“The power of Open Source is the power of the people. The people rule”: Philippe Kahn
Ever since my doctoral studies that mostly entailed performing statistical analysis in R ( and admittedly Octave/MATLAB), I have strongly embraced the emergence of Python as the lingua franca amongst machine learners / data scientists / *insert latest profession-buzzword here*.
My daily workflow involves quickly reacting to the vagaries of messy real-world data, in all it’s naive-assumption-shattering glory. One major difference between graduate school and industry to me is the conquest of the inner-ego that goads you to implement algorithms from scratch.
Once past the white-boarding/hypothesis building phase I quickly parse through the PyPi repository to check if any of the constituent modules have already been authored. This is typically followed by a
>> pip install *PACKAGE_NAME*ritual and voila, I find myself standing on the shoulders of the open-source giants whose careful work I am now harnessing to scale the DIKW pyramid.