SD5810 Welcome to Data: Rubbish in; rubbish out

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.

Availability restrictions

Available only to students on the PG Dip, or MSc in Data Literacy for Social and Environmental Justice

Planned timetable

Not Applicable

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

Module coordinator

Dr T C Mendo

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

Module Staff

Dr L Cole; Dr T Mendo; Dr E Olamijuwon

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 introduces students to the use of secondary data to understand social or environmental processes, using real datasets. It is designed to be appropriate for students who either have not had mathematics or statistics training recently and/or who have not used statistical software packages like spreadsheets or R/RStudio to explore quantitative data. Students will learn how to find secondary data; how to conceptually evaluate data quality; and basic concepts of exploratory data analysis, modelling, and projections (including confidence and uncertainty). This will primarily be a conceptual module, but it will introduce students to the basics of several open-source software options (e.g., R and spreadsheets options) to build confidence with quantitative data manipulation and provide a foundation for subsequent modules. This module will have two pathways, one in social science and one in environmental science.

Assessment pattern

100% Coursework

Re-assessment

100% Coursework

Learning and teaching methods and delivery

Weekly contact

This module includes 5 1-hour synchronous tutorial sessions and at least 5 hours of pre-recorded content (e.g., lectures). Students should consider the amount of independent study time this module involves when planning their learning.

Scheduled learning hours

0

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

Guided independent study hours

145

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

Intended learning outcomes

  • Locate and access data useful for questions of social and/or environmental justice;
  • Describe important steps in establishing data quality;
  • Understand how to approach identifying the benefits and limitations of a dataset;
  • Demonstrate a basic understanding of working with datasets (e.g., in spreadsheets and/or R/RStudio).

SD5810 Welcome to Data: Rubbish in; rubbish out

Academic year

2024 to 2025 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

Available only to students on the PG Dip, or MSc in Data Literacy for Social and Environmental Justice

Planned timetable

Not Applicable

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

Module coordinator

Dr T C Mendo

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

Module Staff

Dr L Cole; Dr T Mendo; Dr E Olamijuwon

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 introduces students to the use of secondary data to understand social or environmental processes, using real datasets. It is designed to be appropriate for students who either have not had mathematics or statistics training recently and/or who have not used statistical software packages like spreadsheets or R/RStudio to explore quantitative data. Students will learn how to find secondary data; how to conceptually evaluate data quality; and basic concepts of exploratory data analysis, modelling, and projections (including confidence and uncertainty). This will primarily be a conceptual module, but it will introduce students to the basics of several open-source software options (e.g., R and spreadsheets options) to build confidence with quantitative data manipulation and provide a foundation for subsequent modules. This module will have two pathways, one in social science and one in environmental science.

Assessment pattern

100% Coursework

Re-assessment

100% Coursework

Learning and teaching methods and delivery

Weekly contact

This module includes 5 1-hour synchronous tutorial sessions and at least 5 hours of pre-recorded content (e.g., lectures). Students should consider the amount of independent study time this module involves when planning their learning.

Scheduled learning hours

0

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

Guided independent study hours

145

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

Intended learning outcomes

  • Locate and access data useful for questions of social and/or environmental justice;
  • Describe important steps in establishing data quality;
  • Understand how to approach identifying the benefits and limitations of a dataset;
  • Demonstrate a basic understanding of working with datasets (e.g., in spreadsheets and/or R/RStudio).