MT5763 Software for Data Analysis

Academic year

2024 to 2025 Semester 1

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.

Planned timetable

12 noon Mon (even), Tue, Thu (lectures). 1pm Thu (practicals)

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

Module coordinator

Dr C M Fell

Dr C M Fell
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 the practical computing aspects of statistical data analysis, focussing on packages most widely used in the commercial sector (for example, R, Python, SAS, SPSS and Excel). We cover the accessing, manipulation, checking and presentation of data (visual and numerical). We fit various statistical models to data, with subsequent assessment, interpretation and presentation. Good practice and 'reproducible research' is covered, as is computer intensive inference and big data considerations. This module is a core, preliminary, requirement for the MSc in Applied Statistics and Datamining and the MSc in Data Intensive Analysis. It covers material essential for study of the more advanced statistical methods encountered in subsequent modules.

Relationship to other modules

Pre-requisites

STUDENTS MUST HAVE GAINED ADMISSION ONTO AN MSC PROGRAMME

Anti-requisites

YOU CANNOT TAKE THIS MODULE IF YOU TAKE MT4113

Assessment pattern

Coursework = 100%

Re-assessment

Coursework = 100%

Learning and teaching methods and delivery

Weekly contact

2.5 hours of lectures (x 10 weeks), 1-hour practical (x 10 weeks)

Scheduled learning hours

35

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

Guided independent study hours

115

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

Intended learning outcomes

  • understand the basics of programming, data wrangling and model fitting in suitable data science software, along with computer-intensive inference
  • understand and apply the principles of reproducible and collaborative research, built around the concepts of mark-down analysis documents, version control and github repositories
  • present data and models to lay and technical audiences in a variety of forms
  • develop basic interactive applications to provide GUIs for model fitting and data analysis
  • access and manipulate data from APIs or web-scraping