This repository contains computing groupwork for the MSc Computational Methods in Ecology and Evolution at Imperial College London.
This repository is divided into weeks, with the directory for each week containing a combination of the following subdirectories:
- Data - contains data files used during the week.
- Code - contains all code scripts for the week.
- Results - empty directory for any results files to be pushed into if they exist.
Scripts in the Code/ directory are typically run on files in the Data/ directory, with results (if there are any) being pushed into the Results/ directory.
E.g.
$ python3 Week2/Code/align_seqs_better.py Week2/Data/407228326.fasta Week2/Data/407228412.fasta
Introduction to the Python programming language.
Topics covered:
- Basics of Python as a programming language.
- Basic Python data types and structures.
- How to write clean and well-annotated Python scripts for automating computing tasks.
- How to write Python functions and programs.
Notes: Biological Computing in Python I
Introduction to the R programming language.
Topics covered:
- How to use R for data exploration
- How to use R for data visualization and producing elegant, intuitive, and publication quality graphics.
- R data types & structures and control flows.
- How to write and debug efficient R scripts and functions.
- How to use R packages and applications in certain areas (e.g., Genomics, Population biology).
Notes:
More advanced Python topics.
Topics covered:
- Python program testing, debugging and documentation.
- How to use Python for retrieving, managing, and analyzing data from local and remote databases. • to automate file handling, string manipulation, and run shell scripts.
- How to use Python for efficient numerical analyses.
- How to run analyses by patching together R or R + Python scripts and functions.
Notes:
This project was developed on a UNIX OS.
The following packages (with versions) are used in the project:
- Python (3.7.7)
- R (4.0.3)
randomnumpymathpandasmatplotlib
toolsdplyr
Email: