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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2022/2023

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DRPS : Course Catalogue : School of Social and Political Science : Sociology

Undergraduate Course: Data Literacy (SCIL07002)

Course Outline
SchoolSchool of Social and Political Science CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 7 (Year 1 Undergraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryData 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.
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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Students MUST NOT also be taking Statistical Literacy (SCIL07001)
Other requirements THIS IS A REPLACEMENT COURSE FOR SCIL07001 STATISTICAL LITERACY. YOU CANNOT TAKE BOTH COURSES
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2022/23, Available to all students (SV1) Quota:  0
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 )
Assessment (Further Info) Written Exam 0 %, Coursework 90 %, Practical Exam 10 %
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:
  1. summarise and analyse data according to established scientific principles
  2. understand how data can inform description, analysis, understanding and decision-making
  3. understand the challenges of making valid and reliable measurements
  4. use basic probability rules to interpret sample data and to calculate prior and posterior probabilities
  5. present data effectively using tables and graphics.
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
KeywordsStatistical Literacy,Data Literacy,Data
Contacts
Course organiser Course secretaryMr Daniel Jackson
Tel: (0131 6)50 8253
Email: Daniel.Jackson@ed.ac.uk
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