The Data Science approach

Often colleagues and friends ask me the following questions and very often there isn’t a simple answer that comes to my mind. In this post, I will try and answer some of these

1. Who is a data-scientist? 

To-date the most comprehensive accompanied with a brilliant viz –

2. What is the data science approach?

Exploratory Analysis: Starts with Big Data

Big Data and Broad Data:  


Business Deliverable: 

3. How is it different from business analytics , statistics or research scientists?

DS vs. Statisticians 

Inference vs. Prediction

Structured vs. Unstructured

DS vs. Business Analytics 

Analysis vs. Product/Service

Centralized data vs. Distributed Data

Structured vs. Unstructured

DS vs. Research Scientists

Applied Domain-specific vs. Generic

Algorithm vs. Product/Service

4. Building a data-science team

Finally, having worked on more than a few data-science team configurations, I think careful attention needs to be paid while putting together a team that aligns well with the business. There is not a single working mantra for doing this, but in general my observation is that a successful data-science team very often is not the one that has the most number of data-scientists on it. In general a well functioning data-science team should have a good mixture of the following roles

– The Explorer (‘aka’ a data scientist)

– The Finisher (‘aka’ a software engineer)

– The Researcher (‘aka’ a research scientist)

– The Sanity Checker (‘aka’ the analyst)

– The Communicator (‘aka’ the product manager)


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