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Lecturer(s)
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Munk Michal, prof. RNDr. Ph.D.
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Course content
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Measurement procedures and measurement quality (estimation of objectivity, reliability and validity) Research plans and research methodology (census, sample survey, experiment) Data sources (combination and extension of data sources, knowledge discovery methodologies, knowledge discovery vs. research plans) Exploratory analysis (summarization, data description and visualization, analysis of residual values, data transformation) Probability as a theoretical basis of inferential analysis (normal distribution of a random variable, overview of distributions derived from the normal distribution) Inferential analysis (parameter estimates and hypotheses testing, parametric/non-parametric tests, dependent/independent samples, univariate/multiple/multivariate analyzes) Basic statistical methods (descriptive statistics, distribution tests, tests about variance and their non-parametric alternatives, tests about expected value and their non-parametric alternatives, relations between variables) Multivariate exploratory techniques (measures of relations, segmentation, classification, dimension reduction, reliability analysis) Linear models (regression analysis, analysis of variance), general linear models GLM, generalized linear models GLZM
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Learning activities and teaching methods
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Dialogic (discussion, interview, brainstorming)
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Learning outcomes
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The aim of the subject is to acquire the basic factual, conceptual, and procedural knowledge of the statistical and mathematical methods, which a student is able to creatively apply in scientific work in the field of applied informatics. The student is able to apply statistical analysis procedures in order to verify research hypotheses and assumptions. For this purpose, the student is able to obtain the necessary data through the measurement procedures, and/or the student can evaluate data sources, combine data sources, extend data sources with data obtained from own survey. The student is able to critically analyze and synthesize research concepts, report on analysis results, make statistical predictions, and provide visual data presentation.
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Prerequisites
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unspecified
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Assessment methods and criteria
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Self project defence
Successful completion of the course is conditional on demonstrating the ability to apply the acquired knowledge to scientific work. Graduation requirements: Successful completion of the course is conditional on an exam and an independent project solution. The oral exam consists of theoretical questions (40%) and a project defense (60%). The course evaluation is determined by the exam result. Credits will not be awarded to a student who receives less than 70 percent in the overall point evaluation.
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Recommended literature
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Agbinya, J. I. Applied Data Analytics - Principles and Applications. Publishers, 2020. ISBN 978-87-7022-095-8.
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Munk, M., Pilkova, A., Benko, L., Blazekova, P., Svec, P. Methodology of stakeholders' behaviour modelling based on time. Elsevier, 2021.
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Munk, M., Pilkova, A., Benko, L., Blazekova, P., Svec, P. Pillar 3 - Pre-processed web server log file dataset of the banking institution. Elsevier, 2021.
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Munk, M., Pilkova, A., Benko, L., Blazekova, P., Svec, P. Web usage analysis of Pillar 3 disclosed information by deposit customers in turbulent times. Elsevier, 2021.
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Santos, M. Y., Costa, C. Big Data : Concepts, Warehousing, and Analytics. River Publishers, 2020. ISBN 978-1-119-52841-8.
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Stuard, D. Practical Data Science for Information Professionals. Facet Publishing, 2020. ISBN 978-1-78330-346-5.
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Verma, J. P., Abdel-Salam, G. Testing Statistical Assumptions in Research. John Wiley & Sons: Incorporated, 2019. ISBN 978-1-119-52841-8.
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