Legal documents play a vital role in protecting the interests of a business and the business owners over the course of a company’s lifetime. Documents such as master agreements, shareholder or partnership agreements, and memorandum of understanding specify how a company’s business affairs should be organised. In Wholesale banking, for example, the contractual relationships that a bank has with its clients dictate how the bank should calculate the daily margin calls, estimate its counterparty credit exposures, and whether a client is entitled to the client money protection rules or not. It is therefore vital that audit professionals pay close attention to legal documents and provide sufficient assurance that a company’s day to day business affairs are being conducted in accordance with its legal and contractual obligations.
“Python” and “R” are amongst the most popular open source programming languages for data science. While R’s functionality was developed with statisticians in mind, Python on the other hand is a general-purpose programming language, with an easy to understand syntax and a gentle learning curve. Historically, R has been used primarily for academic and research work, although anecdotal evidence suggests that R is starting to see some level of adoption in the enterprise world (especially in the Financial Services sector) due to its robust data visualisation capabilities. Python, on the other hand, is widely used in enterprises due to the breadth of its functionalities that span beyond data analytics and also because of the ease with which Python based solutions can be integrated with many other technologies in a typical enterprise set up. Needless to say, due to my enterprise background, I am more inclined towards Python as the data analysis tool of my choice.
In addition to Python and R, there is also a wide variety of very powerful commercial data analysis software. However, Python has several advantages over these commercial offerings as follows.