2023/2024
Data Analysis and Visualization
Type:
Mago-Lego
Delivered by:
Institute of Media
When:
3 module
Open to:
students of one campus
Instructors:
Maria Kazakova
Language:
English
ECTS credits:
3
Contact hours:
32
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
- 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
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
Interim Assessment
- 2023/2024 3rd module0.3 * Attendance + 0.3 * 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