MT5764 Advanced Data Analysis

Academic year

2023 to 2024 Semester 2

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

Not automatically available to General Degree students

Planned timetable

Mon 12:00-1:00 Weeks 2, 4, 6, 8, 10 Tues; Thur 12:00-1:00, Weeks 1-10 (lectures); Mon 2:00 - 4:00 Weeks 2-9 (practicals)

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Dr C S Sutherland

Dr C S Sutherland
This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Dr Nicolò Margaritella

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

This module covers modern modelling methods for situations where the data fails to meet the assumptions of common statistical models and simple remedies do not suffice. This represents a lot of real world data. Methods covered include: nonlinear models; basic splines and Generalised Additive Models; LASSO and the Elastic Net; models for non-independent errors and random effects. Pragmatic data imputation is covered with associated issues. Computer intensive inference is considered throughout. Practical applications build sought-after skills in R and/or the commercial packages SAS.

Relationship to other modules

Pre-requisites

BEFORE TAKING THIS MODULE YOU MUST PASS MT3508 AND ( PASS MT4606 OR PASS MT5761 )

Anti-requisites

YOU CANNOT TAKE THIS MODULE IF YOU TAKE MT5757

Assessment pattern

2-hour Written Examination = 60%, Coursework = 40%

Re-assessment

Oral examination = 100%

Learning and teaching methods and delivery

Weekly contact

2.5 hours of lectures lectures (x 10 weeks) and 8 practicals over the semester.

Scheduled learning hours

41

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

108

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • Show how generalized linear models can be extended to accommodate correlated errors and nonlinear systematic relationships
  • Understand modern statistical modelling methods including LASSO, elastic net, generalized additive models, generalized estimating equations and random effects
  • Apply them to real-world data using R and/or SAS.
  • Validate model assumptions and select between models