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Lecturer(s)
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Course content
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Basic principles of relational databases. Introduction to SQL, basic operations. Intermediate SQL operations. Introduction to Python, development environment, basic principles. Variables and data types in Python. Conditions and loops in Python. Functions and libraries in Python. Selected statistical methods and their application to financial data in Python. Case studies from the insurance and banking sectors.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Work with text (with textbook, with book), Methods of individual activities, Skills training
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Learning outcomes
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To introduce students to the basics of data analysis in the financial sector. Emphasis is placed on the practical use of SQL and Python tools and the application of statistical methods in the context of insurance and banking.
Students who successfully complete the course will be able to: - Explain the principles of relational databases. - Explain the principles of basic operations used in relational database queries. - Define the basic principles of programming in Python. - Define effective procedures for data processing in Python. - Independently decide on the suitability of selected statistical tools for a given problem. - Analyze and interpret results in specific case studies. - Present data analysis outputs in a comprehensible manner. Students who successfully complete the course will be able to: - Use SQL queries to retrieve data from relational databases. - Work with data in Python, including data cleaning. - Apply selected statistical methods in the context of financial data. Students who successfully complete the course will be able to: - Independently retrieve and prepare data from a relational database, select appropriate statistical methods based on the nature of the data and the problem, apply these methods using appropriate software tools (SQL, Python), and interpret the results obtained.
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Prerequisites
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unspecified
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Assessment methods and criteria
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Oral examination, Written examination, Student performance assessment
Credit assignment: test Examination: written and oral.
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Recommended literature
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Python for data analysis . Milton Keynes, UK: Computer Scince Academy, 2020. ISBN 978-1-80125-526-4.
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Grus, Joel. Data science from scratch . Beijing ;: O'Reilly, 2019. ISBN 978-1-4920-4113-9.
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Kubanová,J. Statistické metody pro ekonomickou a technickou praxi.
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Laurenčík, Marek. SQL . Praha: Grada Publishing, 2018. ISBN 978-80-271-0774-2.
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Ma, Weiming James. Mastering Python for finance . Birmingham, UK: Packt, 2019. ISBN 978-1-78934-646-6.
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Pecinovský, Rudolf. Python . Praha: Grada Publishing, 2021. ISBN 978-80-271-3442-7.
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Starmer, Josh. The StatQuest illustrated guide to neural networks and AI. 2025.
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TSE Y.-K. Nonlife Actuarial Models. Cambridge: University Press, 2009.
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