Научно-исследовательский семинар "Большие Данные: принципы и парадигмы"

Магистратура 2025/2026

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

Кто читает: Департамент бизнес-информатики

Когда читается: 1-й курс, 2, 3 модуль

Охват аудитории: для своего кампуса

Преподаватели: Джин Сеунгмин

Язык: английский

Контактные часы: 48

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Abstract

"Research Seminar: Big Data - Principles and Paradigms": This seminar explores the evolving landscape of Big Data, focusing on its principles and paradigms. We will examine current trends in data analytics, particularly in the context of business analytics, and how these paradigms are transforming decision-making processes. The course will cover topics such as emotional data visualization, efficient data compression techniques for scientific visualization, and innovative approaches to visualizing complex data structures. Through this seminar, participants will gain insights into the latest advancements in data visualization and learn how to apply these techniques to enhance business strategies.

Learning Objectives

Identify the latest trends in Big Data visualization and analytics.
Evaluate different methodologies used in Big Data visualization and analytics to assess their applicability in various business contexts.

Expected Learning Outcomes

Define Big data issues and challenges
Define Big data issues and challenges
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
Design and evaluate an approach for the architecture of infrastructure for Big Data products
Discuss the new data intensive techniques and mathematical models to build data analytics
Identify and understand the key factors and mechanisms involved in the diffusion and utilization of big data
Clearly understand the main principles of software engineering
Clearly understand the main principles of object-oriented software engineering
Be capable of specifying software requirements
Be capable of using UML for specifying system structures, interactions, and behaviors
Be capable of using UML for specifying software architecture and design
Clearly understand different software quality properties and be capable of testi ng these quality properties
Clearly understand different software engineering processes and be capable of adopting these processes in software developments
Have some experience in working in a team
Gain the necessary team working and communication skills to work in a team effectively
Identify the latest trends in Big Data visualization and analytics.

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 and analytics
Software and Software Engineering, Developing Requirements, Modelling with Classes using UML
Modelling Interactions and Behaviour using UML, Focussing on Users and their Task
Architecting and Designing Software, Using Design Patterns
Testing and Inspecting to Ensure High Quality, Managing the Software Process
Introduction to Big Data and Visual Analytics
How to Read and Summarize IEEE VIS Papers
The Rise of Interactive Data Visualization
Immersive Data Visualization with Augmented and Virtual Reality
Automated Insights and Intelligent Analysis
The Importance of Data Storytelling
Visualization for Social Good
Smart Dashboards and Natural Language Processing
Data Comics and Creative Storytelling
Multi-Modal Approaches for Unique Perspectives
Enhancing Transparency in Public and Private Sectors
Visualization for Environmental Sustainability

Assessment Elements

Attendance
Students who miss more than 25% of the classes or fail to complete the presentation assignments will automatically fail this course.
Project 1
Course wrap-up and student presentations of low-code visual analytics projects.
Project 2
Course wrap-up and student presentations of low-code visual analytics projects.
Exam
Final exam
Project 3
Project 4

Interim Assessment

2025/2026 3rd module
Final score would be normalized by the rank of students. The distribution of scores would be announced in the class. 1. Attendance: 20% 2. Exam: 20% 3. Projects: 15% per each.

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
Conceptual drawing : freehand drawing and design visualization for design professions, Koncelik, J. A., 2008
Data mining and data visualization, , 2005
Data visualization & presentation : with Microsoft Office, Sue, V. M., 2016
Handbook of data visualization, , 2008
Handbook of graph drawing and visualization, , 2014
Information visualization : design for interaction, Spence, R., 2007
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
The functional art : an introduction to information graphics and visualization, Cairo, A., 2013
Visualizations and dashboards for learning analytics, , 2021

Recommended Additional Bibliography

9781583477182 - Soares, Sunil - Big Data Governance : An Emerging Imperative - 2012 - M C Press - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=502776 - nlebk - 502776
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

Authors

Beklarian Armen Levonovich
PANFILOV PETR BORISOVICH
Dzhin Seungmin

Course Syllabus