# Module MA22S6: Numerical and Data Analysis Techniques

**Credit weighting (ECTS)**- 5 credits
**Semester/term taught**- Hilary term 2016-17
**Contact Hours**- 11 weeks, 3 lectures plus 1 tutorial 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 interference;
- Data modelling - chi^2 analysis;
- Introduction to Markov processes;

**Module Prerequisite**- Either MA1S11 or MA1M01
**Assessment Detail**- This module will be examined in a 2 hour
**examination**in Trinity term.**Continuous assessment**will contribute 20% to the final grade for the module at the annual examination session. Supplemental exams will consist of 100%.