This is the first iteration of an actuaries’ data science wiki. The aim is to expand, refine and develop this wiki to encompass a knowledge-base that scopes the key terms and essential definitions of disciplines associated with data science practice, that have particular resonance for actuarial professionals.
Data analytics is the discipline of analysing data sets to make conclusions about that information. Data analytics techniques can reveal trends and metrics that would otherwise be undiscoverable in massed information. This information can then be used to optimise processes to increase the overall efficiency of business or system operations.
Data analytics is a broad term that encompasses diverse types of data analysis. Any type of information can be subjected to data analytics techniques to gain insight that can be used to achieve improvements. Some of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work through raw data for subsequent human analysis.
Data analytics methodologies include exploratory data analysis (aims to find patterns and relationships in data), and confirmatory data analysis (applies statistical techniques to determine whether hypotheses about a data set are true or false). EDA is comparable to ‘detective work’, while CDA is comparable to the ‘work of a judge or jury during a court trial’ (John W. Tukey, Exploratory Data Analysis, Pearson, 1977).
Data analytics can also be separated into quantitative data analysis and qualitative data analysis. The former involves analysis of numerical data with quantifiable variables that can be compared or measured statistically. The qualitative approach is more interpretive – it focuses on understanding the content of non-numerical data like text, images, audio and video, including common phrases, themes, and points of view.
https://www.investopedia.com/terms/d/data-analytics.asp
https://searchdatamanagement.techtarget.com/definition/data-analytics
Data analysts and actuaries share similarities. They have comparable skill sets, and use mathematics, statistical techniques, and computer knowledge to compile and analyse data, and to report conclusions for business decision-making. The two disciplines differ in the scope of their work and employment settings.
For instance, data analysts work in a broad variety of vertical sectors and industries with multiple types of data. They apply mathematical and statistical techniques to extract, analyse and summarise data. They use spreadsheet and statistical software, work with relational databases, and prepare charts and reports of their findings. Their work transforms large, complicated data sets into usable insights that inform organisational leadership decisions and policies.
Data analysts review information and use the data to help develop recommendations. They do not specifically focus on risks, but may help determine appropriate business or financial decisions that will benefit a company.
https://work.chron.com/data-analyst-vs-actuary-16473.html
https://study.com/articles/difference_between_actuary_data_analyst.html
The main goal of data visualisation is to communicate information clearly and effectively through graphical means. and by maintaining a library of data visualisation techniques. The IFoA Data Visualisation Working Party was established in 2017. Its vision is that data visualisation for actuaries should represent:
https://www.actuaries.org.uk/news-and-insights/news/data-visualisation-techniques-vision-actuaries
Machine Learning is a discipline that uses study of algorithms and statistical models, as used by computer systems, to perform specific tasks without use of explicit instructions: Machine Learning instead relies on patterns and inference. It is generally regarded as a subset of Artificial Intelligence.
The question of what Machine Learning could bring to actuarial work is something of a contentious issue within the insurance sector. Some have speculated on Machine Learning’s capacity to replace manual actuarial work, and therefore reduce insurers’ requirement for human actuaries. Other argue that data science-savvy actuaries could turn knowledge of Machine Learning into a useful asset in their skills offering.
https://www.actuaries.org.uk/documents/practical-application-machine-learning-within-actuarial-work
Predictive modelling involves the use of data to forecast events. It relies on the capture of relationships between explanatory variables and the predicted variables from past occurrences, and the exploitation of this to predict future outcomes. The forecasting of future financial events is a core actuarial skill. Actuaries routinely apply predictive-modelling techniques in insurance and other risk-management applications.