| Course Title | Big Data |
| Course Code | M-546-04 |
| Level | Master |
| ECTS Credits | 6.00 |
| Faculty/unit | Engineering Faculty |
| Field | Computer and information sciences |
| Course Type | Standard |
| Course Leader | Pavlyuk Dmitry - Dr. sc. ing. professor |
| Annotation | In traditional machine learning courses frequently the majority of the attention is paid to the modelling stage. Often in practice modelling stage accounts for a relatively small fraction. Moreover, the model’s accuracy is strongly influenced by the other stages. Hence the objective of this course is to explore all of the stages of the big data lifecycle; individually and as a whole. While the second part of the course will be targeted at the design and development own data product. |
| Aim | The aim of this course is to explore all of the stages of the big data lifecycle; individually and as a whole and provide knowledge and skills to enable the creation of the own data product |
| LO1 | Understand similarities and differences between different big data lifecycle frameworks; be familiar with methods used in each of the stages of the big data lifecycle and demonstrate understanding of the integration of stages of the big data lifecycle |
| LO2 | Ability to apply methods for the bottom-up and top-down data analysis for data identification and acquisition; for pre/processing of different data types (textual, numeric, media, relational); for performing exploratory data analysis; for performing modelling and evaluation; for performing error analysis and optimization |
| LO3 | Ability to define a practical plan for handling each of the stages of the big data lifecycle; justify the choice of methods for big data stages; integrate big data stages |
| LO4 | Ability to design and develop data products, based on the integration of appropriate methods from machine learning, data visualization, and UI/UX |
| Required Literature | - Machine Learning and Big Data : Concepts, Algorithms, Tools and Applications, edited by Uma N. Dulhare, et al., John Wiley & Sons, Incorporated, 2020. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/tsilv/detail.action?docID=6268187.
- Roy S. et al. Big Data in Engineering Applications, Springer Singapore, 2018, https://ebookcentral.proquest.com/lib/tsilv/detail.action?docID=5379963
- Sedkaoui, Soraya. Data Analytics and Big Data, John Wiley & Sons, Incorporated, 2018. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/tsilv/detail.action?docID=5401178.
- Santos, Maribel Yasmina, and Carlos Costa. Big Data : Concepts, Warehousing, and Analytics, River Publishers, 2020. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/tsilv/detail.action?docID=6184637.
- Ryzko, Dominik. Modern Big Data Architectures : A Multi-Agent Systems Perspective, John Wiley & Sons, Incorporated, 2020. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/tsilv/detail.action?docID=6173712.
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| Assessment Method | The assessment is a project including a viva.
- Project (individual completion). This task involves students investigating a business-related cloud adoption problem based on given requirements, proposing a solution and preparing implementation specifications. The actual assignment topics are carefully chosen to demonstrate some basic principles, which are especially significant to the course.
- Oral presentation/Viva (individual completion). This task consists of questions related to coursework produced by the student which should test the students' understanding of the concepts presented in the coursework as well as their understanding and ability to apply those concepts and ideas to real-life scenarios (case studies).
There will be opportunities for formative assessment in the form of regular in-class presentations of research/implementation completed as part of tutorial work and subsequent group discussions.
The resit is a rework/update of the project. |
| Independent study | The independent study incorporates additional material reading/watching. These materials will be provided by the teaching staff during the course. The majority of the time for independent study should be devoted to project development. The consulting hours will be available to support students in this process |
| Full-time |
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| First Sit Elements | Element Weighting, % | Group Work | Links to Results |
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| Reports | 100 | | LO1, LO2, LO3, LO4 |
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