 I am a graduate student at McGill University doing experimental neuroscience and so that means that I work with a different kind of mouse. I record from their neurons, typically I record from their autonomic neurons and I get their communications between the neurons. I use amplifiers, oscilloscopes, electrodes and I was taught how to do all of this. What I wasn't taught is what I do with the data once I have it. Really I was given three main options of what tools I could use for my data analysis. Excel, a program called EGOR Pro which is what we use for data acquisition. It also has a full-fledged data analysis package, it uses its own language or MATLAB is common in the neuroscience community. I am a strong advocate for reproducible science and that means reproducible data analysis and so that automatically rules out Excel and it meant that I needed to learn how to program. No one in my lab knew how to program in EGOR. No one knew MATLAB either so I decided to use an easier, more friendly language. I wrote my data analysis in Python. I mostly use the scientific Python libraries, NumPy, SciPy, a little bit of machine learning with Scikit-learn, but importantly I found this package EGOR packed. Someone had the same problem as me where they had their data from an EGOR file that they needed to extract into a form that Python could use and it was really only because that was available that I was able to do my data analysis in Python. Now what I learned by doing my data analysis in Python by using programming in general and this could be just from a novices perspective, but I found that it really gave me different perspective from my data. Science data is implicitly noisy, but using Python and using programming allowed me to put constraints on some of that noise and understand my data a little bit better. I think that the same is true the other way. To understand where the data is coming from and your end users would give you more perspective on the types of programs that you're writing. Now I went from the bench to a little bit of the computer lab and I did find some barriers in that transition. Mostly the incentive to change was a bit of a barrier. But I found in the Python community there's really so many groups that helped me overcome some of the barriers. The Montreal Python Meetup Group and PyCon that was here in Montreal, stack overflow to ask and answer questions, codes available on GitHub, software carpentry has specific boot camps geared towards scientists and there's some really good MOOCs on edX that really give non-programmers some of the basics of programming. So that's it. If you want to get in touch with me at all, there's my information.