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Regular version of the site
Master 2023/2024

Research Seminar "Big Data: Principles and Paradigms"

Type: Compulsory course (Business Analytics and Big Data Systems)
Area of studies: Business Informatics
When: 1 year, 2, 3 module
Mode of studies: offline
Open to: students of one campus
Instructors: Озкая Мерт, Petr Panfilov
Master’s programme: Business Analytics and Big Data Systems
Language: English
ECTS credits: 3
Contact hours: 24

Course Syllabus

Abstract

"Research Seminar: Big Data - Principles and Paradigms": This seminar explores the evolving landscape of Big Data, focusing on its principles and paradigms. We will examine current trends in data analytics, particularly in the context of business analytics, and how these paradigms are transforming decision-making processes. The course will cover topics such as emotional data visualization, efficient data compression techniques for scientific visualization, and innovative approaches to visualizing complex data structures. Through this seminar, participants will gain insights into the latest advancements in data visualization and learn how to apply these techniques to enhance business strategies.
Learning Objectives

Learning Objectives

  • Identify the latest trends in Big Data visualization and analytics.
  • Evaluate different methodologies used in Big Data visualization and analytics to assess their applicability in various business contexts.
Expected Learning Outcomes

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
  • Identify the latest trends in Big Data visualization and analytics.
Course Contents

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

Assessment Elements

  • non-blocking Exam
    You 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.
  • non-blocking Activity during classes
Interim Assessment

Interim Assessment

  • 2023/2024 3rd module
    0.5 * Activity during classes + 0.5 * Exam
Bibliography

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

  • 9781583477182 - Soares, Sunil - Big Data Governance : An Emerging Imperative - 2012 - M C Press - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=502776 - nlebk - 502776
  • 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

Authors

  • Beklarian Armen Levonovich
  • PANFILOV PETR BORISOVICH