This document summarizes steps for thinking like a data scientist, including posing questions, collecting relevant data, analyzing the data through visualizations and statistics, interpreting the results, and communicating conclusions. It emphasizes that data literacy is important for business managers to effectively collaborate with data scientists and uncover metrics to improve performance. The process of thinking with a data-driven mindset will help managers in India stay competitive as data becomes more integral to decision making across industries.

1. REVIEW ON THE ARTICLE-
How to Start Thinking
Like a
Data Scientist

2. YOU DON’T HAVE TO BE
A DATA SCIENTIST OR
A BAYESIAN STATISTICIAN
TO TEASE USEFUL INSIGHTS FROM
DATA

3. KEY STEPS:
• Become data literate, open your eyes to the millions
of small data opportunities, and enable you work a bit
more effectively with data scientists, analytics, and all
things quantitative.
• Start with something that interests. Whatever it is,
form it up as a question and write it down.
• Next, think through the data that can help answer
your question, and develop a plan for creating them.
Write down all the relevant definitions and your
protocol for collecting the data.

4. • 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. Modify your definition and protocol as you go
along.
• Sooner than you think, you’ll be ready to start drawing
some pictures. Good pictures make it easier for you to
both understand the data and communicate main points
to others.
• Now return to the question that you started with and
develop summary statistics.
• Answer the “So what?” question.

5. • Get a feel for variation. Understanding variation leads to a
better feel for the overall problem, deeper insights, and
novel ideas for improvement.
• Now ask, “What else does the data reveal?”
• Repeat the same analysis steps and keep the focus narrow.
You will surely get your answer!

7. FOR A BUSINESS-

8. SIMPLE APPROACH
1.Posing questions.
2.Making observations and inferences.
3.Developing hypothesis.
4.Designing experiments.
5.Making measurements and collecting data.
6.Drawing conclusions.
7.Effectively communicating the conclusions.

10. The “thinking like a data
scientist” framework will help the
business stakeholders to collaborate
with data scientists to uncover those
variables and metrics that can improve
business performance and drive
business and financial value.

11. Why and how are these insights relevant to a manager in
India?
Slowly but steadily, data are forcing their way into every
nook and cranny of every industry, company, and job.
Managers who aren’t data savvy, who can’t conduct basic
analyses, interpret more complex ones, and interact with
data scientists are already at a disadvantage. Companies
without a large and growing cadre of data-savvy
managers are similarly disadvantaged.

14. CONCLUSION:
There are fewer and fewer
places for the “data illiterate”
and, in my humble opinion, no
more excuses.

15. THANK YOU
BY- TANAY KARNIK