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Regular version of the site
Master 2020/2021

Analytics in Arts and Culture

Type: Compulsory course (Arts and Culture Management)
Area of studies: Management
When: 1 year, 1-4 module
Mode of studies: offline
Instructors: Irina Borovskaya, Aleksei Gorgadze, Valeria A. Ivaniushina, Ekaterina Savelieva, Anastasia Sinitsyna, Julia Trabskaya
Master’s programme: Art and Culture Management
Language: English
ECTS credits: 12
Contact hours: 96

Course Syllabus


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

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

Expected Learning Outcomes

  • 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 organize, conduct and analyze observation for research
  • 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 conduct descriptive statistics on quantitative data, apply basic statistical methods and interpret results of analysis
  • 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 perform data collection and preparation for the analysis using text mining methods
  • Able to create interview guides, conduct and analyze interviews, transcribe audio recordings, interpret results
  • Able to apply and interpret advanced statistical methods to quantitative empirical data
Course Contents

Course Contents

  • Introduction: Research Methods & Methodology
    Differences in methods & methodology. Basic research methods in management. Data collection and analysis methods. Research gap. Purpose and hypothesis. Ethical standards in research. Organization of scientific and analytical research. Discussion of cases. Investment fund model.
  • Observation method in researches
    Organization of observation in field research. Geertz's Thick Description. Types of observation. Participant observation. Observational diaries. Data decision.
  • Questionnaires and Surveys
    Design and structure of questionnaires and question types. Sampling. Data encoding and data cleaning.
  • Descriptive statistics
    Basic methods of statistics in R and SPSS. Correlation. Regression analysis. Comparison of means (t-test, anova). Chi-squared test. Clustering methods.
  • Social network analysis
    Basic concepts of social network analysis. Nodes, edges. Matrix and graph. Observable and perceptive networks. Centrality and centralization. In-degree and out-degree, closeness, betweenness, eigenvector centrality. Density and geodesic distance. Diameter. Reciprocity and transitivity. Node’s attributes, groups and homophily.
  • Text Mining
    Digital Footprint Data. Data Collection: structural data and SNS data (Data Miner, Popsters). Features of text data.: Lexicon. Frequency analysis of texts. Zipf's law. Data preparation. Morphological analysis. Stamping and Lemmatization. Feature engineering: Regular expressions & Text classification.: Feature engineering. Prediction of attributes by words and features. Algorithms of classification. Naive Bayes. Regression Models. Identifying Characteristic Words: Log-likelihood.: Compares the appearance of a word indifferent collections. Collocations & PMI: Co-occurrence. N-grams. Methods for detecting collocations. “bag of words” model. Collocation measure. logDice. Semantic Networks. Topic Modeling (LDA): The operationalization of the "topic" concept as a probability distribution vocabulary. Latent Dirichlet allocation (LDA).
  • Qualitative data collection methods
    Types of interviews. Formulation of research objectives and drawing up a guide. Preparing for data collection: gaining access to respondents, preparing materials for interviews. Drawing up a guide. Recording quality data. Analysis and coding of qualitative data: method, technique, software.
  • Advanced statistics
    Linear regression. Factor analysis. Bivariate pierced the model. Multivariate model. Structural models.
Assessment Elements

Assessment Elements

  • non-blocking 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.
  • non-blocking Midterm tests
  • non-blocking Practical tasks
    Conducting observations, conducting a survey of organizations visitors, conducting a survey of employees, interviewing managers of organizations, collecting text data.
  • non-blocking Homeworks
    Reading articles, preparing guides and questionnaires, searching for materials, laboratory work.
  • non-blocking Examination
Interim Assessment

Interim Assessment

  • Interim assessment (4 module)
    0.2 * Examination + 0.15 * Homeworks + 0.2 * In-class participation + 0.2 * Midterm tests + 0.25 * Practical tasks


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