THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2022/2023

Timetable information in the Course Catalogue may be subject to change.

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DRPS : Course Catalogue : Edinburgh Futures Institute : Edinburgh Futures Institute

Postgraduate Course: Data Science for Society (fusion online) (EFIE11019)

Course Outline
SchoolEdinburgh Futures Institute CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate)
Course typeOnline Distance Learning AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryTake part in a hands-on exploration of diverse real-life data sets, and discover how to relate, visualize, and analyze them to understand and explore problems of inequality in society. In this intensive course you will spend two days in small teams exploring an unseen data set, correlating and comparing it with others, and learning to present your findings.
Course description This course is taught over an intensive 2-day block, with some structured activity before and after the intensive.

In the period before the intensive phase, students will learn through pre-recorded lectures, tutorials, and (partly assessed) interactive notebooks, the tools they will need to explore numerical data sets. Students should already have studied essential python, including numerical python (numpy).

In the intensive phase, students will be assigned to teams and given a (previously unseen) data set relevant to inequality, health, society, or environment. They will be given a set of core questions to explore using the data, but also be encouraged to move beyond this and learn whatever they can from the data, including by correlate locations with other sources. Finally, they will demonstrate their discoveries both as a 15 minute team presentation and later as a written report. For example, a team might be given a data set containing the locations of all the food banks in the UK. They could work to visualize this geographically, derive the distribution of distances of the population from their nearest bank, or cross-correlate with indices of deprivation to determine possible sites for new banks.

The Edinburgh Futures Institute will teach this course in a way that enables online and on-campus students to study together. This approach (our 'fusion' teaching model) offers students flexible and inclusive ways to study, and the ability to choose whether to be on-campus or online at the level of the individual course. It also opens up ways for diverse groups of students to study together regardless of geographical location. To enable this, the course will use technologies to record and live-stream student and staff participation during their teaching and learning activities. Students should note that their interactions may be recorded and live-streamed. There will, however, be options to control whether or not your video and audio are enabled.

As part of your course, you will need access to a personal computing device. Unless otherwise stated activities will be web browser based and as a minimum we recommend a device with a physical keyboard and screen that can access the internet.

Students not studying on EFI PGT programmes, and students taking this course on a CPD basis, will need a course teaching them essential python programming, such as EFI course Text Mining for Social Research.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements To take this course you should have previously studied (either in a formal course or otherwise) the Python programming language at least to a beginner's level. You should ideally have done at least some numerical programming, with Numerical Python (numpy) or a similar package. The core EFI course 'Insights Through Data' is an ideal preparation, but is not required if you have other similar experience.
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2022/23, Available to all students (SV1) Quota:  10
Course Start Semester 2
Course Start Date 16/01/2023
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 4, Seminar/Tutorial Hours 4, Supervised Practical/Workshop/Studio Hours 14, Summative Assessment Hours 2, Other Study Hours 18, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 56 )
Additional Information (Learning and Teaching) Other Study: Scheduled Group-work Hours (hybrid online/on-campus) - 8
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Feedback Pre-intensive: Students may attend a tutorial on the jupyter notebooks and how to use them, to ensure they are prepared for the intensive phase. A second tutorial will cover presentation skills.

Intensive: The intensive phase will have continuous feedback. The presentation will receive instant feedback from the organizer designed to help them write their post-intensive reports.

Post-intensive: A tutorial/seminar in the post phase will cover writing up.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Quickly load, explore, and visualize an unseen numerical data set using one of several tools.
  2. Combine multiple data sets effectively to gain more insights.
  3. Present preliminary findings in a careful but persuasive and/or informative way.
  4. Work as part of a team thoughtfully dividing responsibilities in a shared data exploration context.
  5. Understand the limitations of an exploratory data analysis and consider how it could be improved.
Reading List
Resources for learning the data analysis tools required are all available online.
Additional Information
Graduate Attributes and Skills Verbal communication and presentation
Independent learning and development
Knowledge integration and application
KeywordsData Analysis,Social data,Hackathon
Contacts
Course organiserDr Joseph Zuntz
Tel: (0131 6)68 8262
Email: joe.zuntz@ed.ac.uk
Course secretaryMr Lawrence East
Tel:
Email: Lawrence.East@ed.ac.uk
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