In light of recent changes which have significantly altered the way we work and spend our time, my job at St. Jude has transitioned into a predominantly digital form, requiring me to focus on developing programs for the purpose of analyzing proteomic data.
While my background in Biochemistry and Molecular Biology is rather extensive, there has been a bit of a learning curve in terms of applying programming knowledge and mathematical concepts to analyzing data
So, why make a blog?
- This blog will serve both as a place where I can document my course of learning, as well as provide readers with applicable examples and techniques
- By following along with both the method and the exercises, just as my skills have improved significantly in just a short period of time, so too will yours
- The amount of resources on the internet regarding computer science are vast. I hope to synthesize the information which I have found useful specifically as it relates to data science
A majority of my coding relies on Python (I tend to favor using IPython for math modeling and Atom as a primary text editor) and so these will be the primary tools implemented herein. There are several items which will be focused on throughout, which I spend time investigating every day:
- Learning new Python code techniques
- Learning new Python platforms/libraries (ie. Numpy, Pandas, MatPlotLib, etc.)
- Learning new mathematical concepts (ie. Multivariate Calculus, Linear Algebra, Algorithms, and Statistics)
- Spending time creating new code based upon the concepts I have learned that day
By creating a relationship between new knowledge and the application of such, I have been able to put into practice both the theory and nuances of various aspects of several subjects simultaneously
I hope this blog and the contents therein can be a useful tool for you as you endeavor to become a better programmer!