Postgraduate Course: Insights Through Data (EFIE11556)
Course Outline
| School | Edinburgh Futures Institute |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | *EFI Skills and Methods Suite*
Please Note:
This course is only available to students enrolled on one of Edinburgh Futures Institute's postgraduate programmes.
This course will introduce students to the practical aspects of data science. 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 provides a practical overview of key aspects of data science, traversing the data cycle towards data-based decision making. It mainly focuses on quantitative data and introduces some fundamental statistical methods (e.g., linear regression) alongside selected modern machine learning approaches (e.g., classification).
Students will be introduced to programming in Python, enabling them to develop a working knowledge of data science tools and practices. They will work with exemplary and real-world datasets drawn from a variety of application areas. This offers opportunities to discuss and compare quantitative findings with qualitative insights covered elsewhere in the EFI core and programmes.
Students will explore working with data through different analysis approaches and methods, supported by notebook-based computer worksheets in practical programming workshops. For the group project, students will work in teams on real-world datasets from different fields (e.g., healthcare, education, finance) to analyse data, interpret results, and discuss limitations, hidden biases, and broader societal impacts.
Taught sessions will include a mix of lectures, practical programming activities, discussions, peer feedback, and group work.
Edinburgh Futures Institute (EFI) - Hybrid Course Delivery Information:
The Edinburgh Futures Institute delivers many of its courses in hybrid mode. This means that you may have some online students joining sessions for this course. 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: in some cases, students might not be able to sit in areas away from microphones or outside the field of view of all cameras.
- All presentations, and whole class discussions will be recorded (see the Lecture Recording and Virtual Classroom policies for more details).
You will need access to a personal computing device for this course. Most activities will take place in a web browser, unless otherwise stated. We recommend using a device with a screen, a physical keyboard, and internet access.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
|
| Academic year 2026/27, Not available to visiting students (SS1)
|
Quota: 100 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 10,
Supervised Practical/Workshop/Studio Hours 10,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
176 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
The course will be assessed by means of the following components:
1) Individual Programming Practical Assignment (40%)
Students will individually work on a given Jupyter notebook which includes programming to understand and analyse data and will write code and short reflections on the implications and limitations of the results.
2) Group Critical Data Analysis Project (60%)
Students will work in a group to analyse a dataset 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 on any formative assessment may be provided in various formats, for example, to include written, oral, video, face-to-face, whole class, or individual. The Course Organiser will decide which format is most appropriate in relation to the nature of the assessment.
Feedback on both formative and summative in-course assessed work will be provided in time to be of use in subsequent assessments within the course.
Feedback on the summative assessment(s) will be provided in written form via Learn, the University of Edinburgh's Virtual Learning Environment (VLE).
Formative Feedback Opportunity:
Formative feedback is ongoing feedback which monitors learning and is intended to improve performance in the same course, in future courses, and also beyond study.
Feedback on the assessed works will be provided in written form. Feedback on the formative assessment may be provided automatically and immediately upon completion of a quiz or in written form to the whole class.
Informal feedback will be given during practical programming sessions and class activities. |
| 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 through the analysis of exemplary and real-world data sets.
- Individually apply and extend elementary Python programming skills to develop data science workflows.
- Apply statistical and machine learning methods and evaluate their role in assessing quantitative properties of data, including their limitations.
- Interpret and communicate the results of quantitative analyses of data.
- Collaborate effectively within a team to carry out a data analysis project in a defined context.
|
Reading List
Recommended:
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.
Muller, 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 |
Not entered |
| Keywords | EFI,Edinburgh Futures Institute,Level 11,PGT,Data,Data Programming,Statistics |
Contacts
| Course organiser | Dr Serveh Sharifi Far
Tel: (0131 6)50 5051
Email: Serveh.Sharifi@ed.ac.uk |
Course secretary | Miss Abby Gleave
Tel: (0131 6)51 1337
Email: abby.gleave@ed.ac.uk |
|
|
University Homepage