Liad's little blog

Yesterday I had the pleasure to host Markus Harrer  who gave a talk about analyzing software code using data science, which he describe in details in his blog . There is often a communication gap between software developers and management. While good developers can see the big picture of the code, and timely identify a need to restructure or even rewrite the code, they often miss to see the risks of both, time and cost, of such an operation. The management on the other hand, while being able to identify risks of getting into an adventure of rewriting a legacy system, often fail to understand the outcome and the risks of lack of maintenance. The solution to overcome this 'gap of ignorance' is communicating using data science and analytics. Using Jupyter  notebooks, for example, to combine both textual explanations as well as analytics and diagrams can easily communicate the risks of a software to the management. For using data science on software code, one should decide...

I've tried to install pymssql on Windows using command line: pip install pymssql The operation fails with an error: fatal error C1083: Cannot open include file: 'sqlfront.h': No such file or directory while looking for a solution, the first results  are not really helpful, until you arrive to this helpful one . In short: I was using python version 3.6, which currently isn't supported with pymssql. Using python 3.5 instead solves that issue, and the  pip install pymssql runs well. If you're using Anaconda Navigator , it is made even simpler: In the Environment section, you can add a new environment with your choice of python version. When you're choosing a supported version - 3.5 for example - you will find the pymssql in the packages list and install it from there directly.