Postgraduate Course: AI Tools for Business (CMSE11705)
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 | 20 |
ECTS Credits | 10 |
| Summary | In this course, students learn to transform data into decisions by leveraging AI tools and business analytics, guided by industry-standard workflows like CRISP-DM and SEMMA. The course covers the full data analytics project lifecycle from scoping and data preparation to modelling and decision-making using tools such as Orange and PyOPL. Practical examples and guest lectures bridge theory with real-world applications. |
| Course description |
This course offers a practical, industry-aligned introduction to AI and data analytics for business applications. Structured around established standards like CRISP-DM and SEMMA, it guides students through the full analytics lifecycle from business understanding to decision-making. The journey begins with a historical overview of AI and its evolving role in business, followed by a deep dive into software engineering principles essential for vibe coding with generative AI tools. Students explore the business analytics workflow, learning to distinguish between descriptive, predictive, and prescriptive analytics, and how to align projects with strategic goals. Key topics include stakeholder analysis, project scoping, and tools like the Business Model Canvas. Technical lectures cover data ingestion, preparation, and exploratory analysis, using real-world examples and synthetic datasets to illustrate common challenges and solutions. Hands-on sessions with tools like Orange and PyOPL introduce model building, evaluation, and optimisation, culminating in prescriptive analytics where predictions inform actionable decisions. The course concludes with guest lectures from industry and academia, offering insights into cutting-edge applications and research. By the end, students will be equipped to manage AI-driven analytics projects with confidence, clarity, and creativity.
Outline content:
The course structure is shaped around established industrial standards (such as CRISP-DM and SEMMA) which provide systematic approaches to solving business and data analytics problems across industries.
Some of the topics covered in class include: ยท
- Historical overview of AI
- Software Engineering principles and vibe coding
- The Business Analytics workflow, and Business Understanding
- Data ingestion, Preparation, Exploratory Data Analysis
- Descriptive, Predictive, and Prescriptive Analytics
Student learning experience:
In addition to attending lectures, students will engage in groups (4 to 5 students) with a practical project in the realm of data analytics in the context of which they will be asked to operationalise some of the techniques presented throughout the course.
Each student will have to choose and operationalise one technique from the course, the outcome of the project will be a composition of contributions from all students in the group; and students will have to make clear who contributed with what in their individual statement of contribution.
In line with School standard practices, the lecturer will offer bespoke drop-in time (1 hour each week before the scheduled lecture) to meet students and answer relevant questions. The lecturer will also offer up to 3 hours of group contact time (we expect around 5 groups, so the total budget will be 15 hours of contact time).
After unit Business Understanding, each group will be asked to submit a Project Overview Statement outlining the project they wish to tackle and will receive feedback on their POS shortly after - the POS is however not marked. The final submission will comprise:
1. A 10-minute video presentation of the project outcome with associated PPT slide deck (or equivalent) submitted by the group
2. Supporting portfolio (software developed, data, etc.), which should also include a statement of contribution, submitted by each individual student
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Course Delivery Information
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| Academic year 2026/27, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Dissertation/Project Supervision Hours 3,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
173 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
50 %,
Practical Exam
50 %
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| Additional Information (Assessment) |
50% Portfolio (Individual) - Assesses all course Learning Outcomes
50% Video assignment (Group) - Assesses all course Learning Outcomes |
| Feedback |
Formative: Feedback will be provided on the POS submitted by each group, and during ad-hoc group meetings that each group will be expected to schedule with the lecturer.
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:
- Frame business problems and success criteria by aligning them with descriptive, predictive, and prescriptive analytics using stakeholder analysis, POS, and canvases.
- Design reproducible data pipelines by selecting ETL/ELT strategies, ingestion methods, orchestration tools, while considering data contracts and governance.
- Prepare and explore data through cleaning, transformation, feature engineering, and accurate visualisations.
- Build and evaluate classification, regression, and clustering models using Orange/Python with proper validation, metrics, and result communication.
- Translate predictions into decisions by solving optimisation models, assessing solution quality, and generating actionable, human-in-the-loop recommendations with responsible and effective GenAI use.
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Reading List
Core Text(s):
Artificial Intelligence: A modern Approach (ISBN-10 : 1292153962)
Blaz Zupan and Janez Demsar. Introduction to data mining: Working notes for the hands-on course for phd students at university of ljubljana. http://orange.biolab.si, Fall & Winter 2017.
Robert K Wysocki. Effective project management : traditional, agile, extreme, hybrid. John Wiley & Sons, Nashville, TN, 8 edition, June 2019. (ISBN-10 : 1119562805) |
Additional Information
| Graduate Attributes and Skills |
Communication, ICT, and Numeracy Skills
After completing this course, students should be able to:
Convey meaning and message through a wide range of communication tools, including digital technology and social media; to understand how to use these tools to communicate in ways that sustain positive and responsible relationships.
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.
Practice: Applied Knowledge, Skills and Understanding
After completing this course, students should be able to:
Apply creative, innovative, entrepreneurial, sustainable and responsible business solutions to address social, economic and environmental global challenges.
Cognitive Skills
After completing this course, students should be able to:
Be self-motivated; curious; show initiative; set, achieve and surpass goals; as well as demonstrating adaptability, capable of handling complexity and ambiguity, with a willingness to learn; as well as being able to demonstrate the use digital and other tools to carry out tasks effectively, productively, and with attention to quality.
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| Keywords | Not entered |
Contacts
| Course organiser | Dr Roberto Rossi
Tel: (0131 6)51 5239
Email: Roberto.Rossi@ed.ac.uk |
Course secretary | |
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