Introduction

I am originally trainded as an economist with a Finance major and a Statistics minor. I spent 21 years on financial markets as an analyst and asset manager. After a while, though, I got interested in disruptive technologies, mostly data and data science, which are closely linked to my previous academic studies. Statistics is especially close to my heart as I used to teach it to sophomore students on the Budapest University of Economics (currently Corvinus University). To do something about my new passion, I quit my job in 2017 and completed a data science-focused master's program on the Central European University in Budapest (MSc in Business Analytics). The core of the program was Statistics, Machine Learning, some intro to neural networks, and data infrastructure in the cloud. The program was built in R and I also learnt Python (which I like better than R).

I have encountered a lot of interesting things during my studies. I had superinteresting assignments for which I had to dig out a bunch of fancy solutions in the docs, on stackoverflow, and from other people's blogs. This was when I decided to add some more to the already large and rapidly expanding content space on the net, so that I may be able to give some help to those who are looking for those things I was struggling with. Also, some of the things I did was about really cool topics (like text complexity analysis, or clustering on a dissimilarity matrix) and I think they are worth to be shared. Finally, there were some issues which took me months to solve and I hope these posts will shorten this time for those who find my posts during their search.

All blog codes will be on github in their entirety, under https://github.com/peterduronelly/blogcodes.