It’s been a little time since I last posted, mainly due to an unexpected batch of baby fish arriving … I love data and gardening (kind of the same thing).
I wanted to discuss the many different languages you may come across in your analytic life time, each one with unique and great tools to help you achieve your objective. Us analysts can be pretty protective of our favourite language but it’s well worth getting an understanding of a couple of different tools.
So here is a brief description of a few you will most commonly come across although this is not an exhaustive list.
SQL: This is one of building blocks of analytics and is used to communicate with your database. You’ll come across many types of SQL each with slightly different structures each with their own quirks and syntax. It is a great place to start if you have never programmed before as it helps you build up an understanding of programming logic but also of your data structure.
I’ll do a brief intro to SQL in an upcoming post.
SAS: Is a statistical programming language (and much much more) which is commonly used by big financial institutions. It is often used in combination with SQL and is used to build statistical processes, BI reports and to model data (not the runway type). It is relatively easy to learn due to the structure of the syntax and has a great interface. However, it can be seriously expensive which means often smaller companies, unfortunately, cannot afford this tool currently.
There is a solution for smaller businesses called WPS which whilst not as comprehensive as SAS does provide a good tool for programming in SAS as well as R and Python.
R: Is a great freeware statistical programming language, which has a lot of similar functionality as SAS but using a vastly different syntax. Its functionality is great for more “techy” analytics and especially for machine learning. However, it is a lot harder to learn and there are some risks around using the packages as they are developed by contributors.
Python: Is a language which supports statistics and much more, it is relatively easy to learn and provides a lot of great functionality, from my experience the visualisation isn’t as strong as R or SAS ( but things change quickly in the world of data programming).
This is only a very very brief synopsis of each language and a huge amount of great information out there about these tools. The biggest thing to keep in mind is if you can think of something you wish to achieve analytically, you are very likely to be able to do it in one of the languages above.
The Girl Who Loves Data 