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Course Syllabus

Бакалавриат 2025/2026

Научно-исследовательский семинар

Статус: Курс обязательный (Компьютерные науки и технологии)
Кто читает: Кафедра информационных систем и технологий (Нижний Новгород)
Где читается: Факультет информатики, математики и компьютерных наук (Нижний Новгород)
Когда читается: 3-й курс, 2, 3 модуль
Охват аудитории: для всех кампусов НИУ ВШЭ
Преподаватели: Калягин Валерий Александрович, Лемайкина Елена Александровна, Частова Вероника Игоревна
Язык: английский
Кредиты: 3
Контактные часы: 36

Course Syllabus

Full SyllabusAsk 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

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
  • Generative AI and prompt engineering
  • Low-Code Deep Learning with ChatGPT
  • The Importance of Data Storytelling
Assessment Elements

Assessment Elements

  • non-blocking Attendance
  • non-blocking Presentation 1
    Pre-Defense Presentation.
  • non-blocking Homework
  • non-blocking Presentation 2
    TBD
  • non-blocking Final Exam
    TBD
Interim Assessment

Interim Assessment

  • 2025/2026 3rd module
    0.3 * Homework + 0.15 * Presentation 1 + 0.3 * Final Exam + 0.1 * Attendance + 0.15 * Presentation 2
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