Аналитика и визуализация данных для бизнеса

Магистратура 2021/2022

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

Направление: 38.04.05. Бизнес-информатика

Кто читает: Департамент бизнес-информатики

Где читается: Высшая школа бизнеса

Когда читается: 1-й курс, 2, 3 модуль

Формат изучения: без онлайн-курса

Охват аудитории: для своего кампуса

Преподаватели: Шляпнев Максим Валерьевич

Прогр. обучения: Бизнес-аналитика и системы больших данных

Язык: английский

Кредиты: 5

Контактные часы: 40

Full Syllabus

Abstract

Data and information visualization is the graphical communication of data and information for the purposes of presentation, confirmation, exploration, and analysis. Images can be used to convey numbers, concepts, and relationships using techniques such as maps, icons, graphs, and other visual forms. In the past decade, visualization has evolved into a discipline, drawing from such fields as computer graphics, human-computer interaction, perceptual psychology, and art. The emphasis of the course will be on exposing students to the current research issues and on identifying potential research topics in data visualization as it applies to large-scale big data systems.

Learning Objectives

To introduce students to the fundamental problems, concepts, and approaches in the design and analysis of data visualization systems.
To familiarize students with the stages of the visualization pipeline, including data modeling, mapping data attributes to graphical attributes, perceptual issues, existing visualization paradigms, techniques, and tools, and evaluating the effectiveness of visualizations for specific data, task, and user types.

Expected Learning Outcomes

can identify directions for future work to advance the knowledge of the field
can identify key features and observed limitations
demonstrate knowledge of basic visualizations for document collections data such as node graphs, ThemeRiver, Calendar View
describe the methods and algorithms used to map data to graphical depictions
know a wide range of interaction techniques and styles
know algorithms and implementation details for interaction concepts
know categories of visualization and application areas
know classes of interactive operations and can define them in terms of operators and the operand
know different temporal data visualization techniques represented in TimeViz Browser
know principles and guidelines to improve the effectiveness of specific visualizations
know some commercial data visualization packages with functionality
know state-of-the-art techniques, geographic information systems (GIS) and cartography tools used for geospatial data visualization
know steps in designing visualization
know techniques for evaluating the resulting visualizations
know the definition(s) of the visualization and interpretations of the notion
know the history of data visualization and its connection with computer graphics
know the interactive visualization architecture that combines the interaction spaces into a single pipeline
know the methods and algorithms used to map data to graphical depictions
know the techniques and systems developed to date
know the visualization techniques, loosely grouped by data characteristics
know the web-sources of up to date information on hot topics in data visualization research and development
know variety of available visualization systems and toolkits
know various types of data
know what spatial attributes of data will map to the spatial attributes (locations) on the screen
knows the methods and algorithms used to map data to graphical depictions of relational information, displaying hierarchical structures
understand components and procedures necessary to assess and compare the effectiveness of visualization techniques
understand fundamental computational approaches to transforming unstructured text into structured data suitable for visualization and analysis
understand graphical primitive used in the rendering and techniques that visualize data to convey relational information
understand graphical primitives used in the rendering and techniques that combine two or more of these types of primitives
understand problems found in visualizations and techniques for avoiding these problems
understand the characteristics and methods that are needed for the visualization of geospatial data
understand the design considerations for the components of the good visualization
understand the foundations and characteristics of data, which forms the beginning of the visualization pipeline
understand the foundations of the visualization processes, from basic building blocks to taxonomies and frameworks
understand the role of user interaction within visualizations
understand the techniques that have been applied to spatial data
understand the trends in data visualization field of research and application development
understand the types of transformation the data has undergone to improve the effectiveness of the visualization
understand the visualization design process
understand the visualization pipeline
understand the visualization pipeline with its relationship to other data analysis pipelines
use TimeBench, a data model and software library for visualization and visual analytics for time-oriented data

Course Contents

Introduction
Data foundation
Visualization foundations
Visualization Techniques for Spatial Data
Visualization Techniques for Geospatial Data
Visualization Techniques for Time-Oriented Data
Visualization Techniques for Multivariate Data
Visualization Techniques for Trees, Graphs, and Networks
Text and Document Visualization
Interaction Concepts
Interaction Techniques
Designing Effective Visualizations
Comparing and Evaluating Visualization Techniques
Visualization Systems
Research Directions in Visualization

Assessment Elements

Class attendance
Presentation
10-munutes presentation about topic related to data visualization
Visualization homework/project
Exam. Conference call in MS Teams. Duscission about home work
Test
Written test contains 25 closed questions with 4 variants of answer
Work on classes
Student gets on point for each discussion related to data visualization that involves the whole class into the discussion

Interim Assessment

2021/2022 3rd module
0.15 * Test + 0.2 * Exam. Conference call in MS Teams. Duscission about home work + 0.1 * Class attendance + 0.1 * Work on classes + 0.15 * Presentation + 0.3 * Visualization homework/project

Bibliography

Recommended Core Bibliography

Ward, M., Grinstein, G. G., & Keim, D. (2015). Interactive Data Visualization : Foundations, Techniques, and Applications, Second Edition (Vol. Second edition). Boca Raton: A K Peters/CRC Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1763678

Recommended Additional Bibliography

Brath, R., & Jonker, D. (2015). Graph Analysis and Visualization : Discovering Business Opportunity in Linked Data. Wiley.
Cao, N., & Cui, W. (2016). Introduction to Text Visualization. Atlantis Press.
Dimara, E., & Perin, C. (2020). What is Interaction for Data Visualization? https://doi.org/10.1109/TVCG.2019.2934283
Federico, P., & Miksch, S. (2016). Evaluation of two interaction techniques for visualization of dynamic graphs.
Geospatial data and knowledge on the Web : Knowledge-based geospatial data integration and visualisation with Semantic Web technologies. (2020). [Lund University, Faculty of Science, Department of Physical Geography and Ecosystem Science].
GHINEA, M., FRUNZA, D., Chardonnet, J.-R., Merienne, F., & Kemeny, A. (2018). Perception of Absolute Distances Within Different Visualization Systems: HMD and CAVE.
Grinstein, G., Sieg, J. C., Jr., Smith, S., & Williams, M. G. (1992). Visualization for Knowledge Discovery. International Journal of Intelligent Systems, 7(7), 637–648. https://doi.org/10.1002/int.4550070706
Hauser, H., Rheingans, P., & Scheuermann, G. (2018). Foundations of Data Visualization (Dagstuhl Seminar 18041). https://doi.org/10.4230/DAGREP.8.1.100
He, X., Tao, Y., Wang, Q., & Lin, H. (2019). Multivariate Spatial Data Visualization: A Survey. https://doi.org/10.1007/s12650-019-00584-3
Kuntal, B. K., & Mande, S. S. (2017). Web-igloo: a web based platform for multivariate data visualization. Bioinformatics (Oxford, England), 33(4), 615–617. https://doi.org/10.1093/bioinformatics/btw669
Logre, I., & Dery-Pinna, A.-M. (2018). MDE in Support of Visualization Systems Design: a Multi-Staged Approach Tailored for Multiple Roles. https://doi.org/10.1145/3229096
Mohammad Alharbi, & Robert S. Laramee. (2019). SoS TextVis: An Extended Survey of Surveys on Text Visualization. Computers, 1, 17. https://doi.org/10.3390/computers8010017
Patterson, D., Hicks, T., Dufilie, A., Grinstein, G., & Plante, E. (2015). Dynamic Data Visualization with Weave and Brain Choropleths. Plos One, 10(9), e0139453. https://doi.org/10.1371/journal.pone.0139453
Plaisant, C., Fekete, J.-D., & Grinstein, G. (2008). Promoting Insight-Based Evaluation of Visualizations: From Contest to Benchmark Repository. https://doi.org/10.1109/TVCG.2007.70412
Rind, A. (2017). A software framework for visual analytics of time-oriented data.
Tominski, C. (2015). Interaction for Visualization. Morgan & Claypool Publishers.
Uchida, Y., Xinyun, M., Matsuno, S., Iha, Y., & Sakamoto, M. (2017). Preliminary Study on a System for Visualization of Big Data in SMEs.
Walny, J., Frisson, C., West, M., Kosminsky, D., Knudsen, S., Carpendale, S., & Willett, W. (2019). Data Changes Everything: Challenges and Opportunities in Data Visualization Design Handoff.
Wang, C., & Tao, J. (2017). Graphs in Scientific Visualization: A Survey. Computer Graphics Forum, 36(1), 263–287. https://doi.org/10.1111/cgf.12800
Werner Purgathofer, & Helwig Löffelmann. (1997). Selected New Trends in Scientific Visualization.
Wolfgang Aigner, Silvia Miksch, Wolfgang Müller, Heidrun Schumann, & Christian Tominski. (2008). Visual Methods for Analyzing Time-Oriented Data.

Authors

SHLYAPNEV MAKSIM VALEREVICH

Course Syllabus