Probability rules and Bayes' Theorem.
An introduction to integration.
Standard statistical distributions such as Binomial, Poisson, Normal and Exponential.
Hypothesis testing including goodness of fit.
Linear correlation and regression.
An introduction to an appropriate statistical software package

Big Data And Analytics
Data Collection, Sampling and Pre-processing
Predictive Analytics
Descriptive Analytics
Survival Analytics
Social Network Analytics

100% coursework assessing all learning outcomes

A portfolio consisting of two equally weighted tests (1 hr 30 mins each) and a mathematical based assignment (equivalent to 2500 words)

The module uses formal lectures, and workshop based teaching which will include practical work, seminars and theoretical material. Extensive use is made of the VLE and of formative assessment.
Core theoretical material will be delivered through lectures (36 hours), while tutorials and computer laboratory sessions (36 hours) will give opportunities to apply techniques to a variety of practical problems.
Semester 1 - 12 hours lectures and 24 hours practicals
Semester 2 - 24 hours lectures and 12 hours practicals

Grinstead & Snell. 2012, Introduction to Probability, American Mathematical Society, ISBN-10: 0821894145 & ISBN-13: 978-0821894149

Sheldon M. Ross, 2014, Probability Models, Academic Press, ISBN-10: 0124079482 & ISBN-13: 978-0124079489

Analytics in a Big Data World: The Essential Guide to Data Science and its Applications, Bart Baesens, 2014 , Wiley, ISBN-10: 1118892704

Ugarte, Militino and Arnholt, 2015, Probability and Statistics with R, Chapman and Hall/CRC, ISBN-10: 1466504390 & ISBN-13: 978-1466504394

None.

None.

1. DEMONSTRATE AN UNDERSTANDING OF THE CONCEPTS OF PROBABILITY.
Knowledge & Understanding

2. RECOGNISE WHERE THE USE OF CERTAIN STANDARD PROBABILITY DISTRIBUTIONS WOULD BE APPROPRIATE AND USE THEM IN CALCULATING PROBABILITIES.
Problem Solving

3. APPLY APPROPRIATE STATISTICAL METHODS SUCH AS CORRELATION, REGRESSION, AND GOODNESS OF FIT TO ANALYSE DATA.
Application

4. INTERPRET RESULTS OBTAINED FROM THE ANALYSIS OF DATA AND ASSESS THE FIT OF DATA TO PROBABILITY DISTRIBUTIONS.
Analysis

5. DEMONSTRATE KNOWLEDGE OF FUNDAMENTAL AND ANALYTICAL CONCEPTS AND PROCESSES THAT ARE APPLICABLE TO DATA SCIENCE.
Knowledge & Understanding

6. APPLY APPROPRIATE METHODOLOGIES AND SUITABLE MATHEMATICAL OR ANALYTIC TECHNIQUES TO OBTAIN SOLUTIONS FOR DATA SCIENCE PROBLEMS.
Analysis, Application