2.3 Three Pillars of Knowledge

  1. Analytics knowledge and tool sets

A successful data scientist needs to have a strong technical background in data mining, statistics and machine learning. The in-depth understanding of modeling with the insight about data enable a data scientist to convert a business problem to a data science problem.

  1. Domain knowledge and collaboration

A successful data scientist needs some domain knowledge to understand the business problem. For any data science project, the data scientist need to collaborate with other team members and effective communication and leadership skills are critical, especially when you are the only data person in the room and you need to decide with uncertainty.

  1. (Big) data management and (new) IT skills
The last pillar is about computation environment and model implementation in a big data platform. This used to be the most difficult one for a data scientist with statistics background (i.e. lack computer science or programming skills). The good news is that with the rise of cloud computation big data platform, it is easier for a statistician to overcome this barrier.
Comparison of Statistician and Data Scientist