Научно-исследовательский семинар "Современные тенденции в управлении и аналитике больших данных"

Магистратура 2025/2026

Статус: Курс обязательный (Бизнес-аналитика и системы больших данных)

Кто читает: Департамент бизнес-информатики

Где читается: Высшая школа бизнеса

Когда читается: 2-й курс, 2, 3 модуль

Охват аудитории: для своего кампуса

Преподаватели: Джин Сеунгмин

Язык: английский

Кредиты: 6

Контактные часы: 48

Full Syllabus Ask Question

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

State-of-the-art
Activity during classes
Project defense
Exam
Project work

Interim Assessment

2025/2026 3rd module
The final grade is calculated by weighting and summing the raw scores, followed by min-max normalization, with caps on grade distributions based on student ranks to adjust the overall distribution. The specific formulas and distribution details are as follows. Raw Score Calculation Formula The overall raw score is computed as a weighted sum: Raw Score = 0.1 × Class Activity + 0.2 × Review Assignment + 0.2 × Peer-Review Project (2) + 0.25 x Exam + 0.25 x Project 1 Normalization Formula: The raw score is normalized using the minimum (min) and maximum (max) 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 Normalized scores are assigned based on student rank order, with caps applied to maintain the following distribution across all students: 5 points (top tier): Up to 30% of students 4 points: Up to 40% of students 3 points or below: The remaining 30% of students

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

Course Syllabus