

Day 1 - 21 November 2017

Time

Topic

09:00 - 13:00

Data organisation in spreadsheets and OpenRefine for data cleaning

13:00 - 17:00

Introduction to R

Day 2 - 22 November 2017

Time

Topic

09:00 - 13:00

Continuation of R: data analysis & visualisation

13:00 - 17:00

Data management with SQL