2026/2027
Анализ и визуализация данных
Статус:
Маго-лего
Кто читает:
Институт медиа
Где читается:
Факультет креативных индустрий
Охват аудитории:
для своего кампуса
Язык:
русский
Кредиты:
3
Контактные часы:
32
Программа дисциплины
Аннотация
Most of social, economic, and political changes and trends in the world are nowadays described with data collected on every step and turn. Making sense of the data and using it as a source of information, a newsmaker, or a proof of journalistic research has become an essential part of journalist work. The course teaches analyzing data, seeing meaningful correlations there, visualizing the data for ease of understanding and for visually presenting journalistic research, as well as crafting data-driven narratives and creating data-storytelling
Цель освоения дисциплины
- The course is aimed at journalism majors dealing with modern digital methods of analyzing and presenting information
- The course teaches understanding data and data sources, quality of data, collecting and normalizing data, analyzing data and finding stories in it
- During the course students are taught to see context for data, create data-based narrative, asses what data needs visual representation and what tools to use for most efficient visual data representation and data-storytelling
Планируемые результаты обучения
- Be able to: assess the quality of data visualizations
- assess the quality of data-storytelling
- collect and analyze data for journalistic purposes
- create data-based narratives
- develop data-based stories
- find data and open data
- make meaningful correlations
- place data and data analysis results in context
- visualize data in a number of platforms and online services
Содержание учебной дисциплины
- Data
- Open data and government open data
- Data collection tools
- Excel and online tools for data analysis
- Data visualization theory, tools, and services
- Data-driven material
- Data-storytelling
Промежуточная аттестация
- 2026/2027 3rd module0.3 * Class and homework assignment + 0.1 * Attendance + 0.6 * Final project
Список литературы
Рекомендуемая основная литература
- Chazal, F., & Michel, B. (2017). An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsarx&AN=edsarx.1710.04019
- Pernille Christensen. (2011). An Introduction to Statistical Methods and Data Analysis (6th ed., international ed.). Journal of Property Investment & Finance, (2), 227. https://doi.org/10.1108/jpif.2011.29.2.227.1?utm_campaign=RePEc&WT.mc_id=RePEc
Рекомендуемая дополнительная литература
- Milliken, G. A., & Johnson, D. E. (2009). Analysis of Messy Data Volume 1 : Designed Experiments, Second Edition (Vol. 2nd ed). Boca Raton: Chapman and Hall/CRC. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=271612