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Module MA22S6: Numerical and Data Analysis Techniques

Credit weighting (ECTS)
5 credits
Semester/term taught
Hilary term 2012-13
Contact Hours
11 weeks, 3 lectures including tutorials per week
Prof. Nicolas Garron
Learning Outcomes
The students will learn in a practical way the main numerical techniques used in diferent areas of science. Although they will learn the mathematical background of probability and statistics, the lectures will be based on practical examples. On successful completion of this module, students will be able to:
  • Analyse a numberical dataset (find the mean, variance,...);
  • Find a simple model to describe a given dataset;
  • Perform a chi^2 analysis to test a model on a given dataset;
  • Estimate the model parameters and their standard deviations;
Module Content
  • Probability;
  • Random numbers;
  • Modelling data;
  • Sorting data;
  • Markov process;
  • Poisson process
Module Prerequisite
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 and at supplementals where applicable.