Master
2024/2025
Research Seminar "Big Data: Principles and Paradigms"
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:
1 year, 2, 3 module
Mode of studies:
offline
Open to:
students of one campus
Master’s programme:
Business Analytics and Big Data Systems
Language:
English
ECTS credits:
3
Course Syllabus
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
- 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
- ExamThe final exam will assess your understanding of the key concepts covered throughout the course, including the IEEE VIS papers you summarized. The exam may consist of multiple choice, short answer, and essay questions. These questions are designed to evaluate both your theoretical knowledge and practical application skills. Clear evaluation criteria will be provided, and practice questions will be available to help you prepare effectively. This comprehensive approach ensures that you can demonstrate your learning outcomes successfully.
- Activity during classes
- AttendanceMissing class: -1 credit penalty Missing classes more than half: No credit.
Interim Assessment
- 2024/2025 3rd module0.25 * Activity during classes + 0.25 * Attendance + 0.5 * Exam
Bibliography
Recommended Core Bibliography
- 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
- The functional art : an introduction to information graphics and visualization, Cairo, A., 2013
- Visualizations and dashboards for learning analytics, , 2021