CS5922 Research Methods in Data Science

Academic year

2024 to 2025 Flexible study

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

Available only to students studying the PG Cert/PG Dip/MSc in Data Science (Digital)

Module coordinator

Dr B Varghese

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

Module Staff

TBC: Module coordinator(s): Computer Science (cs5922.staff@st-andrews.ac.uk)

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 provides an introduction to Data Science research methods and to the types of skills necessary for the planning, data gathering, data analysis and dissemination stages of Data Science research. This module will help students to familiarise themselves with how academic data scientists evolve research projects from study design, through data collection and analyses, to publication and further dissemination. It will also help students with preparation for their dissertation module later in the programme.

Assessment pattern

Coursework = 100%

Re-assessment

Coursework = 100%

Learning and teaching methods and delivery

Weekly contact

Students should expect to engage in approximately six tutorials over the course of the module, which will be scheduled with an awareness of the pace at which they are progressing, rather than at a fixed time each week. Students should consider the amount of independent study time this module involves when planning their learning.

Scheduled learning hours

6

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

Guided independent study hours

148

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

Intended learning outcomes

  • Understand the governance and ethical issues underpinning data use
  • Be aware of concepts and options for study design and the precise formulation of research questions
  • Understand the relationship between methodological approach and study design
  • Have experience of the careful reporting of study methodologies and results
  • Understand the technical, scientific and ethical issues that underpin the production and maintenance of data science solutions for use by the research and wider public communities

CS5922 Research Methods in Data Science (15 credits)

Academic year

2024 to 2025 Full Year

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

Available only to students studying the PG Cert/PG Dip/MSc in Data Science (Digital)

Module coordinator

Dr B Varghese

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

Module Staff

TBC: Module coordinator(s): Computer Science (cs5922.staff@st-andrews.ac.uk)

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 provides an introduction to Data Science research methods and to the types of skills necessary for the planning, data gathering, data analysis and dissemination stages of Data Science research. This module will help students to familiarise themselves with how academic data scientists evolve research projects from study design, through data collection and analyses, to publication and further dissemination. It will also help students with preparation for their dissertation module later in the programme.

Assessment pattern

Coursework = 100%

Re-assessment

Coursework = 100%

Learning and teaching methods and delivery

Weekly contact

Students should expect to engage in approximately six tutorials over the course of the module, which will be scheduled with an awareness of the pace at which they are progressing, rather than at a fixed time each week. Students should consider the amount of independent study time this module involves when planning their learning.

Scheduled learning hours

6

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

Guided independent study hours

148

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

Intended learning outcomes

  • Understand the governance and ethical issues underpinning data use
  • Be aware of concepts and options for study design and the precise formulation of research questions
  • Understand the relationship between methodological approach and study design
  • Have experience of the careful reporting of study methodologies and results
  • Understand the technical, scientific and ethical issues that underpin the production and maintenance of data science solutions for use by the research and wider public communities