Master 2020/2021

Big Data Systems Research Seminar "Big Data: Principles and Paradigms"

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'

Category 'Best Course for New Knowledge and Skills'

Type: Compulsory course

Area of studies: Business Informatics

Delivered by: Department of Business Informatics

Where: Graduate School of Business

When: 1 year, 1 module

Mode of studies: offline

Instructors: Petr Panfilov

Master’s programme: Big Data Systems

Language: English

ECTS credits: 3

Contact hours: 28

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 you insights into how big data technologies impact the business.

Expected Learning Outcomes

• Identify and understand the key factors and mechanisms involved in the diffusion and utilization of big data
• Define Big data issues and challenges
• Discuss the new data intensive techniques and mathematical models to build data analytics
• Design and evaluate an approach for the architecture of infrastructure for Big Data products
• 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

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 & analytics

Assessment Elements

• Activity during classes
• Exam
  Exam format: the exam is taken in writing, remotely (online) on MSTeams platform

Interim Assessment

• Interim assessment (1 module)
  0.5 * Activity during classes + 0.5 * Exam

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.