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

Where: Faculty of Creative Industries

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

Abstract

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

• 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

• 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

• 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

• Attendance
• Class and homework assignment
• Final project

Interim Assessment

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

Bibliography

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.2011.29.2.227.1?utm_campaign=RePEc&WT.mc_id=RePEc

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