BS231-5-SP-CO:
Computational Data Analysis: R for Life Sciences

The details
2019/20
Life Sciences (School of)
Colchester Campus
Spring
Undergraduate: Level 5
Current
Monday 13 January 2020
Friday 20 March 2020
15
05 September 2019

 

Requisites for this module
(none)
(none)
(none)
(none)

 

(none)

Key module for

BSC C700 Biochemistry,
BSC C701 Biochemistry (Including Placement Year),
BSC C703 Biochemistry (Including Year Abroad),
BSC CR00 Biochemistry (Including Foundation Year),
BSC C400 Genetics,
BSC C402 Genetics (Including Year Abroad),
BSC C403 Genetics (Including Placement Year),
BSC CK00 Genetics (Including Foundation Year),
BSC C410 Genetics and Genomics,
BSC C411 Genetics and Genomics (Including Placement Year),
BSC C412 Genetics and Genomics (Including Year Abroad)

Module description

The amount of data generated by biological experiments is increasing exponentially, mainly due to the development of new powerful technologies for the acquisition of large-scale genetic and genomic data sets. If we would compile the DNA sequence of the human genome into a book, it would be a 200,000 pages book that will take 10 years to read.

Bioinformatics became a compulsory skill for next generation biologists. In recent years, R became the programming language of choice for bioinformatics and biologists in academia and industry are currently using many tools that were developed in R. Computational Data Analysis: R for Life Sciences provides a basic introduction to programming for biologists in R and aims to provide students with the necessary programming skills and hand-on experience in performing data analysis with R. This module would be essential for further bioinformatics courses that students would take in their third year.

Module aims

No information available.

Module learning outcomes

In order to pass this module the student will need to be able to:

1. write scripts and functions in R and comment the code;
2. read and write data files in different formats;
3. use the basic plot functionalities of R;
4. write documentation and examples of how your functions and scripts should be used;
5. perform basic statistical analysis in R (correlation analysis and statistical tests);
6. demonstrate the ability to work as part of a team.

Module information

No additional information available.

Learning and teaching methods

Lectures - 12h Workshops - 12 x 2 = 24 h

Bibliography

  • Richard Cotton. (2013) Learning R, Sebastopol, CA: O'Reilly.
  • Andy Hector. (2015) The new statistics with R: an introduction for biologists, Oxford: Oxford University Press.

The above list is indicative of the essential reading for the course. The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students. Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Weighting
Coursework Code Assignment 1 15%
Coursework Code Assignment 2 15%

Overall assessment

Coursework Exam
100% 0%

Reassessment

Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Nicolae Radu Zabet, Prof Leo Schalkwyk, Dr Toni Marco
School Undergraduate Office, email: bsugoffice (Non essex users should add @essex.ac.uk to create the full email address)

 

Availability
No
No
Yes

External examiner

Dr Clive Butler
The University of Exeter
Associate Professor of Microbial Biochemistry
Resources
Available via Moodle
Of 45 hours, 45 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).

 

Further information
Life Sciences (School of)

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