Data Visualization

2022/2023

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Департамент менеджмента

Статус:

Курс по выбору

Когда читается:

2-й курс, 2 модуль

Course Syllabus

Abstract

This course provides a comprehensive introduction to working with basic graphics and introduces several supported basic graph types that are particularly useful for visualizing important objects in a dataset. The course starts with simple tools, such as histograms and density graphs, to characterize a single variable. Then we move on to scatter plots and other useful tools to demonstrate the relationship of the two variables. Finally, we present some tools for visualizing more complex relationships in our dataset.

Learning Objectives

• To provide students with basic working knowledge and practical skills in Data Preprocessing and Data Visualization.

Expected Learning Outcomes

• be able to create a table with the descriptive statistics
• know the ways of collecting empirical data for quantitative analysis
• understand and be able to implement data preprocessing
• have skills in reading and transforming data, calculation of basic statistics and interpretation of results
• be able to choose correct type of graph and visualize spatial data using specialized software
• be able to visualize geodata, networks and texts

Course Contents

• Searching and data collection
• Data Preprocessing
• Manipulations with data
• Basic methods of statistics
• Types of graphs
• Visualization of advanced analysis methods

Assessment Elements

• Final test
  The final test includes questions (open and/or closed) and practical tasks on the topics of classes. The weight of each question will depend on the complexity of the task. The final score will be calculated according to a 10-point system, depending on the weight of the questions.
• Presentation of project
  The presentation includes a presentation of data visualization based on own data (open or closed). The format of the presentation (presentation or poster) is agreed with the teacher. The presentation can be prepared in a group (3-5 people).
• Mid-term test

Interim Assessment

• 2022/2023 2nd module
  0.2 * Mid-term test + 0.4 * Presentation of project + 0.4 * Final test

Bibliography

Recommended Core Bibliography

• Munzert, S. (2014). Automated Data Collection with R : A Practical Guide to Web Scraping and Text Mining. HobokenChichester, West Sussex, United Kingdom: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=878670
• Rahlf, T. (2017). Data Visualisation with R : 100 Examples. Cham, Switzerland: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1377904

Recommended Additional Bibliography

• Grant, R. (2019). Data Visualization : Charts, Maps, and Interactive Graphics. Boca Raton, Florida: Chapman and Hall/CRC. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1944722
• Pelau Corina, Stanescu Mihaela, & Serban Daniela. (2019). Big-Data and Consumer Profiles – The hidden traps of data collection on social media networks. Proceedings of the International Conference on Business Excellence, 13(1), 1070–1078. https://doi.org/10.2478/picbe-2019-0093
• R in action: Data analysis and graphics with R, Kabacoff, R.I., 2015
• Roger Tourangeau, & Tom W. Smith. (1996). Asking sensitive questions: The impact of data collection mode, question format, and question context. Public Opinion Quarterly. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.7DC3DBAA