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

Postgraduate Course: Insights Through Data (fusion online) (EFIE11026)

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
SummaryThis course will introduce students to the practical aspects of data science and 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 analysing different data sets and interpreting the results.
Course description The course will give an overview of key aspects of data science traversing the data cycle from critical data acquisition toward fair data-based decision making. The course will provide an introduction to statistical analysis, and also modern machine learning methods and will discuss 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 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.

Taught sessions will include a mix of these activities: lectures, practical programming practices, discussions, peer feedback, and group work. The fusion sessions will be supported by providing further online material for independent learning and also optional drop-in sessions.

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
High Demand Course? Yes
Course Delivery Information
Academic year 2023/24, Not available to visiting students (SS1) Quota:  None
Course Start Semester 1
Course Start Date 18/09/2023
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, Other Study Hours 3, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 77 )
Additional Information (Learning and Teaching) Other Study: Scheduled Group-work Hours (hybrid online/on-campus) - 3
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Summative Assessment:

The course will be assessed by the following components:

1) Individual Programming Practical Tasks (40%)

A set of programming tasks is introduced during the practical sessions. Based on these tasks, students will individually work on given Jupyter notebooks which include programming to understand and analyse data and will write short reflections on the implications and limitations of the results. Three assessment notebooks are to be submitted during the semester. The notebooks will be marked via a combination of automated and manual marking.

2) Group Critical Data Analysing Project (60%)

Students will work in a group to analyse a data set by applying the methods covered in the course. The project will extend the work that was done in the programming practical sessions during the semester. The group project will be described in a report containing the description of the data, analyses, interpretations, and programming codes.
Feedback Feedback for the self-assessment automated online quizzes will be provided automatically and immediately upon completion of each quiz.
Informal feedback will be given during practical programming sessions, class activities, and drop-in sessions.

Feedback for the programming practical coursework tasks will be provided in time to be of use in subsequent assessments within the course.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Engage critically with the theory and practice of modern data science including data acquisition and data handling processes.
  2. Be able to work with Python programmes and to extend and develop indivdually the elementary programming skills acquired during the course.
  3. Understand the role of statistics to assess quantitative properties of data and the limitations of the statistical approach.
  4. Interpret the results of a quantitative analysis of a data set.
  5. Work well in a team to effectively analyse data within a specific scenario including goals and assumptions.
Reading List

Warne, R.T., 2017. Statistics for the social sciences. Cambridge University Press.

Spiegelhalter, D., 2019. The art of statistics: learning from data. Penguin UK.

Feng, A. and Wu, S., The Myth of the Impartial Machine. Parametric Press, no. 1, 2019.

Mu¿ller, A.C. and Guido, S., 2016. Introduction to Machine Learning with Python: a Guide for Data Scientists. Sebastopol, CA: O¿Reilly Media Inc.

Theobald, O., 2021. Machine Learning for Absolute Beginners: A Plain English Introduction. Independently published.
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).
KeywordsEFI,Edinburgh Futures Institute,Level 11,PGT,Data,Statistics
Course organiserDr Serveh Sharifi Far
Tel: (0131 6)50 5051
Course secretaryMr Lawrence East
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