Магистратура
2023/2024
Научно-исследовательский семинар "Большие Данные: принципы и парадигмы"
Статус:
Курс обязательный (Бизнес-аналитика и системы больших данных)
Направление:
38.04.05. Бизнес-информатика
Кто читает:
Департамент бизнес-информатики
Где читается:
Высшая школа бизнеса
Когда читается:
1-й курс, 2, 3 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Прогр. обучения:
Бизнес-аналитика и системы больших данных
Язык:
английский
Кредиты:
3
Контактные часы:
24
Course Syllabus
Abstract
Research seminar Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. We will learn about Big Data trends and challenges, Data Management and Governance, Data Science, and Data Analytics. Course discusses potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications
Learning Objectives
- This course gives insights into how big data technologies impact the business
- Due to the continuous technology advancements and customer demands, software systems are getting larger and more complex ever-increasingly. So, software systems may not necessarily be developed within the limited budget and delivered at the expected time. Quality is also another important issue that needs to be addressed in software development. Indeed, failing to meet the expected level of quality may lead to catastrophic consequences, especially for safety-critical systems (e.g., railway systems, airplanes, etc.). To handle the development of large and complex software systems, software engineering has been introduced, which aims at applying the principles of engineering to software development. By doing so, software systems can be developed within budget and delivered at the expected time with the expected level of quality. The aim of this seminar series is to introduce the discipline of Software Engineering, focussing on the knowledge and technology required and how they can be applied in developing software systems in terms of their requirements, analysis, and design. The seminar series also aims at introducing the Unified Modeling Language (UML) to let students have practical experience in specifying software requirements, analysis, and design.
- The objectives: • To learn the principles of software engineering, e.g., re-use, abstraction, modularity, etc. • To learn software re-use and abstraction • To learn the basics of Object-Oriented software engineering, e.g., classes and objects, and its main principles, i.e., encapsulation, inheritance, dynamic binding, and polymorphism • To learn what software requirements are and how to specify them • To get introduced with the Unified Modeling Language (UML) for specifying software systems • To learn how to use UML for specifying system structures, interactions, and behaviors • To learn what software architecture and design are and how to specify them using UML • To learn software design patterns and their re-use in developing software systems • To learn what software quality is and how to test software quality • To learn the basics of software engineering processes • To gain experience in working as a team • To improve team-working and communication skills
Expected Learning Outcomes
- Define Big data issues and challenges
- Define Big data issues and challenges
- Define the approach to managing the flow of an information system's data throughout its life cycle
- Describe the ethics, and privacy challenges relating to Big Data
- Design and evaluate an approach for the architecture of infrastructure for Big Data products
- Discuss the new data intensive techniques and mathematical models to build data analytics
- Identify and understand the key factors and mechanisms involved in the diffusion and utilization of big data
- Clearly understand the main principles of software engineering
- Clearly understand the main principles of object-oriented software engineering
- Be capable of specifying software requirements
- Be capable of using UML for specifying system structures, interactions, and behaviors
- Be capable of using UML for specifying software architecture and design
- Clearly understand different software quality properties and be capable of testi ng these quality properties
- Clearly understand different software engineering processes and be capable of adopting these processes in software developments
- Have some experience in working in a team
- Gain the necessary team working and communication skills to work in a team effectively
Course Contents
- Big Data's Big Potential
- Big Data's Big Problems
- Principles underlying Big Data computing
- Computational platforms supporting Big Data applications
- Life-cycle data management
- Data analysis algorithms
- Big Data privacy and ethical issues
- Challenges in Big Data management and analytics
- Software and Software Engineering, Developing Requirements, Modelling with Classes using UML
- Modelling Interactions and Behaviour using UML, Focussing on Users and their Task
- Architecting and Designing Software, Using Design Patterns
- Testing and Inspecting to Ensure High Quality, Managing the Software Process
Assessment Elements
- ExamYou are required to use all the materials that are taught to you in the seminar series by means of a project work and put your knowledge into practice. In the project work, you are expected to work as a group of 5-6 students and work collaboratively. Therefore, you will also improve your team work and communication skills.
- Activity during classes
Bibliography
Recommended Core Bibliography
- Buyya, R., Calheiros, R. N., & Vahid Dastjerdi, A. (2016). Big Data : Principles and Paradigms. Cambridge, MA: Morgan Kaufmann. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1145031
- Raheem, N. (2019). Big Data : A Tutorial-Based Approach (Vol. First edition). Boca Raton: Chapman and Hall/CRC. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2031482
Recommended Additional Bibliography
- Ogrean Claudia. (2018). Relevance of Big Data for Business and Management. Exploratory Insights (Part I). https://doi.org/10.2478/sbe-2018-0027
- Ogrean Claudia. (2019). Relevance of Big Data for Business and Management. Exploratory Insights (Part II). https://doi.org/10.2478/sbe-2019-0013
- Prabhu, C. S. R. (2019). Fog Computing, Deep Learning and Big Data Analytics-Research Directions. Singapore: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1994845
- Soares, S. (2012). Big Data Governance : An Emerging Imperative: Vol. 1st ed. MC Press.