| Course Title | Business Intelligence and Data Visualisation |
| Course Code | M-232-04 |
| Level | Master |
| ECTS Credits | 6.00 |
| Faculty/unit | Engineering Faculty |
| Field | Interdisciplinary course |
| Course Type | Standard |
| Course Leader | Spiridovska Nadežda - Dr. sc. ing. associated professor |
| Annotation | Business Intelligence and Data Visualisation introduces students to the core concepts of data visualisation and machine learning in the context of business intelligence and leads them through an independent exploration of a typical machine learning project. In the frame of the course, students will have the experience to use modern visualisation tools (for example Tableau) |
| Aim | This course aims to provide students with a broad understanding business intelligence tools, platforms and methods and introduces them to evidence-based practice in data visualisation |
| LO1 | Define, demarcate, and explore business problems interactively in communication with key stakeholders |
| LO2 | Select, evaluate, and employ appropriate tools, platforms and methods to generate BI and visualisation solutions. |
| LO3 | Reflect upon and critique own and others informational and visual artefact |
| LO4 | Deliver a data-driven narrative appropriately and effectively to stakeholders, based on a theoretical underpinning of communicating for impact |
| Required Literature | - Loth, Alexander. Visual Analytics with Tableau, John Wiley & Sons, Incorporated, 2019. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/tsilv/detail.action?docID=5748882.
- Loshin, David. Business Intelligence : The Savvy Manager's Guide, Elsevier Science & Technology, 2012. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/tsilv/detail.action?docID=1034439.
- Kotorov, Rado. Data-Driven Business Models for the Digital Economy, Business Expert Press, 2020. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/tsilv/detail.action?docID=6178484.
|
| Assessment Method | The overall assessment context will be chosen from a selection of realistic or real-world case studies with accompanying data to be provided by tutors and / or external partners. The written component will be a reflective portfolio updated periodically during the term and covering:
Problem definition and scoping; Identification and selection of data; Exploration of comparable approaches in the literature and on the web; Data analysis; Prototyping of visualisations / artifacts;
Peer, tutor, or external client feedback from critique.
Students will be expected to draw on relevant research in reflecting on the above and to utilise appropriate tools to undertake analysis and design. The final, presentation component will be a presentation by the student of the findings and conclusions of the module-long analysis, with QA from tutors or external clients.
|
| Independent study | Independent study will cover reading of additional materials delivered in frame of the course and preparation of the reports and the solution. Also specific software tool will be explored in details during independent study. |
| Full-time |
|---|
| First Sit Elements | Element Weighting, % | Group Work | Links to Results |
|---|
| Reports | 50 | | LO1, LO2, LO3 | | Presentations | 50 | | LO2, LO4 |
|
