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) |
Assessment structure
- Quiz 1 (5%)
- Quiz 2 (5%)
- Written Assessment (40%)
- Data Analysis Report (50%)
Progression and resit requirements
- To pass the course, students must achieve a minimum overall mark of 40%.
- Students who do not pass the course will be offered the opportunity to resit in accordance with Taught Assessment Regulations.
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| Feedback |
Throughout the course, teaching staff support students in identifying gaps in their understanding, developing problem-solving strategies, and consolidating key skills. Students are encouraged to engage actively with feedback through reflection, discussion with peers, and comparison with worked examples.
Ongoing feedback is provided through a range of structured activities, including:
- Weekly tutorial sessions, where students work individually and in small groups on guided problem-solving and data analysis tasks, with immediate verbal feedback and clarification provided by tutors during the session.
- Formative Quizzes, multiple choice questions set-up on Learn. These quizzes provide automated feedback, and selected questions are reviewed during tutorials to address common misconceptions.
- Collaborative work during statistics tutorials, where students jointly develop components of a data analysis report and discuss interpretation of results.
- Model answers and worked solutions, released after tutorials, enabling students to compare their own work with exemplar approaches and reflect on areas for improvement.
- Formative coursework activities, designed to support skill development across the course and prepare students for summative assessment tasks.
- Online discussion spaces, where students can ask questions, share difficulties, and receive guidance from teaching staff and peers.
Feedback on summative assessments is provided in line with University regulations, highlighting strengths and areas for further development. Together, these feedback mechanisms ensure that students have regular opportunities to monitor their progress, refine their analytical skills, and build confidence in applying mathematical and statistical methods.
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| 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 Edinburghs online platform.
Recommended Reading:
- Alcorn, D. (2018) National 5 maths with answers, Second edition. London: Hodder Gibson. [Available in COL Resource Centre]
- [Book] Field, A. (2024). Discovering Statistics Using R statistics. Thousand Oaks: Sage Ltd. ISBN : 9781529668728 [available at the library] [eTextbook available]
- OpenIntro Statistics: freely available pdf - https://www.openintro.org/book/os/
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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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