Software Applications for Actuaries
- Credits: 5
- Ending: Examination
- Range: 4C
- Semester: winter
- Year: 1
- Faculty of Economic Informatics
Teachers
Included in study programs
Teaching results
Completion of the subject Software applications for actuaries presupposes the development of IT skills and skills in the field of data science.
Knowledge
Progress in the field of new knowledge is evident after completing the course. Students will gain an overview of actuarial software and learn to work with programming languages R, Python and VBA. They will gain a basic overview of actuarial analysis in R and Python.
Competences
Based on the above knowledge, students can choose a suitable programming language according to its parameters and perform adequate actuarial analyzes, respectively analysis of data processing in these programming languages.
Skills
As part of the educational process, they will acquire such skills that will enable students to read, process and analyze data necessary for further actuarial analyzes, or to carry out such operations and create reports that can help in managerial decision-making in insurance companies.
Indicative content
1. Actuarial software and possibilities of its use. Advantages disadvantages. R. Language R. R Studio Studio console. R Project. Objects in R (vector, factor, matrix, data table, list, array). Working with objects. Libraries (packages). Graphics in R.
2. Basics of programming in R. Data Science in R. Machine Learning in R. Data Manipulation (Data Wrangling). Imputation of missing values. Anomalies in the data. Working with dplyr, shiny and ggplot2 libraries.
3. Statistical analyzes in R. Probability distributions in R. Some applications of R in actuarial science.
4. Python language. Installation and start. Basic data types. Variables.
5. Programming mode. Some basic functions. Working with modules. Anaconda. Jupyter Notebook.
6. Data Science in Python. Interconnection R and Python languages. Some Python applications in actuarial science.
7. Introduction to Visual Basic for Applications (VBA) in Microsoft Excel. Getting acquainted with the VBA editor environment, Macro Recorder. Basics of object hierarchy. Working with a Range object (property: Cells, Value, FormulaR1C1, Column, Row, Count, Address, Offset, Resize, method: Select, End, Clear). Defining an object variable of type Range.
8. Area selection (Range, End method, CurrentRegion, UsedRange), Union and Intersect method, Area copying, Using With-End With and For Each-Next structures when working with a Range object.
9. Work with procedures. Procedure declaration, scope of procedures, declaration of variables, scope and their validity, array of variables and its declaration, fast loading of arrays, static and dynamic arrays, control of code flow using loops (For-Next, Do While, Do Until) and constructions (With -End With, For Each- Next, If - Then, Select Case).
10. Working with functions. Built-in user dialogs (Inputbox and MsgBox functions), selected workbook functions (WorksheetFunction) and VBA functions, creation of own functions (User defined functions).
11. Working with data in a workbook. Searching for VBA-compliant data, copying, deleting, and editing it (iteration method, SpeciallCells method, Autofilter method, AdvancedFilter method).
12. Creating dashboards using ActiveX controls, resp. Forms (ListBox, ComboBox, OptionButton, CheckBox, ScrollBar) and functions (Offset, Index, Choose, If, Match, VLookup, Direct, Column), resp. name manager and data verification (list).
13. Creating dashboards using a PivotTable in the context of a slicer, timeliner, and conditional formatting.
Support literature
1. PÁLEŠ, M. Jazyk R pre aktuárov. Bratislava : Vydavateľstvo Letra Edu, 2019.
2. DE LAFAYE MICHEAUX, P. – DROUILHET, R. – LIQUET, B. The R Software. Fundamentals of Programming and Statistical Analysis. New York : Springer, 2013.
3. DUTANG, C. – GOULET, V. – PIGEON, M. actuar: An R Package for Actuarial Science. Journal of Statistical Software, 2008.
4. ALBERT, J. – RIZZO, M. R by Example. New York : Springer, 2012.
5. CHARPENTIER, A. Computational Actuarial Science with R. Boca Raton : CRC Press, 2015.
6. LANTZ, B. Machine Learning with R. Second Edition. Birmingham : Packt Publishing, 2015.
7. JEKEL, C. Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib. In Siam Review, vol. 62, 2, 2020.
8. PECINOVSKÝ, R. Python. Kompletní příručka jazyka pro verzi 3.8. Praha: Grada Publishing, 2020.
9. PILGRIM, M. Python 3. Ponořme se do Python(u) 3. Praha: CZ.NIC, z. s. p. o., 2011.
10. UNPINGCO, J. Python for Probability, Statistics, and Machine Learning. Second Edition. Cham: Springer Nature Switzerland AG, 2016.
11. HILPISCH, Y. Derivatives Analytics with Python. Data Analysis, Models, Simulation, Calibration and Hedging. West Sussex: John Wiley & Sons Ltd, 2015.
12. ALEXANDER, M. – KUSLEIKA, D. Excel 2019. Power programming with VBA. Indianapolis: John Wiley & Sons, Inc. 2019.
13. KRÁL, M. Excel VBA. Výukový kurz, Praha: Computer Press, 2012.
14. MANSFIELD, R. Mastering VBA for Microsoft Office 2016. Indianapolis: John Wiley & Sons, Inc. 2016.
15. ALEXANDER, M.– WALKENBACH, J. Microsoft Excel. Dashboards & Reports. New Jersey: 2013.
16. ALBRIGHT, CH., S. VBA for Modelers. Developing decision support systems with Microsoft Office Excel. South-Western. 2012.
17. GOLDMEIER, J. –DUGGIRALA, P. Dashboards for Excel. California: Apress. 2015.
Syllabus
1. Actuarial software and possibilities of its use. Advantages disadvantages. R. Language R. R Studio Studio console. R Project. Objects in R (vector, factor, matrix, data table, list, array). Working with objects. Libraries (packages). Graphics in R. 2. Basics of programming in R. Data Science in R. Machine Learning in R. Data Manipulation (Data Wrangling). Imputation of missing values. Anomalies in the data. Working with dplyr, shiny and ggplot2 libraries. 3. Statistical analyzes in R. Probability distributions in R. Some applications of R in actuarial science. 4. Python language. Installation and start. Basic data types. Variables. 5. Programming mode. Some basic functions. Working with modules. Anaconda. Jupyter Notebook. 6. Data Science in Python. Interconnection R and Python languages. Some Python applications in actuarial science. 7. Introduction to Visual Basic for Applications (VBA) in Microsoft Excel. Getting acquainted with the VBA editor environment, Macro Recorder. Basics of object hierarchy. Working with a Range object (property: Cells, Value, FormulaR1C1, Column, Row, Count, Address, Offset, Resize, method: Select, End, Clear). Defining an object variable of type Range. 8. Area selection (Range, End method, CurrentRegion, UsedRange), Union and Intersect method, Area copying, Using With-End With and For Each-Next structures when working with a Range object. 9. Work with procedures. Procedure declaration, scope of procedures, declaration of variables, scope and their validity, array of variables and its declaration, fast loading of arrays, static and dynamic arrays, control of code flow using loops (For-Next, Do While, Do Until) and constructions (With -End With, For Each- Next, If - Then, Select Case). 10. Working with functions. Built-in user dialogs (Inputbox and MsgBox functions), selected workbook functions (WorksheetFunction) and VBA functions, creation of own functions (User defined functions). 11. Working with data in a workbook. Searching for VBA-compliant data, copying, deleting, and editing it (iteration method, SpeciallCells method, Autofilter method, AdvancedFilter method). 12. Creating dashboards using ActiveX controls, resp. Forms (ListBox, ComboBox, OptionButton, CheckBox, ScrollBar) and functions (Offset, Index, Choose, If, Match, VLookup, Direct, Column), resp. name manager and data verification (list). 13. Creating dashboards using a PivotTable in the context of a slicer, timeliner, and conditional formatting.
Requirements to complete the course
100% written work
Student workload
Total study load (in hours): 130 hours
52 hours of lectures,
52 hours preparation for seminars,
26 hours preparation for written work.
Language whose command is required to complete the course
slovak
Date of approval: 10.02.2023
Date of the latest change: 15.05.2022