Undergraduate Course: Mathematics, Statistics and Data Analysis for Arts, Humanities and Social Sciences (FNDN07012)
Course Outline
| School | Centre for Open Learning |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 7 (Year 1 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course will build on your existing mathematics skills and introduce you to data and statistical analysis. You will learn to explore, visualise, and analyse data to understand the world through quantitative methods, applying mathematical techniques to real-world contexts. |
| Course description |
This course is designed for students progressing to degrees in the College of Arts, Humanities, and Social Sciences, where a foundation in mathematics, statistics, and data literacy is essential.
Through lectures and tutorials, you will develop core skills in mathematics, statistics, data manipulation and analysis using a variety of software applications. You will be introduced to fundamental data analysis principles, including descriptive statistics, secondary data sources, and bias considerations.
This course bridges the gap between existing knowledge and undergraduate-level mathematics, statistics, and data analysis that students in arts, humanities, and social sciences will encounter in their degree programmes.
Balancing theory and practical application, the course emphasises problem-solving and critical thinking skills, essential for academic research. Through lectures and tutorials, you will consolidate mathematical foundations, develop statistical and data science skills, and learn to apply these concepts to real-world data.
Practical skills include using digital tools for data analysis, visualisation, and interpretation, supporting applications in the social sciences and humanities.
You will engage in a diverse and interactive learning experience combining lectures and tutorials. Lectures introduce key mathematical, statistical, and data analysis concepts, while tutorials offer opportunities for problem-solving, discussion, and hands-on practice in small groups.
You will gain practical experience using a variety of software applications for data manipulation, analysis and visualisation.
An online platform provides access to course materials and regular formative assessments, including quizzes with instant automated feedback and detailed teacher feedback throughout the course.
The course culminates in a data analysis project, where you will work with a provided dataset to formulate a research question, conduct a mini literature review, perform statistical tests, and report findings.
If you require additional support, MathsHub offers regular study sessions, where you can have supported study and/or book a slot to speak with a course teacher(s) about a specific issue you are having. This bespoke, individualised support ensures that you, if needed, receive comprehensive assistance in all aspects of the course.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
|
| Academic year 2025/26, Not available to visiting students (SS1)
|
Quota: 90 |
| Course Start |
Flexible |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Seminar/Tutorial Hours 64,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
132 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Written Assessment (40%) - LO1 , LO2, LO3, LO4
Project (60%)- LO1 , LO2, LO3, LO4
To pass the course, students must achieve a minimum of 40% overall, meeting all Learning Outcomes.
Students who do not pass the course will be offered the opportunity to resit in accordance with Taught Assessment Regulations.
The mark needed for progression to undergraduate study is normally 60%, however, this may vary depending on the receiving programme. |
| Feedback |
Throughout the course, teaching staff will support you to identify the gaps in your skills and learning, and your strengths. You will be encouraged to engage with feedback through personal reflection and discussion with peers.¿¿¿
You will receive ongoing feedback through:¿
Weekly tutorial sessions with immediate feedback on problem-solving approaches.¿
Regular online quizzes with automated feedback and opportunities to try similar questions multiple times.¿
Detailed written feedback on the data analysis project, including data handling, statistical methods, and reporting.
One-on-one feedback with teachers during tutorials and MathsHub sessions.¿
Peer feedback during group work.¿
Comprehensive feedback on the final written assessment, highlighting areas of strength and areas for further development.¿
This multi-layered feedback system ensures that you have multiple opportunities to track your progress and refine your analytical skills throughout the course.¿ |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Apply fundamental mathematical principles¿including algebra and calculus¿to analyse and solve complex problems
- Utilise essential statistical techniques to describe, analyse, and interpret data
- Apply mathematical and statistical concepts to solve real-world problems
- Communicate data insights effectively using written reports and data visualisations
|
Reading List
There is no core textbook. All the learning materials will be provided through the University of Edinburgh¿s online platform.
Recommended Reading:
Alcorn, D. (2018) National 5 maths with answers, Second edition. London: Hodder Gibson. [Available in COL Resource Centre]
OpenIntro Statistics: freely available pdf - https://www.openintro.org/book/os/
Field, A. (2012). Discovering Statistics Using R . London: Sage Ltd. ISBN : 9781446258460 [available at the library] |
Additional Information
| Graduate Attributes and Skills |
You will develop graduate, personal and professional skills in mindset and skills:
Mindset:
You will be encouraged to develop a reflective approach on your knowledge and skills and identify ways for improvement and growth.
You are encouraged to adopt an inquiring mindset and develop an appreciation of the importance of mathematics and data analysis.
You will build confidence in applying mathematical and statistical concepts to real-world problems.
You will engage with diverse data sources and research methodologies to develop a global perspective of the role of mathematics and data science.
Skills:
You will build personal and intellectual autonomy in approaching mathematical and statistical challenges.
You will develop teamwork skills through collaborative group work.
You will develop strong communication skills in presenting ideas clearly and concisely. |
| Keywords | Not entered |
Contacts
| Course organiser | Ms Liz MacDougall
Tel:
Email: emacdou3@ed.ac.uk |
Course secretary | Mr James Cooper
Tel: (0131 6)50 4400
Email: jcooper6@ed.ac.uk |
|
|
University Homepage