Welcome to The Full Stack Data Scientist, a place where data scientists from all backgrounds and with all skill levels can connect, hopefully learn new aspects and insights of data science, and can further develop. My name is Philipp Singer, a data scientist living and working in Vienna. During my career in both scientific, as well as corporate fields, I have observed the need of a more full-stack view on data science. In today’s information age, you can learn about data science in many diverse ways. Out of many examples, you can study it at university, you can do online MOOCs, you can participate in online courses, or you can study it yourself by simply doing it. However, while many of these resources teach multiple aspects of the already very diverse field of data science, they often neglect that the field even requires a more 360° view.
As an example, suppose you now have finished your data science studies, and start your career in a company. Your first task is to develop a predictive model for internal purposes. However, as you are probably one of the few data experts in the company, there are no pre-defined processes and structures on how to proceed meaning that you need to find your own ways. From university, you are quite prepared on the actual data science work of doing data preparation, developing the model, and evaluating it. Yet, there is more to it. For starters, you need to meet with stakeholders and business departments, in order to better understand the requirements, then you need to define and plan the project, then you need to acquire the data, then you can only do the actual coding work, before you finally need to also develop interface, service connections, and deploy your model. Following this rough process from start to finish, is what I defined as a full stack data scientist. So even though this term is motivated by the definition of a full stack web developer, it covers an even wieder definition in our context.
We will properly define the idea of a full stack data scientist as well as the rough process we aim at following, in following posts. Further articles of this website will then cover all areas of this process in varying granularity, specifity, and complexity, without specific order. So I wish you a lot of fun with this blog and I hope we can all learn new things together.
Also published on Medium.