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Computational Skills for Data Analysis - Data Carpentry - Dublin

Computational Skills for Data Analysis - Data Carpentry - Dublin

Start Date:
May 28, 2018
Finish date:
May 29, 2018
Booking deadline:
May 21, 2018
Last Revised:
Jun 27, 2018
Code:
2018-DC-Dublin
Hits:
1275
Favoured:
0 Favoure
Price:
free
Location:
Conway Institute, University College Dublin
Group:
User
Level:
Beginners
Available seats:
0 of 35
Tutor:

Description

Contents:
The aim of Data Carpentry is to enable researchers to learn about the computational skills needed for data management and analysis in all domains of research. This 2-day workshop will consist of hands-on workshop sessions on high-quality, domain-specific training covering the full lifecycle of data-driven research. In this course, we will use genomics dataset as a case-study.


Topics covered include:

  • Data organization and cleaning
  • Introduction to Unix/Shell
  • Data wrangling and pipeline/workflow
  • Introduction to R
  • Using R, Unix/Shell and cloud for genomics

Learning goals:
This course aims to teach data organization, cleaning and exploration skills to researchers in
computational biology who wish to analyze data, and to introduce a toolset that can help them in
their work. 

Prerequisites:
This is a course for researchers and students of University College Dublin who are using computers for their
analyses, even if not full time. The target student will know a little bit of command
line/programmatic computer usage, but not consider themselves a data expert.

Eligible participants:
This course is open to the students/researchers of the University College Dublin for free. The external students/researchers must contact the organizers for their registration (malvika.sharan@embl.de, norman.davey@ucd.ie).

Please email the organizers to be added to the waiting list, in case the course is fully booked.

Schedule:
https://malvikasharan.github.io/2018-05-28-Dublin/

Course Organizers:

Malvika Sharan (sharan@embl.de)

Norman Davey (norman.davey@ucd.ie)

Organizing Institutes:

 


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