Postgraduate Course: Insights Through Data (fusion online) (EFIE11026)
Course Outline
| School | Edinburgh Futures Institute |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
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 studying interpretations of data sets. |
| 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 toward fair data-based decision making. The course also discusses current developments of machine learning methods and their future impact on society.
Students will be introduced to programming data-processing methods that will help them gain a working knowledge of data science. They will work with exemplary datasets and discuss different interpretations which can be compared with qualitative insights covered for the most part elsewhere in the EFI core and programmes. Students will explore data 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 a time requirement comparable to 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 in the fusion sessions.
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 students acquiring skills develop an ethical framework. Students in 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 (e.g. as an elective while studying in other University of Edinburgh programmes) will be given recommended prior readings and be expected to investigate data ethics to ensure their work 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.
Edinburgh Futures Institute (EFI) - Online 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 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.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2022/23, Available to all students (SV1)
|
Quota: 25 |
| Course Start |
Semester 1 |
| Course Start Date |
|
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 4,
Seminar/Tutorial Hours 2,
Supervised Practical/Workshop/Studio Hours 5,
Online Activities 5,
Formative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
80 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
The course will be assessed by the following components:
1) 10% (Individual) quizzes on data analysis and data science:
Students are expected to engage with the course on a weekly basis. Each student They will answer a set of MCQs that do not require revision beyond exposure to the lecture videoslearning materials.
2) 30% (Individual) problem solving in programming practicals:
A set of programming tasks is introduced and prepared during the lab sessions. Based on these tasks, the students will individually modify given Jupyter notebooks which includes programming to solve computational problems and to write short reflections on the implications and limitations of the results. The notebooks are to be submitted shortly after midterm. The quantitative aspects are marked automatically, and the reflections are assessed by the lab demonstrator, finally both aspects will be moderated by the course lecturer.
3) 60% (Group) final group task in a critical data analysis project to be finished by the end of the semester.
The project will extend work that was done in the programming practicals. The group project will be presented by the group and described in a report containing the presented material and appendices with additional documentation, any program code, generated data, and links to other media used or produced in the group task. Students will obtain an individual grade for the group work. This will be calculated by using a combination of lecturer grade and peer marking. The lecturer assigned grade will be 80% of the mark, peer marking will make up the remaining 20%. The peer marking scheme will be done using 0-5 mark scoring on a number of criteria, with the summed peer mark being used to create a weighting to distribute the marks awarded for the peer marking component. |
| Feedback |
Feedback for the quizzes and automated testing will be provided automatically and immediately upon completion of each test or quiz component. In addition feedback on performance relative to earlier components will be given.
Feedback will be provided during the tutorials and upon presentation of the solution by tutor/demonstrator.
Feedback on the group task will be given verbally at demonstration to the teaching staff. Teaching staff will give non-individual formative feedback on the reports. |
| No Exam Information |
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.
|
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,Statistics |
Contacts
| Course organiser | Dr Michael Herrmann
Tel: (0131 6)51 7177
Email: Michael.Herrmann@ed.ac.uk |
Course secretary | Mr Lawrence East
Tel:
Email: Lawrence.East@ed.ac.uk |
|
|
University Homepage