Talent over CVs

As a young and dynamic startup, we are continuously looking for great new talent to join our team of data scientists. But talent is hard to find in a pile of CVs and, as a data science company, it seemed logical to use a data-driven approach to asses applicants. That's why we designed three simple data science challenges (which you can find on GitHub). The 3 different tasks target the different objectives of our company:

• Data Analysis and Visualisation
• Data Modelling, Machine Learning, and Prediction
• Web Development

Each of the tasks is designed to see which programming style you use and how well you document and communicate your code. Code that is not only of high-quality, but also is well-documented and easy to understand is our priority. Please take extra care that you push a polished version of your code.
Second, quality comes before quantity. The objective is not to find the best overall method, so please focus on a single approach rather than trying several methodologies. Remember that you work on an unknown dataset, so don't assume too much. Just try to satisfy the requirements of specialised methodologies.
Finally, we are always happy to see people addressing all three challenges at once, but this is certainly not required!
But enough of the instructions and let's showcase some great examples which we have received:

Cluster analysis of energy consumption data

credit: Dimitry Foures

credit: Philip Squires

Predicting energy production using Bayesian networks

credit: Jan Teichmann