This document provides guidance on how to start thinking like a data scientist. It recommends starting with a question that interests you and developing a plan to collect relevant data to answer the question. It stresses the importance of trusting the data collected and addressing any gaps. The document advises visualizing the data through pictures to help understand and communicate findings. It also emphasizes determining the significance of the results by asking "so what?" and ensuring findings are both interesting and important before concluding the analysis.

1. HOW TO START THINKING LIKE A
DATA SCIENTIST

2. FROM UNDERSTANDING VARIATION TO VISUALIZATION.
open your eyes to the millions of
small data opportunities, and
enable your work a bit more
effectively with data scientists,
analytics, and all things
quantitative.

3. 1) ITS NOT THAT
TOUGH TO START !
Start with something that interests,
even bothers you

4.  think through the data that can
help answer your question, and
develop a plan for the solution.
 Write down all the relevant
definitions and your protocol for
collecting the data.

5. COLLECTTRUSTFINDGAPS
Now collect the data.
It is critical that
you trust the data.
And, as you go,
you’re almost certain
to find gaps in data
collection.

6. Sooner than we think,
some pictures will be
drawn.
Good pictures make it
easier to understand the
data and communicate
main points to others as
well.

7. 2) ANSWER THE
“SO WHAT?”
Don’t stop here!

8. MANY ANALYSES END BECAUSE THERE IS NO
“SO WHAT?”
It often happens there is no problem,
and thus no so what!(LUCKY IF
THAT’S THE CASE)
But more often it happens that, we
don’t know what to do with all the
data and effort!

9. Some analyses require us to get a feel
for variation.
Understanding variation leads to a
better feel for the overall problem,
deeper insights, and novel ideas for
improvement.

10. MANAGERS NEED TO PLAY
THEIR PART

11. RESULTS NEED TO PASS BOTH THE
“INTERESTING” AND “IMPORTANT” TEST.
 Workers need to be given assurance that
their work Is useful and meaningful!

12. On the company level, results so far
only pass the interesting test. We
don’t know whether results are
typical.
Managers should support and
provide the impetus on helping
people work on their data.

13. Managers have to pass the
messages and ask
A)Are results consistent with
others’ experiences in the
company? Is some data worse
than others?

14.  b)if not Return to step one, pose the next group of
questions, and repeat the process. Keep the focus
narrow — two or three questions at most.

15. GLOSSARY
 How to Start Thinking Like a Data Scientist by
Thomas C. Redman (Nov 2013)
 https://www.google.co.in/search?biw=1366&bih=6
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