Analytics in Arts and Culture

2021/2022

Academic Year

ENG

Instruction in English

6

ECTS credits

Delivered at:

Department of Management

Course type:

Compulsory course

When:

1 year, 1-3 module

Instructors

Gorgadze, Aleksei

Paklina, Sofia

Sinitsyna, Anastasia

Trabskaya, Julia

Course Syllabus

Abstract

Art analytics allows the industry to measure the financial and intrinsic value of each art piece with greater accuracy. Exceptionally popular art pieces can be auctioned for millions of dollars because there is no other duplicate piece available. However, the price of a single art piece is often decided without important context like the artist’s other completed works. Art analytics promises to address this situation by pulling information from different sources to give a more well-rounded view of the art piece’s value. In addition to the economic value of art, there are also social and educational benefits as well. Art brings cultural value to any society, but measuring that value in precise numbers has always been a challenge. However, due to art analytics, it is becoming easier to measure the intrinsic value art brings, not just to our homes but the public as well. Art analytics makes use of new data like social media and user-generated sites that make it easier to measure the emotional and mental effects of art. Art and the wider cultural sector can diversify their business models and discover other avenues for revenue thanks to art analytics. Art institutions can experiment with different business models without risking their hard-earned capital. Art analytics uses sophisticated algorithms to analyse data to make predictions on how targeted customers respond to new art events. For example, will customers pay for live-stream theatre? With analytics, institutions will be emboldened to try out new forms of art and develop new experiences that will expand their audience.

Learning Objectives

• Develop students' holistic understanding of the methodology of scientific and analytical research.
• Develop students’ skills in the use of research tools, both for planning, preparing and conducting research projects in the framework of writing a term paper and master's thesis, and for performing and evaluating research and analytical work.
• Master students' capabilities to develop and implement various research strategies.

Expected Learning Outcomes

• Able to apply and interpret advanced statistical methods to quantitative empirical data
• Able to conduct descriptive statistics on quantitative data, apply basic statistical methods and interpret results of analysis
• Able to create a questionnaire for a survey based on a theoretical concept, organize a representative sample, conduct a survey, encode and analyze the received data
• Able to create interview guides, conduct and analyze interviews, transcribe audio recordings, interpret results
• Able to define the basic principles and peculiarities of the research and analytical method
• Able to develop a research design and research strategy
• Able to formulate questions to identify the network structure, to apply methods of social network analysis to empirical data and develop research design within social network analysis framework
• Able to organize, conduct and analyze observation for research
• Able to perform data collection and preparation for the analysis using text mining methods

Course Contents

• Introduction: Research Methods & Methodology
• Observation method in researches
• Questionnaires and Surveys
• Descriptive statistics
• Social network analysis
• Text Mining
• Qualitative data collection methods
• Advanced statistics

Assessment Elements

• Practical tasks
  Conducting observations, conducting a survey of organizations visitors, conducting a survey of employees, interviewing managers of organizations, collecting text data.
• In-class participation
  Participation in seminar workshops and contribution to seminar discussions, based on the mandatory readings. In-class participation is evaluated throughout the whole course.
• Midterm tests
• Methods
  The assessment is formed from the evaluation elements in the 2nd module
• Task 1
• Task 2

Interim Assessment

• 2021/2022 3rd module
  0.1 * Task 1 + 0.15 * Practical tasks + 0.25 * Methods + 0.05 * In-class participation + 0.15 * Midterm tests + 0.1 * Task 2

Bibliography

Recommended Core Bibliography

• Bamman, D., Eisenstein, J., & Schnoebelen, T. (2014). Gender identity and lexical variation in social media[The resear]. Journal of Sociolinguistics, 18(2), 135–160. https://doi.org/10.1111/josl.12080
• Introducing research methodology: A beginner's guide to doing a research project, Flick, U., 2015
• Kothari, C. R. (2004). Research Methodology : Methods & Techniques (Vol. 2nd rev. ed). New Delhi: New Age International. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=277465
• Luke, D. A. (2015). A User’s Guide to Network Analysis in R. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1114415
• Nguyen, D., Gravel, R., Trieschnigg, D., & Meder, T. (2013). “How old do you think I am?” A study of language and age in Twitter. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.E50BF78
• Seetaram, N., Gill, A., & Dwyer, L. (2012). Handbook of Research Methods in Tourism : Quantitative and Qualitative Approaches. Cheltenham, UK: Edward Elgar Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=477866
• Ted Dunning. (1993). Accurate methods for the statistics of surprise and coincidence. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.421C83DD

Recommended Additional Bibliography

• Gorgadze Aleksey, & Kolycheva Alina. (n.d.). Mapping Ideas: Semantic Analysis of “Postnauka” Materials. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsclk&AN=edsclk.https%3a%2f%2fcyberleninka.ru%2farticle%2fn%2fmapping-ideas-semantic-analysis-of-postnauka-materials
• Munzert, S. (2014). Automated Data Collection with R : A Practical Guide to Web Scraping and Text Mining. HobokenChichester, West Sussex, United Kingdom: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=878670
• R in action: Data analysis and graphics with R, Kabacoff, R.I., 2015

Authors

• SINITSYNA ANASTASIYA SERGEEVNA
• GORGADZE ALEKSEY ALEKSEEVICH
• TRABSKAYA YULIYA GEORGIEVNA