Undergraduate Course: Data Analysis for Business 2 (BUST08061)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 8 (Year 2 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course further develops students' business research skills through a strong focus on statistics and data, and their role in informing effective business decisions. It demonstrates how data collection and statistical analysis underpin sound managerial and strategic choices. The course builds directly on key concepts introduced in Data Analysis for Business 1, extending students' knowledge of quantitative methods, data analysis, and statistical reasoning in business contexts. These concepts enable students to engage more confidently in quantitative research and to critically evaluate academic and industry evidence.
Students will learn practical techniques for collecting, coding, analysing, and synthesising data from a variety of sources. The course emphasises the interpretation of statistical results and the use of quantitative evidence to support business decision-making. It provides hands-on experience in generating and analysing quantitative datasets, enabling students to further develop practical data analysis skills and apply statistical techniques to real business problems. Data analysis techniques for this course will be supported with R. |
| Course description |
This course is designed to further develop student skills relating to data and statistical analysis of business data. Students will work with a range of data sources, and develop practical skills in synthesising evidence to generate meaningful business insights. The course provides hands-on experience in collecting and analysing data, enabling students to apply analytical techniques to real-world business questions.
Specific topic coverage may vary year-on-year, but a typical topic outline is provided below
-Tests of Goodness of Fit
-Experimental Design
-Simple Linear Regression
-Multiple Regression
-Model Building
-Time Series Analysis and Forecasting
-Non-Parametric Methods
-Recent Advances in Business Data Analytics
Student learning experience:
The course aims to support students in developing the skills and capabilities to undertake independent research and become critical thinkers, starting from reviewing scholarly literature, problematising the field and identifying gaps, designing appropriate research strategies, and selecting appropriate research methods to address these gaps, all the way to analysing and interpreting data and presenting research findings. By learning how to do research and justify and present their methodological choices with confidence and rigour, students will also learn how to evaluate research evidence to support decision-making in business contexts. The course will thus better equip them for the world of business as well as their final year when undertaking their own independent dissertation research.
The course is designed to be hands-on providing students with the opportunity to learn by conducting research and analysing data, in addition to understanding the theoretical underpinnings of business research decisions that shape society.
Interactive lectures present critical overviews of key concepts, processes, and debates within the subject, relating these to a wide range of current examples of research in business practice. Tutorials are designed to give students the opportunity to work in groups and apply their learning, providing a practical experience with the research process and specific methods. It is expected that students engage with readings and activities in advance of each class and actively contribute to, and participate in, classroom debates. The aim is to foster a collaborative, non-judgemental, and inclusive learning environment.
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Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2026/27, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Seminar/Tutorial Hours 8,
Fieldwork Hours 4,
Online Activities 10,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
154 )
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| Assessment (Further Info) |
Written Exam
40 %,
Coursework
60 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
60% Project Report (Individual), 2,000 words - Assesses course Learning Outcomes 1,4,5
40% Written Exam - Assesses all course Learning Outcomes |
| Feedback |
Formative feedback: will be given in class during the discussion sessions, in project advice meetings, as well as on all submitted coursework.
Summative Feedback: Feedback on coursework, together with individual marks, will be provided on Learn within agreed deadlines. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Apply appropriate statistical techniques to analyse business data.
- Estimate and interpret statistical models, including simple and multiple regression models, in order to analyse relationships within business data.
- Evaluate model assumptions, limitations, and robustness, and assess the reliability of statistical findings.
- Analyse structured datasets to identify patterns, trends, and support forecasting in business contexts.
- Interpret and communicate statistical results clearly and appropriately to support evidence-based business decision-making.
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Reading List
| Anderson, D. et al. (2024). Statistics for Business and Economics, Cengage. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Quantitative research,statistics,data analysis |
Contacts
| Course organiser | Dr Nicholas Myers
Tel:
Email: Nick.Myers@ed.ac.uk |
Course secretary | |
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