Module Details

Module Code: COMP9087
Title: Scien. Prog. for Biologists
Long Title: Scientific Programming for Biologists
NFQ Level: Advanced
Valid From: Semester 1 - 2021/22 ( September 2021 )
Duration: 1 Semester
Credits: 5
Field of Study: 4811 - Computer Science
Module Delivered in: 5 programme(s)
Module Description: Computational and experimental biologists as well as biostatisticians often develop a 'workflow' that involves several steps such as data collection, analysis, model building and testing hypothesis. This module aims at learning to program for scientific computation. The learners will be equipped with the skills to effectively use a collection of non-commercial tools and libraries for computational biology and bioinformatics.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Analyse fundamental components of high-level programming language.
LO2 Develop programs for solving specific tasks using standard programming concepts.
LO3 Compare programming techniques to clean, transform and automate biological data analysis.
LO4 Integrate off-the-shelf libraries and their associated functionalities to address biological questions.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Introduction
Importance of programming in today's world, categories of programming language, their typical application in scientific computing, basic concepts in Python programming: source code, text editors, syntax, semantics, statements, Python versions and installation.
String Manipulation
Learn to write and execute simple python programs. Works in the context of biological sequence manipulation-concepts of terminals, standard output, variables and naming, strings and characters, special characters, comments, and error handling.
Flow control
Importance of flow control- conditional statements, loops. Learn truth and falsehood, Boolean logic, identity and equality, evaluation of statements, branching, iteration, and ranges.
Data Structures
Various data structures such as arrays, lists, tuples, dictionaries, sets and how to use them.
Functions
Learn how to write functions in Python. Concepts of argument passing, encapsulation, data flow through a program.
File handling
Introduce the importance of files in biological pipelines and workflows, and explore the Python interfaces for reading from and writing to files. Concepts: paths and folders, relationships between variables and values, text and binary files, and newlines.
Python library
How to find and install packages, create data objects defined in the package, and write programs that use these objects. Introduction BioPython (or maybe SciKit Bio)- tools for computational biology and bioinformatics.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Reflective Journal % of Total Mark 50
Timing Week 7 Learning Outcomes 1,2,3
Assessment Description
Compendium of labs into a report ensuring that the student is engaging in practical laboratory sessions.
Assessment Type Project % of Total Mark 50
Timing Sem End Learning Outcomes 2,3,4
Assessment Description
Develop a workflow to address specific biological problems using Python libraries and techniques. Produce a report documenting findings and critically analyzing results.
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Delivers the concepts and theories underpinning the learning outcomes. Every Week 2.00 2
Lab Contact Application of learning to case studies and project work. Every Week 2.00 2
Independent Learning Non Contact Student reads recommended books and practices implementation. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Delivers the concepts and theories underpinning the learning outcomes. Every Week 2.00 2
Lab Contact Application of learning to case studies and project work. Every Week 2.00 2
Independent Learning Non Contact Student reads recommended books and practices implementation. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • Eric Matthes. (2019), Python Crash Course: A Hands-On, Project-Based Introduction to Programming, 2nd Edition. No Starch Press, p.544, [ISBN: 1593279280].
  • Al Sweigart. (2019), Automate the Boring Stuff with Python, 2nd Edition. No Starch Press, p.500, [ISBN: 1593279922].
  • Dr Martin Jones. (2020), Biological data exploration with Python, pandas and seaborn: Clean, filter, reshape and visualize complex biological datasets using the scientific Python stack, Independently published (3 Jun. 2020), p.398, [ISBN: 9798612757238].
  • Tiago Antao. (2018), Bioinformatics with Python Cookbook: Learn how to use modern Python bioinformatics libraries and applications to do cutting-edge research in computational biology, 2nd. Packt Publishing, p.360, [ISBN: 1789344697].
Recommended Article/Paper Resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_SCOBI_9 Master of Science in Computational Biology 1 Mandatory
CR_SNUHA_9 Master of Science in Nutrition & Health Analytics 1 Mandatory
CR_KINDD_9 Master of Science in Technical Communication 1 Elective
CR_SCPBI_9 Postgraduate Diploma in Science in Computational Biology 1 Mandatory
CR_KIDDE_9 Postgraduate Diploma in Science in Technical Communication 1 Elective