# Module MA22S6: Numerical and Data Analysis Techniques

**Credit weighting (ECTS)**- 5 credits
**Semester/term taught**- Hilary term 2017-18
**Contact Hours**- 11 weeks, 3 lectures including tutorials per week
**Lecturer**- Prof. Stefan Sint
**Learning Outcomes**-
The students will learn in a practical way the main numerical techniques used in different areas of science. They will learn the mathematical background of probability and statistics and most importantly the practical aspects.

On successful completion of this module students will be able to;

- Use discrete and continuous random variables to describe phenomena observed in nature (science experiments, population statistics, ...) and to quantify how well a model works;
- Find a simple model for a given dataset, such as the output of an experiment;
- Perform a chi^2 analysis to estimate the model parameters and their standard deviations;
- Use Markov processes to describe stochastic phenomena;

**Module Content**-
- Probability - Random variables and distribution;
- Sampling - Statistical inference;
- Data modelling - chi^2 analysis;
- Introduction to Markov processes;

**Module Prerequisite**- MA1S11 or MA1M01
**Assessment Detail**- This module will be examined in a 2 hour
**examination**in Trinity term.