Data Analysis and Visualization

2021/2022

Academic Year

ENG

Instruction in English

4

ECTS credits

Delivered at:

Institute of Media

Course type:

Compulsory course

When:

1 year, 3, 4 module

Instructors

Berezhnaya, Tina

Kazakova, Maria

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

Course Contents

• 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

Assessment Elements

• Attendance
• Class and homework assignment
• Final project

Interim Assessment

• 2021/2022 4th module
  0.4 * Final project + 0.3 * Class and homework assignment + 0.3 * Attendance

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