Data Analysis and Visualization
- 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.
- 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
- DataDefinition of data, difference of data and information, big data as philosophy and technology, big data vs. open data
- Open data and government open dataDefinitions 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 toolsOnline data search and collection tools. Web-scraping. Journalistic tools and legal regulations for obtaining data.
- Excel and online tools for data analysisAutomated processes in Excel. Various online data analysis tools (Google family and open-source solutions).
- Data visualization theory, tools, and servicesData 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 materialData and context. Data as news source and data as proof. Interim visualizations.
- Data-storytellingSpecific features of data stories. Typical mistakes of data-storytelling. Data storytelling workshop.
- Interim assessment (4 module)0.1 * Attendance + 0.5 * Class and homework assignment + 0.4 * Final project
- 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
- 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