Introduction to Python

Code

VETS4036

School

Veterinary Medicine and Science

Level

4

Credits

20

Semesters

Spring UK, Summer UK

Summary

• Introduction to Python
• Ways to record scripts and good practice
• Controlling flow of a script
• Loading and saving data
• Using modules
• Imaging
• Databases and practical applications

Target Students

Compulsory for MRes Bioinformatics Scientist (Apprenticeship) - September start students.

Classes

Online lectures x 13, 30 hours total Self directed learning - 15 hours total Assessmen - 15 hours total

Assessment

• 100% Report: Reflective Portfolio Piece = 1000 word report - Reflective essay discussing research interests and choice of subject area for project based on a current work based computational problem. & Understanding Python Scripts= Create a python script to complete the tasks outlined in the brief. Annotate and comment the script with what each section would do including called modules, manipulation of data frames and expected outputs.

Assessed by end of designated period

Educational Aims

Learning Outcomes

• Evaluate details of omic-scale/big-data-driven life science making use of core platform technologies (K4).

• Understand techniques to integrate, interpret, analyse and visualise biological data sets (K10).

• Evaluate common bioinformatics programming languages; algorithm design, analysis and testing (K12).

• Develop a working knowledge of relevant big-data and high performance computing platforms including Linux/Unix, local and remote High Performance Computing (HPC), and cloud computing (K16).

• Make use of suitable programming languages and/or workflow tools to automate data handling and curation tasks (S9).

• Apply a range of current techniques, skills and tools (including programming languages) necessary for computational biology practice (S15).

• Contribute to (where appropriate, lead) research to develop novel methodology (S16).

• Build and test analytical pipelines, or write and test new algorithms as necessary for the analysis of biological data (S17).

• Carry out the analysis of biological data using appropriate programmatic methods, statistical and other quantitative and data integration approaches – and visualise results (S21).

Conveners

• Dr Adam Blanchard