It is an ongoing discussion what the difference between analytics and data science exactly is. It is a blurry line, but I think the infographic from the American University pretty much nails it.
The key opinion it reflects is that a data scientist should spend most of its time creating and programing new algorithms, while a business analyst should rather focus on applying and utilizing the existing ones.
Nevertheless there are some interesting points here as well, such as:
- Years of education are not really different, but the disciplines are. Data Scientists tend to have degrees with more rigorous mathematical training. I think and actually kind of know from a personal experience that this is the key differentiator. Also this is what makes it at times more difficult for a business analyst to become a data scientist.
- It appears financial institutions prefer business analysts while the government and colleges prefers data scientists. I’m not exactly sure why is it so.
- Business analyst jobs are projected to grow faster than data scientists (27% to 15%). While the this trend might sound strange I’m not surprised. I also talked about on this blog about the importance of having business people who can actually apply and monetize the algorithms created by data scientist. While good data scientists are rare and we are very far from having enough of them to really digest all available data, I still believe that there is a way bigger gap in applying data products and commercializing them.
If you think it missed something when it comes to differences, please let me know in the comments.