The general skills module includes the following lectures:

## Python introduction (Andrew Cooper)

This course gives a quick introduction to python, it covers:

- Python syntax
- Collections and loops
- Input and output
- Functions and classes
- Plotting and scientific computing libraries

## Introductory Statistics (8 lectures + workshop, presented by Ifan Hughes)

This course provides the foundation in statistics needed for the other courses. Its syllabus includes

- Review of elementary statistical concepts
- Central Limit Theorem
- Functional approach to error propagation
- Weighted least squares fitting
- Least-squares fitting of complex functions
- Computer minimisation and the error matrix
- Hypothesis testing—how good are our models?

## Introduction to Numerical Methods (8 lectures, 4 drop-in sessions, one project)

This course gives an introduction to numerical methods, based on the book by Giordano and Nakanishi (G&N). In the first part we focus on construction and implementation of basic algorithms in python. In the second part we will use a compiled language to implement numerical methods. The course is assessed through homework and the project.

- Discretization and discretization error (G&N, Chapter 1)
- Solving differential equations: Runge-Kutta method (G&N, Chapter 2)
- Chaos (G&N, Chapter 3)
- Potential and Fields (G&N, Chapter 5)
- Waves (G&N, Chapter 6)
- Root finding and integration (G&N, Appendix B)
- Introductory Monte Carlo method: Random walks and percolation (G&N, Chapter 7)
- Statistical Mechanics and the Ising model (G&N, Chapter 8)

For the project the students were invited to chose a topic in the book but outside the lectures of the course.

## Introduction to Machine Learning (8 lectures, 4 drop-in sessions)

The intruduction to machine learning course provides a basic overview of machine learning techniques with practical exercises using scikit-learn.

- Classification vs regression
- Linear and logistic regression
- Model selection: overfitting and regularisation
- ROC curves
- Decision trees
- Neural networks
- Unsupervised learning: k-nearest neighbours

## Introduction to High-Performance Computing

## Generic Skills components

- ILM-acrredited 2-weeks mini-MBA provided by the Durham Business School
- 3-days workshop on “Communicating Science” provided by the Durham Science Faculty outreach team.
- A series of seminars and presentations by our partners
- 8 weeks team project