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)
- Teacher: Spiridovska Nadežda
This module will provide students with the opportunity to bring together knowledge and skills gathered throughout the study programme by conducting a project designing, implementing, evaluating and presenting an AI-based solution to a real-world problem.
Students work in small groups, selecting and using appropriate project management methodologies and tools. The work should involve making best use of the technical and human resources available to you.
A series of lectures by staff and guest speakers will present emerging problems of applied AI and state-of-the-art techniques for handling the issues that often arise during AI-based projects.
Students work in small groups, selecting and using appropriate project management methodologies and tools. The work should involve making best use of the technical and human resources available to you.
A series of lectures by staff and guest speakers will present emerging problems of applied AI and state-of-the-art techniques for handling the issues that often arise during AI-based projects.
- Supervisor: Pavlyuk Dmitry
This course will equip students with knowledge and understanding of tools and techniques commonly utilised within the field of Machine Learning. The course will set the context of the machine learning and predictive analytics utilisation in business intelligence. Courses discusses range of applications for predictive analytics. Wide range of the machine learning techniques will be considered in the course Decision tree learning, Artificial neural networks, Naive Bayes classifier, Genetic algorithms etc.
- Teacher: Kijonoka Jeļena
This course (Part 1 and Part 2) aims to develop a systematic approach to research through the use of information gathering and planning techniques. The course gives a comprehensive background and approaches for designing qualitative, quantitative, and mixed methods research in the engineering sciences. This course addresses the key elements of the research process: identifying and formulating a research problem, writing an introduction, stating a purpose for the study, identifying research questions and hypotheses, and advancing methods and procedures for data collection and analysis etc.
Through practical exercises in Part 1, students will develop the research skills that are required of many careers as well as in advanced graduate degrees.
The Part 2 aims to extend and deepen the understanding of different research approaches and methodologies in order to prepare students for carrying out their MSc Thesis in their respective areas of specialization.
The course is an essential part of the preparation for the Masters Thesis.
Through practical exercises in Part 1, students will develop the research skills that are required of many careers as well as in advanced graduate degrees.
The Part 2 aims to extend and deepen the understanding of different research approaches and methodologies in order to prepare students for carrying out their MSc Thesis in their respective areas of specialization.
The course is an essential part of the preparation for the Masters Thesis.
- Supervisor: Pavlyuk Dmitry