数据分析与人工智能理学硕士

必修Core modules

• common univariate probability distributions
• joint and conditional distributions
• parameter estimation (for example via maximum likelihood)
• confidence intervals
• hypothesis testing
• 统计建模statistical modelling

选修Optional modules (autumn)

• core database and cloud technologies
• data acquisition and cleansing
• how to manipulate mass datasets (focusing on SQL, Hadoop)
• effective solutions to common data challenges (for example, missing data)
• handling geospatial and open data
• visualisation technologies (for example Tableau, PowerMap, QGIS, CartoDB)
• web visualisation (HTML5)
• modelling and analysis of biomedical systems using Ordinary Differential Equations
• phase-line and phase-plane analysis of models with one and two variables, with corresponding computational analysis
• fitting models to data
• modelling spatial systems using Partial Differential Equations and individual-based models

选修Optional modules (spring)

• multivariate regression methods
• classification methods, such as linear discriminant analysis
• multivariate normal distributions and their use in multivariate analysis of variance tests
• statistical software for conducting analysis of multivariate data
• Pharmacokinetics and Pharmacodynamics (PK-PD) modelling
• identifiability, sensitivity and model selection for PK-PD models
• statistical nonlinear mixed effects (NLME) modelling to account for patient variability when fitting data
• advanced partial differential equation models for space- and time-dependent drug delivery
• communicating the assumptions and outcomes of the modelling process with presentation of uncertainties
• approximation theory
• numerical solution of nonlinear problems
• numerical solution of ODEs
• numerical solution of PDEs