Individual tests (20%)

A series of 6 individual tests:

Test 1: Algebra and Calculus (released Week 3 of Autumn Semester)
Test 2: Combinatorics and Probability (released Week 5 of Autumn Semester).
Test 3: Matrices and Sequences (released Week 7 of Autumn Semester).
Test 4: Statistics and Differential Equations (released Week 9 of Autumn Semester).
Test 5: Variables, conditional statements and while loops and Functions and data structures (released Week 2 of Spring Semester).
Test 6: Object Oriented Programming and Using a terminal and an editor (released Week 4 of Spring Semester).

You can complete each test when it is released. The deadline for completing all of them is at the end of the Spring Semester.

I recommend you do each quiz as the corresponding content is covered. Content for the first 4 tests is covered in the Autumn Semester. Content for the last 2 tests is covered in the Spring Semester.

Each quiz corresponds to topics covered in the course. You can attempt each quiz as many times as you wish (your last attempt is the one that counts).

Individual coursework (20%)

A mock coursework is available: here.

Solutions to the mock are here.

Marking criteria

There will be 8 questions, each worth 5 marks:

3 marks are awarded for the quality of the analysis: This means the correctness and the appropriateness of the code.
- 0 marks: Code that does not answer the question
- 1 marks: Code that partly answers the question correctly
- 2 marks: Code that mostly answers the question correctly
- 3 marks: Code that answers the question correctly
2 marks are awarded for the quality of the communication: This means the narrative and story given by words (if appropriate for the question), and the clarity of the code including using sensible variable names.
- 0 marks: No narrative given
- 1 marks: An attempt at some narrative and readable code
- 2 marks: Clear narrative, clear code with descriptive variable names

The coursework for 2025/2026 can be downloaded available here.

Group project (60%)

Build a Python library to provide tools for a problem related to mathematics.

You will evidence your progress with 2 mediums:

A 2 page paper
A 15 minute recorded presentation

Your final submission should include the following 7 files:

A main.tex file: the source file for a 2 page paper written in LaTeX.
A main.pdf file: the pdf file for a 2 page paper written in LaTeX.
A <library>.py file: the source file for your Python library
A test_<library>.py file: the test files for your Python library
A README.md file: the documentation for your Python library
A presentation.mp4 (or similar file format): the video recording of your 15 minute presentation
A contribution.md file: a file describing the contributions of every member of your group.

Marking criteria

The various components of the submission should aim to demonstrate how the following aspects of the work have been addressed:

Communication (both paper and presentation) (30%)

Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
Functionality: Have the functional claims of the software been confirmed?
Presentation: Was the presentation format used appropriately? Were the visual aids appropriate?

Typical description of mark:

Below 40%

Difficult to read and lacks a logical train of thought or argument. Very poor organisation and communication of work.
Between 40 and 49%

Poor style of writing with some parts difficult to follow. Poor organisation and presentation of material.
Between 50 and 59%

Satisfactorily written and presented with adequate technical content.
Between 60 and 69%

Well organised and clearly written with sound technical content. Results analysed and clearly presented.
Above 70%

Very well organised and clearly written with good technical content. Results assessed critically and arguments very well presented and supported.

Scope (50%)

Documentation: Does the documentation have a Tutorial, How to section, Reference and Explanation section? Is it clear? Is the source code clear?
Modularity: Is the code written in a modular way?
Testing: Have tests been written for all functionality of the software?

Typical description of mark:

Below 40%

The work does not correspond to the project description.
Between 40 and 49%

The library includes documentation, some modular functionality and an attempt at automatic testing.
Between 50 and 59%

The documentation follows the Diataxis framework although it is poorly written. The code is modular but has a number of areas of improvement. The tests have been written and test some functionality of the code.
Between 60 and 69%

The documentation is clear. The code is written in a modular way with few areas of improvement. The tests confirm most functionality of the code and the documentation.
Above 70%

Well written documentation, code is modular and follows all conventions and guidelines covered in the course. The tests cover all functionality of the code and the documentation.

Research (20%)

State of the field: Do the authors describe how this software compares to other commonly used packages?
References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

Typical description of mark:

Below 40%

No state if the field or references included.
Between 40 and 49%

An inaccurate state of the field included. Some poor references included.
Between 50 and 59%

An accurate state of the field included. Good references included but with little to no context and/or explanation.
Between 60 and 69%

A well written state of the field and good references with context and depth.
Above 70%

Outstanding state of the field demonstrating a great understanding not only of the library but of the already existing tools. The references are all of high quality and a thorough demonstration of understanding is given.

Note that this assessment has some overlap with the review criteria for the Journal of Open Source Software https://joss.readthedocs.io/en/latest/review_checklist.html. Some examples of papers written for that journal that can be helpful are:

Matching: A Python library for solving matching games https://joss.theoj.org/papers/10.21105/joss.02169
Nashpy: A Python library for the computation of Nash equilibria https://joss.theoj.org/papers/10.21105/joss.00904

Deadline: TBD.

Use of Code Generation Tools

Code generation tools (for example Large Language Models such as ChatGPT or Google Gemini) are increasingly capable of producing working programming code. These tools can sometimes generate high-quality solutions, but they can also produce incorrect, inefficient, or unsafe code.

The purpose of this module is for you to develop your own programming, problem-solving, and debugging skills.

For this coursework, you must submit your own original work.

You must not:

Submit code that you do not fully understand
Submit code generated wholly or partially by automated code generation tools
Use code generation tools to complete assessed programming tasks

You may:

Use lecture materials, textbooks, and official documentation
Discuss general programming ideas with others (but not share code)
Use standard debugging tools

Automated code generation often produces recognisable artefacts. Where there is reasonable evidence that submitted work was generated using such tools, the submission may be treated as an academic integrity violation and penalties may apply.

This policy exists to ensure you develop the skills required for later modules and professional programming practice.

Past group projects

A list of titles of past projects:

2024-2025

Guessing games
Upwards
Geometria
Wordle
ChessEngine
NinePlusTen: Blackjack
Ravioli
SudokuPy: Sudoku Solver
Modelling 2D Collisions with Python
Variance in Variation: Stat F
A library for Normal distributions
Rouletter
Simulating Dice with Python
Pulleys
Investment Appraisal
Motion
Musical Intervals
Projectile
Go Fish
Projectile
Balance Sheet Analysis
Incline Planes Library
GeomPy - The Geometric Problem Solver with Python
RubixCube
Appraisal library
Exploring n-th Roots of Complex Numbers using Python
Hypothesis test
Ratio Analysis
RepMat: Representation theory of finite groups in Python.
Projectiles
Modeling Projectile Motion: A Computational Approach
Appraisal
Sudo Solve
Space
ComplexNum
Balancing Equations with chemicalEquationBalancer
Golf Simulator
Money Manager
DiffEq
Projectiles
Blackjack
SUVAT solver
Sudo
Blackjack Hand Simulator
Blackjack
Blackjack
PCP Finance vs Lease Calculator
Anagram solver
Blackjack
Temptrack
Blackjack
Zipf’s law
Wordle Solver
The art of Splitting the G
Bouncing ball
SUVAT
xG: expected goals
Connect 4
Slot Machines
Tic Tac Toe

2023-2024

LinAlg: Determining and Utilising Bases of $R^n$ Vector Spaces
Solving Lattice Geometry Questions in Python: Geometreg
OrbiPy
relations
Exploring the possibilities of Noughts and Crosses
Revs: Voluments of revolution
Camel
StatsTestLib
Polynomial Python Library
Risk and Return in Financial Markets
Basketball
Projectile motion
Blackjack: A Python Library for Computing Probabilities in a Game of Blackjack
Probability Distributions In Python: PDIP
Financial statement analysis
Elasticity
Quantum
ProjectPy – Projectile Motion
Preypredator
Pyclipse
Capman - Capital Management
The conv library
Random Vehicle Registration Generator
Poker.py: A Python Library for Poker Simulations
2DCentreOfMass: A Python library for the computation of the centre of gravity of a polygon
Project motion
Financial Ratios
Simulating a poker game
Projectile Motion Library
Motion
Hypothesis testing
SUVAT
Investment Appraisals
Modelling a Sudoku Using Python: Automorphic Library
Primes and Their Conjectures
Loan Calculator
Taylor’s Polynomial
investement appraisal
Modelling Particles
Simulating Newton’s Laws of Gravity: GBRT
Survival Analysis Tool
ballthrow.py
SUVAT solver
Blackjack
SUVAT:A Python Library-SUVAT Calculator
Modelling Projectiles project
Macaulay duration: A Python library for describe the volatility of the bond price
The ProfitRatios Library
Graphical Calculator
Using Python to play Blackjack
Rubiks Cube solver
Area and Volume Script
Financial statement
Optimizing Clash Royale Deck Composition: Balancing Elixir Cost and Cycling Efficiency
Understanding House Investments
Zip’s Law
A Python Library to Quantify the Relationship Between Engagement and Achievement while Considering Contextual Factors
Balance sheet analysis
Ciphers
Distributions

2022-2023

Predicting the Landing Point of a Ball using Python
Modelling a pully system
Cup2py - Proof of concent for recursive decentralised peer to peer communication protocal
Music Maker: Python library generating sounds from mathematical expressions
Modeling Lifts with Python: Lifts
Base converter with Python
Using code to create music: jam
Breaking good: generating hydrocarbons
Solving ciphers using Python: CiPy
Rubik’s cube solver
Wildflowers: sequences.py
Shaper creator
Appraisal: A Python library for Investment Appraisal