2024/2025
Аналитика в искусстве и культуре
Статус:
Маго-лего
Кто читает:
Департамент менеджмента
Когда читается:
3, 4 модуль
Охват аудитории:
для всех кампусов НИУ ВШЭ
Преподаватели:
Тараскина Елена Владимировна
Язык:
русский
Кредиты:
6
Программа дисциплины
Аннотация
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.
Цель освоения дисциплины
- 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
Планируемые результаты обучения
- 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
Содержание учебной дисциплины
- Introduction to analytics: research methods
- Data sources and data collection
- Data types and data preparation
- Data description and visualization
- Hypothesis testing
- Data analysis
Элементы контроля
- In-class activityShort quizzes at the beginning of the seminar classes and discussion of the small tasks from the lectures.
- HomeworksStudent are supposed to answer to the set of questions, based on the information from the given scientific articles.
- ProjectThe project consists of practical assignments, written project report and project defense.
- TestsElectronic tests in LMS for 25-30 minutes
- Final examClosed-book test in Smart LMS (including both closed and open-ended questions).
Промежуточная аттестация
- 2024/2025 4th module0.3 * 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
Список литературы
Рекомендуемая основная литература
- 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
Рекомендуемая дополнительная литература
- 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.