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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2022/2023

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DRPS : Course Catalogue : School of Geosciences : Geography

Undergraduate Course: Data Science for Geographers (GEGR10130)

Course Outline
SchoolSchool of Geosciences CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryData Science for Geographers is one of a number of advanced research electives that prepare you to conduct an independent dissertation project and develop transferable skills for your future careers. It provides advanced training in quantitative data analysis through practical hands-on experience of working with different types of quantitative data (e.g. individual-level data and area-based data) and using a range of different analytical approaches. Statistical analysis will be completed in the widely used (including in industry) open-source software package known as 'R'. The course will also introduce students to all aspects of designing, planning and executing an independent research project.
Course description There has been a significant growth in the application of statistical analysis to geographical data over the last two decades on the back of significant advances in computing power and the capabilities of statistical and GIS software packages. In parallel, there is now a huge amount of secondary data available online from a range of sources that is ready to be used in student research projects. Building on Research Design in Geography and Quantitative Methods in Geography the course provides advanced training on all aspects of designing a project based on secondary numerical data including locating relevant data resources, managing, cleaning and preparing large datasets and conducting appropriate and critically informed statistical analyses. Techniques that are covered include multiple linear and generalised linear regression, cross-sectional models and longitudinal models and spatial approaches using GIS. The course will consist of directed and hands-on practical sessions providing experience of working with quantitative social and spatial data using a range of different analytical approaches. The course assessment provides an opportunity for students to deepen their engagement with a substantive conceptual issue in Human Geography, develop a central research question, and conduct appropriate statistical analyses. The ideas, initiative and energy for the research project must come from students, although they will receive guidance via lectures, workshops and hands-on practical sessions in weeks 1-5 (in a computer lab or teaching studio) and project development support in weeks 6, 7 and 8 of semester 1. The research elective will be of particular benefit for students doing dissertation research using statistical analysis.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2022/23, Not available to visiting students (SS1) Quota:  30
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 196 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) One 4000 word project report (100%)
Feedback Formative verbal feedback will be given during the weekly practical classes (2 per week). Students will also receive formative written and verbal feedback on a class assessment consisting of a 1000 word proposal for the degree assessment project. Feedback will be given on the summative assessment at the end of the course and all students will be invited to an examination feedback session following release of course results.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Design, plan and execute an independent research project that is appropriately framed by a critical understanding of theoretical and conceptual issues in Human Geography
  2. Have an understanding of the main analytical techniques for quantitative research in Geography including their application, a critical awareness of their strengths and weaknesses and how to interpret the results
  3. Demonstrate detailed knowledge and understanding of a substantive concern at the forefront or Human Geography
  4. The ability to use R (including statistical programming) to carry out data management and manipulation tasks, statistical modelling and to produce effective visualisations and presentation of data and results
  5. Have the ability to present quantitative research information to a variety of audiences and employ a range of writing and analytical skills for the original interpretation and presentation of research
Reading List
Clifford, N., French, S. and Valentine, G. (2010). Key Methods in Geography. London: Sage;
Cloke, P., Cook, I., Crang, P., Goodwin, M., Painter, J. and Philo, C. (2004). Practising Human Geography. London: Sage;
Flowerdew, R. and Martin, D. (2005). Methods in Human Geography: A guide for students doing a research project. Harlow: Pearson: Prentice Hall.
Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Sage publications.
Grolemund, G., & Wickham, H. (2018). R for data science.
Wickham, H. (2016). ggplot2: elegant graphics for data analysis. Springer.
Wheelan, C. (2013). Naked statistics: Stripping the dread from the data. WW Norton & Company.
Lane, D. ¿The Online Stats Book¿ (online at: http://onlinestatbook.com/)

Additional Information
Graduate Attributes and Skills Students will be able to: 1. work with autonomy to plan, design and execute an independent research project; 2. employ advanced statistical analysis techniques to analyse secondary data; 3. Become proficient in the use of the R statistical software platform.

KeywordsQuantitative analysis,numerical data,statistical modelling,data science,independent research
Contacts
Course organiserDr Thomas Clemens
Tel: (01316) 51 40 16
Email: Tom.Clemens@ed.ac.uk
Course secretaryMs Kathryn Will
Tel: (0131 6)50 2624
Email: Kath.Will@ed.ac.uk
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