Undergraduate Course: Data Literacy (SCIL07002)
Course Outline
| School | School of Social and Political Science |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 7 (Year 1 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Data Literacy is a skills-oriented course. The course will teach you how to:
- think logically and scientifically about data
- summarise and analyse data using Excel
- evaluate statistical claims made in scientific literature and the mass media
- use 'statistical imagination' to obtain accurate information about big populations from small samples
- use 'statistical imagination' to rationally change your beliefs as you encounter new evidence
- present data effectively using tables and graphics.
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| Course description |
The statistical analysis of data is one of the few tools we have that is capable of capturing the complexity of the world and giving us a perspective that goes beyond subjective impressions and anecdotal evidence. Data analysis skills are not only uniquely powerful but universally applicable. As society becomes more 'data-driven', these skills are becoming just as important as everyday reading and writing skills.
This Data Literacy course will teach you how to understand, analyse and communicate numerical evidence in your academic, professional and everyday life. This includes learning how to do basic statistical analysis with Excel; work out probabilities; assess and compare risks, visualize data and develop a sound judgment necessary to evaluate any statistical claims you may encounter in the media and in academic literature.
Data Literacy is not a conventional statistics course; instead, the emphasis throughout is on teaching you the fundamental ideas behind statistical reasoning and why statistics and data matter in the contemporary world. By the end of this course, you will have grasped the challenges, but also appreciated the power, of seeing the world through data.
This course is open to all students at the University of Edinburgh, regardless of degree subject or level of study. No previous experience with statistics is required.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | Students MUST NOT also be taking
Statistical Literacy (SCIL07001)
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Other requirements | THIS IS A REPLACEMENT COURSE FOR SCIL07001 STATISTICAL LITERACY. YOU CANNOT TAKE BOTH COURSES |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2021/22, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 22,
Seminar/Tutorial Hours 10,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
164 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
90 %,
Practical Exam
10 %
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| Additional Information (Assessment) |
Weekly online multiple choice assessment based on course reading (best 8 results from 10 weeks) 40%;
Tutorial participation 10%
Open book take home paper 50% |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- summarise and analyse data according to established scientific principles
- understand how data can inform description, analysis, understanding and decision-making
- understand the challenges of making valid and reliable measurements
- use basic probability rules to interpret sample data and to calculate prior and posterior probabilities
- present data effectively using tables and graphics.
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Reading List
1. Spiegelhalter, D. 2019. The Art of Statistics: Learning from Data. UK: Pelican.
2. Rosling, H. 2018. Factfulness. New York: Flatiron Books.
3. Harford, T. 2020. How to Make the World Add up. London: The Bridge Street Press.
4. Ellenberg, J. 2014. How Not to be Wrong: the Hidden Maths of Everyday Life. London: Allen Lane.
5. Gigerenzer, G. 2002. Reckoning with Risk. London: Allen Lane.
6. Kahneman, D. 2012. Thinking Fast and Slow. London: Penguin.
7. Hacking, I. 2001. An Introduction to Probability and Inductive Logic. New York, Cambridge: Cambridge University Press.
8. Wootton. D. 2016. The Invention of Science. London: Penguin Books. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Statistical Literacy,Data Literacy,Data |
Contacts
| Course organiser | Dr Plamena Panayotova
Tel:
Email: ppanayo2@exseed.ed.ac.uk |
Course secretary | Mr Daniel Jackson
Tel: (0131 6)50 8253
Email: Daniel.Jackson@ed.ac.uk |
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