Postgraduate Course: Data Science and Statistics for Osteoarchaeology (PGHC11624)
Course Outline
| School | School of History, Classics and Archaeology |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | "It's easy to lie with statistics. It's hard to tell the truth without statistics." This course introduces students to the world of data science giving them hands on experiences with the R software environment with an emphasis on data visualization. It critically engages with how we can use data to (de-)construct narratives about the human past with a particular emphasis on osteoarchaeological case studies. |
| Course description |
The ability to turn the incomplete remnants of past lives -- be those artefacts, words on a page, or bodies -- into meaningful information about social, religion, economic or other behaviours is at the core of what we do as archaeologists and historians. This is also the aim of data science -- to gain insights from 'messy' real world data. This course will stand students in good stead to explore further study or work in data-driven fields.
In this course you will learn how to describe, explore, compare and visualize data in R; and how to communicate your findings both visually and in writing. This course is primarily a 'learn by doing' format; the lectures will give you an introduction to statistical methods and graphics in archaeology and adjacent fields, while the practicals will familiarise you with the R statistical environment and problem solving within it. The practicals are geared towards human anatomical and biomolecular datasets to give you a solid foundation for routine data analysis in human osteoarchaeological contexts. Additional guest seminars will also introduce you to exciting applications of data science in research and industry. No prior familiarity with mathematics or coding is assumed, and all concepts will be introduced using archaeological and/or historical examples.
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Course Delivery Information
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| Academic year 2025/26, Not available to visiting students (SS1)
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Quota: 0 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 10,
Seminar/Tutorial Hours 5,
Supervised Practical/Workshop/Studio Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
161 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Coursework:
Lab portfolio (20%)
5,000 word Group research assignment (80%) |
| Feedback |
The lab portfolio will provide assessed work that will generate meaningful early and mid-semester feedback giving students plenty of time before the end of semester submission of their final long-form group assignment.
Students are expected to discuss their coursework with the Course Organiser at least once prior to submission, and are encouraged to do so more often. Meetings can take place with the Course Organiser during their published office hours or by appointment. Students will also receive feedback on their coursework, and will have the opportunity to discuss that feedback further with the Course Organiser. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate the ability to design and implement meaningful visualizations and appropriate statistical techniques to various archaeological and anatomical data sets;
- Demonstrate the ability to recognise and interpret meaningful patterns in data using appropriate data science techniques;
- Critically evaluate data science methods used in a wide range of scientific publications, especially when linked to social behaviour and biology and, where appropriate, propose alternative methods and hypotheses;
- Demonstrate a practical understanding of how to organize and manage data for analysis and reporting using best practice methods;
- Demonstrate independence of mind and initiative; intellectual integrity and maturity; an ability to evaluate the work of others, including peers.
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Reading List
Baxter, Mike, and Hilary Cool. Basic Statistical Graphics for Archaeology with R: Life Beyond Excel. Nottingham: Barbican Research Associates Nottingham, 2016.
Chang, Winston. R Graphics Cookbook: Practical Recipes for Visualizing Data. Second edition. Sebastopol, CA: O'Reilly, 2018.
Field, Andy, Jeremy Miles, and Zoe Field. Discovering Statistics Using R. 1st edition. Los Angeles, CA, USA: SAGE Publications Ltd, 2012.
Matejka, Justin, and George Fitzmaurice. 'Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing'. Autodesk Research (blog), 2017. https://www.autodeskresearch.com/publications/samestats.
'R: The R Project for Statistical Computing'. https://www.r-project.org/.
'RStudio Desktop'. https://posit.co/download/rstudio-desktop/.
Wasserstein, Ronald L., and Nicole A. Lazar. 'The ASA Statement on P-Values: Context, Process, and Purpose'. The American Statistician 70, no. 2 (2 April 2016): 129-33. https://doi.org/10.1080/00031305.2016.1154108.
Wasserstein, Ronald L., Allen L. Schirm, and Nicole A. Lazar. 'Moving to a World Beyond "p « 0.05"'. The American Statistician 73, no. sup1 (29 March 2019): 1-19. https://doi.org/10.1080/00031305.2019.1583913.
Weissgerber Tracey L., Winham Stacey J., Heinzen Ethan P., Milin-Lazovic Jelena S., Garcia-Valencia Oscar, Bukumiric Zoran, Savic Marko D., Garovic Vesna D., and Milic Natasa M. 'Reveal, Don't Conceal'. Circulation 140, no. 18 (29 October 2019): 1506-18. https://doi.org/10.1161/CIRCULATIONAHA.118.037777.
Wickham, Hadley, Mine Cetinkaya-Rundel, and Garrett Grolemund. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. 2nd edition. Beijing ; Sebastopol, CA: O'Reilly Media, 2023. https://r4ds.hadley.nz/. |
Additional Information
| Graduate Attributes and Skills |
On successful completion of the course, students should be able to:
- gather, integrate and critically assess relevant information
- extract key elements and meanings from complex data sets
- answer a research question by developing a reasoned argument based on quantitative analysis
- present their ideas and analyses in a coherent fashion |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Sam Leggett
Tel:
Email: Sam.Leggett@ed.ac.uk |
Course secretary | |
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