Hi there,

I gather here some selected highlights from my personal development path on becoming a truly versatile data scientist. On my day-to-day work I have built and validated credit risk models for Nordic banks using mainly R. This GitHub account is an initiative to keep my Python skills and analytical curiosity up-to-date.

My main interests lie in analyzing economic and financial data that are publicly available. Aside of that, I also highly enjoy exploring rare events and oddities that might be left unnoticed without digging into some more non-conventional data.

A few years ago, I worked with Nordic countries' educational and economic data on NUTS 3 (i.e. county) level to run a few regressions for my master's thesis. This made me interested about the possibilities for geospatial analysis using geojson data on R. As is often the case, I got carried away and developed a script to plot similar data also on country, municipality and zip-code level. The product of this is visible in the Karttaplotit repository.

Lately, I've grown an interest towards financial markets and the immense amount of publicly available data there is. This has made me to study the process of building interactive and nice-looking dashboards using Python. I've included this project in the repository Portfolio_analysis_dashboard.

Currently, I'm re-familiarizing myself with Python's fundamental Pandas and Plotly libraries. The progress is visible in the Corona_analysis repository. As is clear from the repository name, I am using the official daily WHO data in the analysis.

My future self-improvement goals include bringing the key concepts of the mostly used python libraries back to my mind as well as learning the basics of app and website development.

Here are some insightful stats about my GitHub activity. It seems that Python is currently my favorite tool although I am hoping to increase my web content using HTML and CSS.