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- DATA ANALYSIS WITH EXCEL FOR INSURANCE COMPANIES CODE
- DATA ANALYSIS WITH EXCEL FOR INSURANCE COMPANIES FREE
This is very fast because the functions can run in parallel and can scale linearly.Īmazon has a wide array of tools which enable data analysis for very large datasets. A reduce function then consumes the result(s) and produces the final analytical result. In this data is fed to a map function which emits are result or array of results. The key enabler is a map-reduce concept which is a staple of functional programming. By using functional programming concepts and distributed datastore, hadoop and related technologies are able to analyse very large volumes of data. They huff and puff and grind to a stop some point in their life cycle. However, no matter how great these tools are, they have their limitations and processing grinds to a halt as you throw more data to these hungry animals. A number of libraries are developed each day and you can do serious magic with some of the code.
DATA ANALYSIS WITH EXCEL FOR INSURANCE COMPANIES FREE
The advantage of these tools is that they are free and have a large community driven support system. There has been a shift towards opensource and free tools such as R, Power Pivot and Python libraries like numpy, scipy and pandas to do analytical work. For sophisticated analysis, companies would have to purchase server versions and feed truck load of storage and memory. However, these tools are quite expensive and do not scale beyond a level.
DATA ANALYSIS WITH EXCEL FOR INSURANCE COMPANIES CODE
The next level of tools have been the giant analytic packages like SAS, SPSS, Matlab etc which utilise memory better and with native machine code can manipulate electrons faster and churn out pretty graphs compared to Ms-Excel. I am sure anyone working with these tools would have gone through hours of frustration when the data type of the underlying data was not uniform or blanks were present. However, these tools also do not cover large giga-byte data sets. OLAP and Pivot tables provide the next level of sophistication and can handle larger datasets. Excel formulae and SQL joins do not scale well and certainly not feasible to carry out even mildly sophistical analysis e.g. However, these solutions are quite limiting due to inherent limitations of the tools involved. These could be supplemented with triggers and user defined functions in SQL.
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Some companies created SQL views and queries to analyse the data. Historically, analysis was either done at aggregate levels, whereby simple calculators, MS-Excel, APL scripts or MS-Access could be utilised to carry out analysis of the data. In this article, I'll discuss the analytical tools available to insurance companies to gain insight in their data. I discussed traditional SQL based databases, OLAP and modern NO-OLAP and other sophisticated databases. The focus of this script will not be on outright 'predictive performance', but rather we will take a more 'data science' / research oriented approach, focusing on model robustness and data understanding.In Part 1 of this post, I focused on data stores available to insurance companies. Using a data set provided by Prudential Insurance as part of their recent Kaggle Challenge ), we will apply a number data science techniques to visualise, better understand, statistically analyse and prepare the data for prediction. However, despite this bounty, much of the Insurance industry is still built around 17th century 'Actuarial' math, meaning this data is either under utilised or not used at all.Įven with the integration of more modern financial economics into the insurance process, much of it relies on 'assumption based' approaches - such as determining the Discount Rate to be used - this is where Machine Learning comes in. Of all the industries rife with vast amounts of data, the Insurance market surely has to be one of the greatest treasure troves for both data scientist and insurers alike. Here we will look at a Data Science challenge within the Insurance space.