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 | This course looks at numerical data of all kinds. It examines how data is produced, the different forms it can take, and how it can be analysed. It explains how such data can be used to correct cognitive biases in the way we see the world around us. It demonstrates how information from small samples can give us accurate information about much bigger populations. It shows how you can use Bayes rule to rationally change your beliefs as you encounter new evidence. |
| Course description |
Everyone, from scientists to doctors, lawyers or politicians, uses data to support the arguments they make. The contemporary world produces vast amounts of new data, doubling its volume every couple of years. That is one reason why Hal Varian, of Google, has declared that statistics will be the sexiest science of the 21st century! Most corporations now recruiting "data scientists" because they believe that how they learn from data will determine their success.
Yet most data gets used badly. Courts make poor decisions because lawyers don't understand the data. Doctors, journalists, civil servants can get it wrong too. Data literacy is about rules for logical thinking about evidence and imagination in applying them. These rules are straightforward to learn, but often counter-intuitive, because evolution has trained us to notice and rationalise whatever catches our attention.
Anyone can use these rules to judge the quality of evidence in anything from newspaper stories to scientific papers. Once mastered, the world becomes a more curious and interesting place.
The skills this course imparts are not only fundamental to logical and critical thinking, but also highly valued by employers as society becomes more "data driven".
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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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 2020/21, 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:
- Evaluate the use of numerical data by others, and to use and present it effectively themselves.
- Understand how data can inform description, analysis, understanding and decision making
- Appreciate the challenges of good measurement, and evaluate the quality of data and present data effectively using tables and graphics.
- Use basic probability rules to interpret sample data using confidence intervals and p-values.
- Use Bayes rule to calculate prior and posterior probabilities.
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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 | Miss Plamena Panayotova
Tel:
Email: ppanayo2@exseed.ed.ac.uk |
Course secretary | Mr Ewen Miller
Tel: (0131 6)50 3925
Email: Ewen.Miller@ed.ac.uk |
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