I am a freelance developer with over 15 years of expirience. My weapons of choice is primarly python and recently node.js, but I do not hesistate to dive into html/css/js and I strive to be a full-stack developer.

Being initially self taught, I started my way up from visual basic and the ADO/DAo world and Fortran 77 for numeric simulations. Upon discovering python (version 2.3) I started using it for all kind of automation tasks: building reporting pipelines, exporting measurement data from the bare-bones instruments into excel or similar user-friendly reporting tools. After switching jobs, I began building web sites with python as well, using Django, Flask and even Bottle. Meanwhile I used python to find, scrape, analyze, present and deliver all sorts of data.

I prefer minimal, simple web sites - responsive, mobile-first, clean and to the point - built using CSS, SASS, HTML5 & some JS.

A couple of years ago I was able to introduce some data analytics / data science into my work using the python data stack:

  • pandas
  • numpy
  • matplotlib / bokeh / plotly / seaborn
  • scikit-learn

After a while, I started using the R ecosystem and Wickham’s dplyr and tidyr wonderful packages.

Finally, a couple of years ago, I expanded my knowledge by discovering the wonderful world of server-side javascript and the whole bunch (Express.js, gulp. grunt, passport, MongoDB).

While I have never worked as a full-time developer (I am an analyst at heart), I love to devour books, articles, tutorials, full-blown courses, speeches and conferences about analytics, web development, data science, algorythms and similar topics.

To wrap it up in a list:

  • web apps and sites (small, medium, big, deployment on Heroku, DigitalOcean, Nginx etc)
    • django, flask, express.js, anything really except php
  • rest apis (django-rest, flask-rest, hand-made, node-based, authentication, authorization)
  • data analytics & data science projects:
    • obtaining data (including scraping techniques)
    • data munging and ETL pipelines, transformations, projections (including the fancy stuff: LDA, SVD etc)
    • exploratory data analysis - R and Jupyter notebooks
    • algorithms (from linear regression to random forests)
    • hypothesis formulation and testing, A/B testing, Bayesian analysis
    • modelling and deployment of models
    • final deliverables: reports, charts, web apps, APIs
  • data vizualization
    • tableau / Power BI
    • D3.js
    • python based

If you believe I could help you out with your project, do not hesitate to drop me an email (it is hidden in the header of this website).