Statistics & Data Analytics
Entry requirements
A level: AAB or BBB (Additional requirements: A-level Mathematics.) IB: 34 (6,5,5) (Additional requirements: HL Mathematics (Analysis & Approaches).) Scottish Higher: BBBB is the minimum requirement from S5 to be reviewed for an S6 offer Offers are not guaranteed to applicants who meet the minimum from S5 Typically offers will be made at AAAAA by end of S6. B at Advanced Higher is equivalent to A at Higher Additional requirements: Higher Mathematics and a Higher Science subject at AA. (AB may be considered). Scottish Higher (widening access): MD20 : BBBB (also other target groups*) MD40 : AABB* Additional requirements: Higher Mathematics and a Higher Science subject. Successful completion of Top-Up or one of our Summer Schools.
About this course
BSc/MSci in Statistics & Data Analytics, a mathematics and statistics degree focused on the collection, analysis, presentation, and interpretation of quantitative information. Core topics include Programming, Data Analysis, Statistics, Mathematical Modelling, Research Methods, and Artificial Intelligence & Machine Learning. The curriculum includes independent project work evaluated by external examiners. The first year covers introductory mathematics alongside optional introductory statistics modules. The second year progresses through core studies in probability, statistical methods, multivariable calculus, and linear algebra. The third year builds advanced competence via compulsory modules such as Statistical Inference, Regression Models, Statistical Programming in R and Python, Multivariate Methods, Stochastic Processes, Principles of Designed Experiments and Surveys, Data Skills & Consultancy, Communication Skills and Ethics, and Multivariate Statistics and Machine Learning 2. The fourth year features compulsory advanced study in probability and statistics, Bayesian methods, flexible regression, and data analysis alongside a statistics project, plus optional choices like Spatial Statistics, Time Series, Theoretical Foundations of Machine Learning and Deep Learning, Biostatistics, Functional Data Analysis, Mathematical Finance, Statistical Genetics, and Environmental and Ecological Statistics. The fifth year concludes with a research project and further specialised options including Deep Learning.
Modules
- Mathematics 1
- Statistics 1Y: Introduction to Statistics: Learning from Data
- Statistics 1Z: Data Modelling in Action
- Statistics 2R: Probability
- Statistics 2S: Statistical Methods, Models and Computing 1
- Mathematics 2A: Multivariable Calculus
- Mathematics 2B: Linear Algebra
- Statistics 2X: Probability II
- Statistics 2Y: Statistical Methods, Models and Computing 2
- Statistical Inference
- Regression Models
- Statistical Programming in R and Python
- Multivariate Methods
- Stochastic Processes
- Principles of Designed Experiments and Surveys
- Data Skills & Consultancy
- Communication Skills and Ethics
- Multivariate Statistics and Machine Learning 2
- Principles of Probability and Statistics
- Advanced Bayesian Methods
- Flexible Regression
- Advanced Data Analysis
- Statistics Project
- Spatial Statistics
- Time Series (Level H)
- 4H: Theoretical Foundations of Machine Learning and Deep Learning
- Biostatistics
- Functional Data Analysis
- 4H: Mathematical Finance
- Statistical Genetics
- Environmental and Ecological Statistics
- Statistics Research Project 5
- Spatial Statistics
- Time Series (Level H)
- 4H: Theoretical Foundations of Machine Learning and Deep Learning
- Deep Learning (M)
- Biostatistics
- Functional Data Analysis
- Statistical Genetics
- Environmental and Ecological Statistics (Level M)