Taken by students from:

Code	Degree Title	Year of study	core or option	credits
P-F3P4	Complexity Science MSc	1	core	12 CATS
P-F3P5	Complexity Science MSc+PhD	1	core	12 CATS
P-F3P6/7	Erasmus Mundus Masters in Complex Systems	1	optional core	6 ECTS

Context: This is part of of the Complexity DTC taught programme.

Module Aims:

This module aims to provide students with an introduction to basic techniques for data analysis, statistical modelling and inference, time-series forecasting and quantification of uncertainty.

Syllabus:

1. Introduction to probability and Bayes' rule, data and error bars, statistical models.
2. Statistical inference and fitting a model to data, Maximum Likelihood Estimation, curve fitting and linear regression, model selection, statistical significance.
3. Spectral methods: Fourier series and applications, Discrete Fourier Transform, spectral analysis of time-series data, using the Fast Fourier Transform.
4. Models of time-series data, fitting models to timeseries data and forecasting.
5. The module will include a group project which will challenge the students to apply the techniques covered in the lectures to make concrete predictions from particular data sets.

Illustrative Bibliography:

C.M. Bishop, Pattern Recognition and Machine Learning, Springer 2006

J.D. Hamilton, Time Series Analysis, Princeton University Press 1994

G.E.P. Box, G.M. Jenkins and G.C. Reisel, Time Series Analysis: Forecasting and Control, Prentice Hall 1994

Teaching:

Lectures per week	2 x 2 hours
Classwork sessions per week	2 x 2 hours
Module duration	5 weeks
Total contact hours	40
Private study and group working	60

Assessment information 2013 / 2014:

Week	Assessment	Issued	Deadline	how assessed	%credit
1/2	Problem sheet #1	8.11.	19.11.	written script	20
3/4	Problem sheet #2	17.11.	4.12.	written script	30
5	Oral Examination	6.12.		Oral examination	50