Why a Data Scientist is not another
The different types of orientations of a data scientist
About a month back I wrote on the evolution of data engineering, which as a role has been rapidly evolving in terms of requirements and decided to take a look at what is a data-scientist in 2018.
Looking at the current requirements and orientation within five key axis of data science, namely: Data Acquisition, Experimentation, Predictive Modeling, Data Vizualization and Productionization. We can see that there is a stratum of roles and functions within data science and that more often than none a data scientist is not another in terms of job function.
Data Acquisition
Within the real of data acquisition, I can see three different types of advanced orientations for data scientists digital analytics, backend engineering and data engineering.
For those more oriented towards UX and digital analytics, the focus on data acquisition tends to gravitate towards the creation of custom logging in google analytics, the implementation of data layers and different tags. The implementation knowledge of custom events and of tag…
