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Regular version of the site
Master 2019/2020

Data Analysis and Visualization

Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type: Compulsory course (International News Production)
Area of studies: Media Communications
Delivered by: Institute of Media
When: 1 year, 3, 4 module
Mode of studies: offline
Instructors: Tina Berezhnaya
Master’s programme: International News Production
Language: English
ECTS credits: 3
Contact hours: 48

Course Syllabus


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.
Learning Objectives

Learning Objectives

  • 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.
Expected Learning Outcomes

Expected Learning Outcomes

  • Be able to find data and open data
  • Be able to collect and analyze data for journalistic purposes
  • Be able to make meaningful correlations
  • Be able to visualize data in a number of platforms and online services
  • Be able to assess the quality of data visualizations
  • Be able to place data and data analysis results in context
  • Be able to create data-based narratives
  • Be able to develop data-based stories
  • Be able to assess the quality of data-storytelling
Course Contents

Course Contents

  • Data
    Definition of data, difference of data and information, big data as philosophy and technology, big data vs. open data
  • Open data and government open data
    Definitions of open data, attributes of open data, legal regulation of open data, data champions, open data sources, data ethics. Government open data regulations and sources, open-washing in government open data, data search tools, data management.
  • Data collection tools
    Online data search and collection tools. Web-scraping. Journalistic tools and legal regulations for obtaining data.
  • Excel and online tools for data analysis
    Automated processes in Excel. Various online data analysis tools (Google family and open-source solutions).
  • Data visualization theory, tools, and services
    Data visualization requirements. Infographics vs. data visualization. Cognitive mechanisms and visual representation. Types of data visualizations and their applications. Native Excel tools for data visualization. Online tools for data visualization (Infogram, Rawgrapsh, Flourish, Tableau, DataWrapper and others). Maps for data visualization and GIS services.
  • Data-driven material
    Data and context. Data as news source and data as proof. Interim visualizations.
  • Data-storytelling
    Specific features of data stories. Typical mistakes of data-storytelling. Data storytelling workshop.
Assessment Elements

Assessment Elements

  • non-blocking Attendance
  • non-blocking Class and homework assignment
  • non-blocking Final project
Interim Assessment

Interim Assessment

  • Interim assessment (4 module)
    0.1 * Attendance + 0.5 * Class and homework assignment + 0.4 * Final project


Recommended Core Bibliography

  • 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.

Recommended Additional Bibliography

  • 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