Postgraduate Course: Insights Through Data (fusion on-site) (EFIE11025)
Course Outline
| School | Edinburgh Futures Institute |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course will introduce students to practical aspects of data science and to the impact of modern AI. It will provide a basic background in data science skills and methodologies via direct interaction with data through programming, statistics and machine learning. The goal of the course is to facilitate a spectrum of insights by testing claims that have been made for data sets or by exploring relationships with data sets and processes. |
| Course description |
The course will give an overview of key aspects of data science traversing the data cycle from critical data acquisition via effective data maintenance and processing towards fair data-based decision making. The course discusses current developments of machine learning methods including deep neural networks and their future impact on the society.
Students will be introduced to programming data-processing methods that will help them gain a working knowledge of data science. They will acquire a dataset based on a number of options provided, and discuss different quantitative interpretations which can be compared with qualitative insights covered for the most part elsewhere in the EFI core and programmes. Students will explore their dataset through different analysis paradigms and methods, supported by notebook-based computer worksheets. Working in groups, they will use various data processing and representation methods. The results will be discussed based on current insights in data process theories, and students will explore hidden biases and potential societal impacts.
Each of the five sessions will include a lecture and discussion, a demonstration tutorial and lab sessions for programming practice in small groups. All components require preparation and wrap-up with about the same time requirement as the sessions, not including the coursework. The programming component will run through all sessions, while other aspects of data science (data management, data analytics, machine learning, and the implications of big data) will be discussed one after the other in weeks 2, 4, 6, and 8 (or one session later).
The experience for students taking the course online will be comparable with that on-campus as students will engage on a weekly basis enabled by the fusion model.
It is important that student studying data skills and methods develop an ethical framework. Students on EFI programmes will be able to develop this framework through the compulsory shared core course 'Ethical Data Futures' in Semester two. Other students taking this course (eg as an elective which studying on other University of Edinburgh programmes) will be given recommended prior readings and be expected to take an online questionnaire on data ethics to ensure the work on this course is appropriately framed.
Edinburgh Futures Institute (EFI) - On-Site Fusion Course Delivery Information:
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 be aware that:
- Classrooms used in this course will have additional technology in place: students might not be able to sit in areas away from microphones or outside the field of view of all cameras.
- Unless the lecturer or tutor indicates otherwise you should assume the session is being recorded.
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.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- Engage critically with the theory and practice of modern data science including data acquisition and data handling processes.
- Be able to work with Python programmes and to extend and develop indivdually the elementary programming skills acquired during the course.
- Understand the role of statistics to assess quantitative properties of data and the limitations of the statistical approach.
- Interpret the results of a quantitative analysis of a data set.
- Work well in a team to effectively analyse data within a specific scenario including goals and assumptions.
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Reading List
Reading will include web-resources including tutorials, documentation and opinion, such as https://www.scipy.org/
Other readings:
Colman, F., Bühlmann, V., O'Donnell, A. and van der Tuin, I. (2018). Ethics of Coding: A Report on the Algorithmic Condition [EoC]. H2020-EU.2.1.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies. Brussels: European Commission.
Feng, Alice, and Shuyan Wu. 'The Myth of the Impartial Machine'. Parametric Press, no. 1, Spring 2019.
Vertesi, J. and Ribes, D., 2019. DigitalSTS: a field guide for science & technology studies. Princeton University Press.
Warne, R.T., 2017. Statistics for the social sciences. Cambridge University Press.
Spiegelhalter, D., 2019. The art of statistics: learning from data. Penguin UK. |
Additional Information
| Graduate Attributes and Skills |
By identifying key issues and creatively tackling problems, students will develop key understanding, applied knowledge and generic cognitive skills (SCQF characteristics 1, 2 and 3).
By carrying out practical exercise they will develop key skills of data analysis (SCQF characteristic 4), and by pursuing their interests, opportunities for learning and presenting their work to peers they will exercise autonomy, and develop communication and accountability (SCQF characteristics 4 and 5). |
| Keywords | EFI,Edinburgh Futures Institute,Level 11,PGT,Data,Data Programming,Statistics |
Contacts
| Course organiser | Dr Michael Herrmann
Tel: (0131 6)51 7177
Email: Michael.Herrmann@ed.ac.uk |
Course secretary | |
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