Communicating Estonian data science better

There’s a lot of interest towards data science in Estonia today, and plenty of people making use of data science tools, but not much of this knowledge is publicly shared. Communicating better the work we’re doing now would inspire more people to consider data science as an area worth going deeply into – either as a career or to learn useful tools. In addition to drawing in people from the outside, it would be interesting and helpful to know what others within the community are working on.

To be more blunt: we need to publicise data science better. This includes:

  • sharing the benefits of using data science in companies and NGOs, in science, and in government,
  • sharing concrete projects we’ve developed – from dashboards and visualisations to ML pipelines,
  • sharing our opinions of using data science and machine learning, and their implications for society.

Concrete ways of doing the above include writing (blog posts, technical reports, etc), presenting (to students, to other practicioners, to the general public), and any other way of communicating both practical and coolness value of data science.

It’s useful to have a central place to share this content, which is why I propose doing this in the (newly started) blog. Content can be published elsewhere; this blog can also act as an aggregator.

Given the above, I’m looking for multiple volunteers to fill the following roles (ideally multiple people per role). If you’re interested, shoot me an email at!

If you have another project in mind that would develop data science in Estonia, do share that as well.

Contributor / blogger


  • Writing something. The options are plentiful:
    • a blog post, opinion piece or essay (e.g. “why Python beats R”),
    • a technical report of your work (e.g. “how I built my first image search engine”),
    • an interview with an Estonian of foreign practicioner, or another person of interest,
    • summary of a talk at a meetup (e.g. presentations from Machine Learning Estonia or Data Science seminars in Tartu),
    • introduction to an important idea (e.g. basic rules of visualisation, or balancing precision and recall),
    • etc.
  • Producing videos, infographics or other media. Interviews and panel discussions work great on video, and images/infographics can work great to give a high-level understanding of what data scientists do.
  • Publishing it on (in markdown). Another good option is to publish on your own blog/website/Medium, and add a snippet on that refers to your original post – your choice.

Some of the above can be easily done by someone with no experience but an interest in data science, e.g. a student. The author can choose the language – Estonian or English –, and will get support in the form of editing and feedback from Taivo (and possibly other volunteers).

Effort: as much as you’re willing to put in; you can publish as often and as regularly as you like.

Designer / web developer operates on top of Jekyll and a very basic template with some modifications – see the public GitHub repo.

Tasks: review and rewrite the the website to make sure it works on all devices, works consistently well, and is easy to share. This can be as simple as choosing a good Jekyll theme and customising it a bit. Most choices are up to you but we’d need to synchronise on the important stuff.

Effort: 5-20 hours total.