Machine Learning is a super active field of research and massively used across basically all industries. It has become part of our daily live, especially the digital live, though it stays invisible and in the background mostly.

But what is Machine Learning? Can it solve any algorithmic problem? What are the most common classes of algorithms, and for which applications are they suitable? Check out this overview.

Data Scientists are scarce. At least full-stack Data Scientists that cover all the skills needed for succesfull Data Science projects. High-performing experts, being able to handle advanced machine learning algorithms, deploying trained models in a containerized ecosystem in the cloud with proper architecture, security measurements and scalability, all based on a well defined use case with high business impact while handling all stakeholders and keeping on track with the project plan and all agreed deliverables.

Sound like magic? It is. So let's find alternatives.

In contrast to 2015, when the Data Science hype was rolling across Germany, we now do have more and more universities offering specializations or whole degrees in Data Science and tightly related disciplines. Yet, still many aspiring Data Scientists have completed different studies, e.g., computer science, physics, mathematics or economics. To be honest - I highly appreciate the diverse backgrounds of Data Scientists I have been collaborating with.

Coming from a non-DS field, you'll need to do some additional homework in order to keep up with ML natives. But in the age of MOOCs (massive open online courses), there are enough offerings. I'll try to provide an overview and give some guidance. 

Data Scientist are pack animals - they need a team to develop and achieve their maximum productivity. Leaving them as individual fighters will turn them inefficient, stuck too often, lost in complicated projects - and most likely lead to churn. But how do you build a Data Science team? How do you achieve a good skill mix? And how do you create a culture of creativity, open-mindedness and productivity? There is not the "one size fits all" answer, but at least there are some clear guidelines.