Enhancing Statistics and Actuarial Science Education with Computer Packages
Explore the use of various computer packages for teaching statistics and actuarial science, potential benefits of utilizing proper statistical tools, incorporating data science content for professional exams, and the importance of data science in the curriculum to enhance students' skills in real-world data analysis and problem-solving.
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Computer packages for teaching statistics and actuarial science Paul King et Sally Calder, Luke Hatter, Andy Hinde, Trevor Watkins
What packages are used? CT1 Excel CT3 R R R R R R R Excel Excel Excel Minitab Minitab Minitab Minitab SAS SPSS Matlab CT4 R R R Excel Excel SPSS CT5 R R Excel Excel Excel Excel Excel Excel Maple Matlab CT6 R R R R R Excel Excel Excel Excel STATA Minitab SAS WinBUGS R R R R R R Excel Excel Excel Excel Excel Excel Excel Excel Other SAS SAS SAS Maple Minitab STATA WinRes Igloo Prophet Matlab Matlab SQL R Excel Minitab SAS Matlab Maple STATA SPSS 23 24 6 5 4 2 2 2 Responses 14 2
Potential benefits Ability to use a proper statistical package for teaching statistics and data science: Mirrors real world work More realistic exam questions Focus on principles not computation 3
How to use packages for professional exams? One possible idea: Combine CT subjects: ? Examine each combination with: A closed book examination (as now) A practical assessment (as we do for CA2?) Remove current content where there is overlap and Introduce data science content 4
How to use packages for professional exams? One possible idea: Combine CT subjects: CT3 & CT6 (R) CT4 & CT5 (Excel) CT1, CT8 ?? Examine each combination with: A closed book examination (as now) A practical assessment (as we do for CA2?) Remove current content where there is overlap and Introduce data science content 5
Data science in the syllabus The aim of the data and systems subject is to give the student the ability to apply methods from statistics and computer science to real-world data sets in order to answer business and other questions. Data as a resource Data analysis Machine learning Professional and risk management issues Visualizing data and reporting 6
