Module 1

NewQuantitative data analysis 


Module summary:

This module is designed to give supported, hands on experience of computational programming and data analysis within the statistical programming environment R, including the processing and analysis of ‘-omics’ data.


Topics include:

  • Introduction to data analysis environments
  • Working with R: basic computing skills (for loops, conditional statements, data structures etc.)
  • Working with R: reading in and writing out data in R, data manipulations, plotting/data, visualisation.
  • Statistical analysis approaches
  • Bioconductor
  • Analysis of phenomics (Biolog) data
  • Analysis of genomics data (including Galaxy)
  • Analysis of transcriptomics data (microarray, RNA-seq, including Galaxy)
  • Analysis of interactomics data (ChIP/MeDIP/CLIP)
  • Analysis of proteomics data (MASCOT, Scaffold)
  • Analysis of metabolomics data

This online module aims to:

  • To increase awareness of statistical concepts underpinning quantitative/systems biology research in the biosciences.
  • To increase awareness of the utility and need of computational sciences in the biosciences.
  • To increase awareness of the major molecular concepts relevant to current systems biology research.
  • To enable delegates to evaluate the quality of data.
  • To enable delegates to analyse quantitative data using the computing environment R for future independent data analysis in quantitative biosciences research.
  • To provide students with hands on experience of analysing -omics data within the R   statistical programming environment.

On successful completion of this module, students will be able to:

  • Demonstrate an ability to independently programme and analyse data within the statistical programming environment R.
  • Demonstrate an ability to identify and critically evaluate approaches for analysing a given –omics data set.

Module Leader

Emma Laing

Dr Emma Laing

Lecturer in Bioinformatics 

e.laing@surrey.ac.uk 

 

 


Related modules

Integrative interpretation of large-scale data

Systems modelling and network analysis

Register your interest bbsrc

Key Information

  • Start date

    Start Date - On-going  (applicants can apply year round) 


  • Duration

    Duration: 15 weeks/150 hours


  • Cost

    Cost: £1,250


  • Credits

    Award: Certificate of achievement, subject to validation - 15 credits will be awarded as Recognition of Prior Learning (RPL)


  • Delivery of module

    Delivery of Module: Online lectures, live discussion events with academics and industry experts, interactive and self-directed learning exercises based on industry-directed case studies. Delivered via SurreyLearn virtual learning environment.


  • Assessment

    Assessment: TBC


Entry Requirements

At least a 2.2 degree (or equivalent) in a relevant biological sciences discipline. Applicants not possessing these qualifications may be considered, depending on the length and quality of their practical experience and recommendations from employers/supervisors.

Apply Now

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