XX50215: Statistics for data science
[Page last updated: 24 May 2023]
Academic Year:  2023/24 
Owning Department/School:  Department of Computer Science 
Credits:  6 [equivalent to 12 CATS credits] 
Notional Study Hours:  120 
Level:  Masters UG & PG (FHEQ level 7) 
Period: 

Assessment Summary:  EX 100% 
Assessment Detail: 

Supplementary Assessment: 

Requisites:  
Learning Outcomes:  After completion of the unit, students should be able to:
* perform elementary mathematical operations in probability and statistics, * translate realworld problems into a probabilistic or statistical framework, * solve statistical problems in abstract form, * critically interpret outcomes in a realworld context, * relate underlying theory to requirements in practical data science. 
Aims:  Students should gain an understanding of the basic theory of probability and statistics. Students will recognise when this theory can be applied in practice. 
Skills:  Problem solving (T, F, A), computing (T, F, A), written communication (F, A) 
Content:  Topics covered will include: The laws of probability. Discrete and continuous random variables. Bayes' Theorem. Expectation, variance and correlation. Conditional and marginal distributions. Common distributions including the normal, binomial and Poisson. Statistical estimation including maximum likelihood. Hypothesis testing and confidence intervals. 
Programme availability: 
XX50215 is Optional on the following programmes:Department of Computer Science

Notes:
