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Course Syllabus

Master 2024/2025

Research Seminar "Latest Trends in Data Governance, Big Data Analytics & Data Architecture"

Type: Compulsory course (Business Analytics and Big Data Systems)
Area of studies: Business Informatics
Delivered by: Department of Business Informatics
Where: Graduate School of Business
When: 2 year, 1-3 module
Mode of studies: offline
Open to: students of one campus
Instructors: Seungmin Jin, Petr Panfilov
Master’s programme: Business Analytics and Big Data Systems
Language: English
ECTS credits: 6

Course Syllabus

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Abstract

This course's key objective is to make the students familiar with implementing the most important big data concepts in various business domains. We will discuss industries like: Banking and Securities; Communications, Media and Entertainment; Healthcare; Education; Manufacturing and Natural Resources; Government; Insurance; Retail and Wholesale trade
Learning Objectives

Learning Objectives

  • This course gives insights into how big data technologies impact the business
Expected Learning Outcomes

Expected Learning Outcomes

  • Describe the ethics, governance, and sustainability challenges relating to Big Data
  • Design and evaluate an approach for the architecture of infrastructure for Big Data products based upon particular needs, including selecting an appropriate set of technologies, and governance strategy for storage and processing data
  • Discuss the impact of digitization and the adoption of Big Data in business and overall society
  • Explain the challenges of creating and maintaining Big Data products
  • Demonstrate effective utilization of LLMs in academic writing while maintaining research integrity and scholarly standards
Course Contents

Course Contents

  • Big Data Ecosystem
  • (Big) Data Management
  • Data Products and Economics
  • Data Culture and Ethics
  • Latest Trend of Scientific Writing using LLMs
Assessment Elements

Assessment Elements

  • non-blocking State-of-the-art
  • non-blocking Activity during classes
  • non-blocking Project defense
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • 2024/2025 3rd module
    0.1 * Activity during classes + 0.4 * Exam + 0.25 * Project defense + 0.25 * State-of-the-art
Bibliography

Bibliography

Recommended Core Bibliography

  • 11 essentials of effective writing, Radaskiewicz McNeely, A. M., 2014
  • GPT-3 : the ultimate guide to building NLP products with OpenAI API, Kublik, S., 2022
  • GPT-4. Руководство по использованию API Open AI, Эль Амри, А., 2024
  • Malaska, T., & Seidman, J. (2018). Foundations for Architecting Data Solutions : Managing Successful Data Projects: Vol. First edition. O’Reilly Media.
  • Thomas Erl, Wajid Khattak, & Paul Buhler. (2016). Big Data Fundamentals : Concepts, Drivers & Techniques. Prentice Hall.

Recommended Additional Bibliography

  • Jules S. Damji, Brooke Wenig, Tathagata Das, & Denny Lee. (2020). Learning Spark. O’Reilly Media.
  • Kleppmann, M. (2017). Designing Data-Intensive Applications : The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1487643
  • Mark Richards, & Neal Ford. (2019). Fundamentals of Software Architecture : An Engineering Approach. O’Reilly Media.

Authors

  • Dzhin Seungmin