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 degree programmes in the College of Arts, Humanities, and Social Sciences, where a foundation in mathematics, statistics, and data literacy is increasingly essential. It aims to build confidence with quantitative concepts while developing the skills required to engage critically with data in academic contexts.
The course bridges the gap between students¿ existing knowledge and the mathematical, statistical, and data analysis demands they will encounter at undergraduate level. Through a balance of theory and practical application, you will consolidate core mathematical foundations and develop key statistical and data analysis skills. Topics include algebra and functions, introductory probability, descriptive and inferential statistics, data visualisation, and hypothesis testing, with attention to issues such as secondary data use and introductory considerations of bias in data analysis.
Learning takes place through a combination of lectures and tutorials. Lectures introduce key mathematical, statistical, and data analysis concepts and provide a conceptual framework for understanding quantitative methods. Tutorials are interactive, practice-based sessions where you apply these concepts through problem-solving, discussion, and hands-on data analysis activities, working both individually and in small groups.
You will gain practical experience using digital tools and software applications for data manipulation, analysis, visualisation, and interpretation, supporting applications across the social sciences and humanities. Course materials, datasets, and guidance are provided through an online learning platform, which also supports formative coursework activities.
Throughout the course, you will receive regular feedback through tutorials and written formative tasks, enabling you to reflect on your progress and consolidate your understanding. Collectively, the course develops your quantitative problem-solving skills, your ability to interpret and communicate data effectively, and your confidence in applying mathematics and statistics in your future academic work.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2026/27, Not available to visiting students (SS1)
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Quota: 65 |
| 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 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Quiz 1 (5%) Assesses core mathematical concepts and problem-solving skills developed during the Mathematics block.
Quiz 2 (5%) Assesses core mathematical concepts and problem-solving skills developed during the Mathematics block.
Written Assessment (40%) Assesses core mathematical concepts and problem-solving skills developed during the Mathematics block.
Data Analysis Report (50%) This is a single summative coursework assessment completed across three structured stages, with only the final report (Stage 3) formally graded. Stages 1 and 2 are completed during scheduled tutorial sessions in Weeks 14¿16 under supervised, time-limited conditions, so that students can demonstrate their own statistical understanding and analytical skills and to support academic integrity (including limiting the opportunity to use generative AI tools). Students work in the same R Markdown file throughout, and each stage builds directly on the previous one.
To pass the course, students must achieve a minimum of 40% overall, meeting all Learning Outcomes. |
| 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 core mathematical principles, including algebra, functions, and introductory calculus, to solve quantitative problems relevant to the arts, humanities, and social sciences.
- Describe and interpret data using appropriate statistical techniques, including summary measures, visualisation, and basic inferential methods.
- Apply mathematical and statistical reasoning to real-world datasets, selecting appropriate methods to address defined analytical questions.
- Use digital tools and software to manipulate, analyse, and visualise data accurately and efficiently.
- Communicate quantitative findings clearly and effectively, using written explanation, tables, and data visualisations appropriate for an academic audience.
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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 | Miss Javiera Alfaro Chat
Tel:
Email: jalfaro3@exseed.ed.ac.uk |
Course secretary | Mr James Cooper
Tel: (0131 6)50 4400
Email: jcooper6@ed.ac.uk |
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