Postgraduate Course: Data-driven Business Insights (CMSE11648)
Course Outline
| School | Business School |
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 | 10 |
ECTS Credits | 5 |
| Summary | This course is specifically crafted to provide students with a comprehensive understanding of data analytics techniques and their practical application in business environments. The curriculum aims to equip learners with the skills necessary to analyse, interpret, and leverage insights to drive business decisions and strategies. |
| Course description |
This course is tailored to impart a comprehensive understanding of data-driven business insights, emphasising the practical application and strategic importance of data analysis in contemporary business contexts. Throughout the course, participants will explore the dynamic landscape of data analysis, learning to harness the power of data-driven decision-making in business environments. Emphasis will be placed on the various methodologies and tools for extracting valuable insights from data, while also providing a theoretical foundation for these practices.
Students will develop proficiency in utilising data analysis tools to address real-world business challenges, gaining hands-on experience in the translation of complex data into actionable business insights. The course aims to balance the exploration of advanced data analysis concepts with their practical applications, moving beyond the technical intricacies of algorithms to focus on their relevance and application in a business setting.
Outline Content
- Linear Models
- Nonlinear Models
- Feature Selection
- Accuracy Metrics
- Analytics Interpretations
Student Learning Experience
Students will cover the underlying principles of data-driven techniques with a focus on drawing actionable business insights.
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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 2024/25, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Block 3 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
88 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
100% Project report (Individual) - Assesses all course Learning Outcomes |
| Feedback |
Formative: Feedback will be provided throughout the course.
Summative: Feedback will be provided on assessments within agreed deadlines. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Critically assess the role and impact of data-driven insights in shaping strategic business decisions.
- Explore and articulate the range of methodologies involved in generating data-driven insights, including data analysis, feature selection, and model validation techniques.
- Master and apply foundational modelling techniques, emphasising their relevance and adaptability in deriving business insights.
- Communicate complex data-driven concepts effectively, adapting the message for various stakeholders and ensuring clarity and precision.
- Foster an analytical mindset, enabling the identification, interpretation, and leverage of data-driven insights to address business challenges.
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Reading List
Core text(s)
Boschetti, Alberto, and Luca Massaron. Python Data Science Essentials: Become an Efficient Data Science Practitioner by Understanding Python's Key Concepts. Packt Publishing, 2016. Print.
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (2013) An Introduction to Statistical Learning with Applications in R.
Geron, Aurelien. Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems / Aurelien Geron. Third edition. Beijing: O'Reilly, 2022. Print.
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Additional Information
| Graduate Attributes and Skills |
Communication, ICT, and Numeracy Skills
After completing this course, students should be able to:
Critically evaluate and present digital and other sources, research methods, data and information; discern their limitations, accuracy, validity, reliability and suitability; and apply responsibly in a wide variety of organisational contexts.
Knowledge and Understanding
After completing this course, students should be able to:
Demonstrate a thorough knowledge and understanding of contemporary organisational disciplines; comprehend the role of business within the contemporary world; and critically evaluate and synthesise primary and secondary research and sources of evidence in order to make, and present, well informed and transparent organisation-related decisions, which have a positive global impact.
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| Keywords | Banking,Data analytics,Business Insights |
Contacts
| Course organiser | Dr Stavros Stavroglou
Tel: (0131 6)51 1603
Email: Stavros.Stavroglou@ed.ac.uk |
Course secretary | |
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