As a student preparing for data anaylst & science roles, should I generalize vs specialize?


This question was posted on Springboard forum.

Here’s my answer:

It depends on your target industry & where they are in their life-cycle.

It has four stages: Startup, Growth, Maturity, Decline.

Industry lifecycle

Generalization is great in earlier stages. If you are targeting jobs at startups; generalize. You should know enough about lot of things.

T-shaped professionals are great for Growth stage. They specialize in something but still know enough about lot of things. E.g. Sr Growth/Marketing Analyst. Know enough about analytics & data science to be dangerous but specializes in marketing.

Specialization is great for mature industries. They know a lot about few things. E.g. Statisticians in an Insurance industry. They have made careers out of building risk models.

Any advice for moving into data science from business intelligence?


This was asked on Reddit: Any advice for moving into data science from business intelligence?

Here’s my answer:

I come from “Business Intelligence” background and currently work as Sr. Data Scientist. I found that you need two things to transition into data science:

Data Culture: A company where the data culture is such that managers/executives ask big questions that need a data science approach to solve it. If your end-consumers are still asking bunch of “what” questions then your company might NOT be ready for data science. But if your CEO comes to you and says “hey, I got the customer list with the info I asked for but can you help me understand which of these customers might churn next quarter?” — then you have a data science problem at hand. So, try to find companies that have this culture.

Skills: And you need to upgrade your skills to be able to solve data science problems. BI is focused too much on technology and automation and so may need to unlearn few things. For example: Automation is not always important since you might work on problems where a model is needed to predict just a couple of times. Trying to automate wouldn’t be optimal in that case. Also, BI relies heavily on tools but in Data science, you’ll need deeper domain knowledge & problem-solving approach along with technical skills.

Also, I personally moved from BI (as a consultant) -> Analytics (as Analytics Manager) -> Data science (Sr Data Scientist) and this has been super helpful for me. I recommend to transition into Analytics first and then eventually breaking into data science.

Hope that helps!