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Lecturer(s)
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Matlach Vladimír, Mgr. Ph.D.
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Course content
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(1) Introduction: digital humanities, data, big data and data-mining Sources of data from Twitter, Facebook, and Instagram, to street turnout, digital records of meetings, ebooks, descriptions of cultures, and how to get something out of them. (2) Basic data-mining relationships Extracting information from relationships between people, products and virtually anything that is (un)reported. Basic graph theory, practical tools and applications. (3) Introduction to the R language for data-mining and its alternatives. The basics of working with the R language from data retrieval to data processing. (4) Advanced data-mining relationships. Finding relationships based on similar properties of objects, categorizing them, interpreting them. From multivariate analysis (MDS, PCA, SVD), to clustering, to neural networks. (5) Text analysis and processing Specifics of text data mining from bag-of-words model to purely quantitative descriptions. (6) Automatic decision making and mining Fundamentals of machine learning from data and practical applications.
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Learning activities and teaching methods
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Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration
- Attendace
- 8 hours per semester
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Learning outcomes
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Digital-humanities are primarily associated with the emergence of big data and the potential to digitally record diverse human interactions. Whether it is digitised archives, social networks or tables of cultural characteristics, it is precisely such data that is being further analysed through different approaches, bringing new insights from these data. This course aims to present a range of methods on data and ways to further extract knowledge from it. Problems are presented through a series of practical exercises, including an introduction to the R language commonly used in data analytics. (Take a notebook.)
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Prerequisites
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unspecified
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Assessment methods and criteria
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Student performance, Seminar Work
(1) 100% attendance (2) Completion of a seminar paper
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Recommended literature
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Aggarwal, Ch. C. (2015). Data Mining: The Textbook. Switzerland: Springer International Publishing AG.
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Bartholomew, D.J., Steele, F., Moustaki, I., Galbraith, J. (2008). Analysis of multivariate social science data (2nd edition). Chapman and Hall, London.
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Bramer, M. (2007). Principles of Data Mining. London: Springer-Verlag New York.
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Cichosz, P. (2015). Data Mining Algorithms: Explained Using R. Hoboken, NJ, United States: John Wiley & Sons.
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Čech, R., Popescu, I. I., Altmann, G. (2014). Metody kvantitativní analýzy (nejen) básnických textů. Olomouc.
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Demel, Jiří. (2002). Grafy a jejich aplikace. Academia.
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Everitt, B. S., Landau, S., Leese, M., Stahl, S. (2011). Cluster Analysis. Oxford: Wiley-Blackwell.
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Husson, Franc. (2011). Exploratory Multivariate Analysis by Example Using R.
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Lantz, Brett. (2013). Machine Learning with R. Birmingham: Packt Publishing.
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Manning, Ch. D., Raghavan, P., Schütze, H. (2008). An Introduction to Information Retrieval. New York: Cambridge University Press.
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Popescu, I. (2009). Word Frequency Studies.
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Rogers, S., Girolami, M. (2016). A First Course in Machine Learning. United States: Chapman & Hall/CRC.
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Spencer, Neil Hardy. (2014). Essentials of Multivariate Data Analysis.
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