Кто читает:: Департамент менеджмента

Статус:: Курс обязательный

Когда читается:: 1-й курс, 3, 4 модуль

Преподаватель

Тараскина Елена Владимировна

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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 define the basic principles and peculiarities of the research and analytical method
Know the main methods of qualitative and quantitative research
Understand the difference between the aims of qualitative and quantitative research
to be able to find the appropriate data
to be able to collect qualitative or quantitative data
to understand the sampling issues as well as bias and validity issues
to be able to identify the type of data and type of variable
to be able to prepare the data for the analysis
to be able to use structured or unstructured data for the analysis
to be able to measure the center and the location of the variable
to be able to measure the variation of the variable
to use the appropriate type of visualization
to be able to formulate the hypothesis
to be able to test the hypothesis
to be able to use factor analysis and principal component analysis
to be able to analyse data using quantitative or qualitative methods
to be able to use regression analysis
to interpret the results of analysis

Course Contents

Introduction to analytics: research methods
Data sources and data collection
Data types and data preparation
Data description and visualization
Hypothesis testing
Data analysis

Assessment Elements

In-class activity
Short quizzes at the beginning of the seminar classes and discussion of the small tasks from the lectures.
Homeworks
Student are supposed to answer to the set of questions, based on the information from the given scientific articles.
Project
The project consists of practical assignments, written project report and project defense.
Tests
Electronic tests in LMS for 25-30 minutes
Final exam
Closed-book test in Smart LMS (including both closed and open-ended questions).

Interim Assessment

2024/2025 4th module
0.3 * Final exam + 0.05 * Homeworks + 0.1 * Homeworks + 0.075 * In-class activity + 0.075 * In-class activity + 0.075 * Project + 0.175 * Project + 0.075 * Tests + 0.075 * Tests

Bibliography

Recommended Core Bibliography

Fabbris, L., & Davino, C. (2013). Survey Data Collection and Integration. Springer.
Groebner, David, et al. Business Statistics, EBook, Global Edition, Pearson Education, Limited, 2018. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=5186156.
HU, C.-P., & CHANG, Y.-Y. (2017). John W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.BCEBF1CE
Nabavi, M., & Olson, D. L. (2019). Introduction to Business Analytics. New York: Business Expert Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1922612
S. Christian Albright, & Wayne L. Winston. (2019). Business Analytics: Data Analysis & Decision Making, Edition 7. Cengage Learning.
Stock, J. H., & Watson, M. W. (2015). Introduction to Econometrics, Update, Global Edition (Vol. Updated third edition). Boston: Pearson Education. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1419285

Recommended Additional Bibliography

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
Robert I. Kabacoff. (2015). R in Action : Data Analysis and Graphics with R: Vol. Second edition. Manning.

Authors

TARASKINA ELENA VLADIMIROVNA
BUDKO VIKTORIYA ALEKSANDROVNA

Магистерская программа «Менеджмент в индустрии впечатлений»

Контакты

Analytics in Arts and Culture