• Description

• Directories List

• Requirements

• Contributions

• Credits

This is the master directory for all of my assessed coursework from the CMEE course at Silwood Campus, Imperial College London.

Work from the course is catagorised by contiguous week of the course, from week 1 (starting 05/10/2020) to week 11.

Weeks not listed below had no assessed work for this module, hence the respective directories have nothing of interest for the user.

Please see the README file for a given week in its correspondingly-named directory for (much) more detail.

Author:   Ben Nouhan,   bjn20@ic.ac.uk

Topics covered this week include introductions to:

• Use of UNIX and Linux operating systems

• Shell scripting

• Version control with Git

• Creating scientific documents with LaTeX

Topics covered this week include introductions to:

• Basics of Python syntax and data structures

• Python’s object-oriented features

• Learning to use the ipython environment

• How to write and run python code

• Understand and implement Python control flow tools

• Writing, debugging, using, and testing Python functions

Topics covered this week, almost exclusively related to R, include:

• Basic R syntax and programming conventions

• Principles of data processing and exploration (including visualization) using R

• Principles of clean and efficient programming using R

• Generating publication quality graphics in R

• Developing reproducible data analysis “work flows” as to run and re-run your analyses graphics outputs and all, in R

• Making R simulations more efficient using vectorization

• Finding and fixing errors in R code using debugging

• Making data wrangling and analyses more efficient and convenient using custom tools such as tidyr

• Using additional tools and topics in R eg. accessing databases, building your own packages

Advanced Python topics covered this week include:

• Linear algebra (matrix and vector operations) using scipy.linalg

• Sparse Eigenvalue Problems using scipy.sparse

• Numerical integration (including solving of Ordinary Differential Equations (ODEs)) using scipy.integrate

• Random number generation and using statistical functions and transformations using scipy.stats

• Optimization using scipy.optimize

• Signal Processing using scipy.signal

• Data manipulations and calculations using numpy

The project comprises a workflow, ecoded by /code/run_MiniProject.sh, which extracts bacterial growth data from the .csv file in the data directory, prepares it for analysis, uses it to fit various models (both linear and non-linear), then plots and further analyses those models. It then compiles the written report into a pdf file Ben_Nouhan_Report.pdf - please read this for further understanding of the background behind this project.

All packages not mentioned herein are part of the default installations of Python-3.9.0, R-4.0.3 or LaTeX-2e.

I am not currently looking for contributions, but feel free to send me any suggestions related to the project at   b.nouhan.20@imperial.ac.uk

This project was (almost exclusively) inspired by The Multilingual Quantitative Biologist book: (https://mhasoba.github.io/TheMulQuaBio/intro.html)
Special thanks to Dr Samraat Pawar, Pok Ho and Francis Windram for their help.