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

2026/2027

Учебный год

ENG

Обучение ведется на английском языке

6

Кредиты

Кто читает:

Департамент бизнес-информатики

Статус:

Курс обязательный

Когда читается:

2-й курс, 2, 3 модуль

Преподаватель

Джин Сеунгмин

Course Syllabus

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

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

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

• Latest Trend of Scientific Writing using LLMs
• The Importance of Data Storytelling
• Low-Code Deep Learning with ChatGPT
• Data Culture and Ethics
• Generative AI and prompt engineering
• Big Data Ecosystem
• (Big) Data Management
• Data Products and Economics

Assessment Elements

• Report
• Class Activity
• Project work
• Homework
• Attendance
  It is applied to lectures and seminars.
• Exam

Interim Assessment

• 2026/2027 3rd module
  The final grade is calculated as a weighted sum of raw scores, which is then min-max normalized. To adjust the overall distribution, grade allocations are capped based on student rankings. The specific formulas and distribution details are as follows: Raw Score Formula The overall raw score is computed as follows: Raw Score = 0.1 × Attendance + 0.1 × Class Activity + 0.2 × Report + 0.1 × Homework + 0.4 × Exam + 0.1 × Project Normalization Formula Raw scores are normalized using the minimum and maximum values across all students: Normalized Score = [(Raw Score - min(All Raw Scores)) / (max(All Raw Scores) - min(All Raw Scores))] × 100 Grade Distribution and Caps Final grades are assigned based on rank order, with caps applied to maintain the following distribution (+- 10%): • 5 points (Top tier): Up to 30% of students • 4 points: Up to 70% of students (cumulative) • 3 points or below: The remaining 30% of students The final exam is a mandatory blocking assessment component. Failure to submit the capstone project results in an unsatisfactory final mark, regardless of the student’s accumulated score in other components.

Bibliography

Recommended Core Bibliography

• 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

Recommended Additional Bibliography

• 11 essentials of effective writing, Radaskiewicz McNeely, A. M., 2014
• 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