Need of data scientist.

1. Stepping into the
shoes of
A Data Scientist
By Pratyush Sunandan ooo

2. What is a
Data Scientist

3. A Data scientist is a person who has the
knowledge and skills to conduct sophisticated
and systematic analyses of data. A data scientist
extracts insights from data sets for product
development, and evaluates and identifies
strategic opportunities.

5. Disadvantages of NOT being a
Data-Sapiens
Managers who aren’t data savvy,
who can’t conduct basic analyses,
interpret more complex ones, and
interact with data scientists
Companies without a large and
growing cadre of data-savvy
managers

6. An Exercise

7. First, start with something that interests, even bothers, you at
work, like consistently late-starting meetings. Whatever it is,
form it up as a question and write it down: “Meetings always
seem to start late. Is that really true?”
Play with Data: Be a Data Literate
Fun Fact
One does not have to be a data scientist or a Bayesian
statistician to tease useful insights from data.

8. A Typical Case
Starting with something that interests, even bothers at work-
Consistently late-starting meetings
“Meetings always seem to start late. Is that really true?”
Think
Through
the Data
Develop
a Plan
Write
Relevant
definitions
& modify
when
required
Collect
the Data
and Find
Gaps in it
Draw &
Model
Pictures
Develop
Summary
Statistics
Explore
Variations
and
Variables

9. Make a Rough Sketch
 Good pictures make it
easier to both understand
the data and communicate
main points to others.
The fig. is a time-series plot,
where the horizontal axis has the
date and time and the vertical
axis has the variable of interest.
Thus, a point on the graph is the
date and time of a meeting
versus the number of minutes
late.

10. Answer the “so what?” question. In this case,
“If those two weeks are typical,
I waste an hour a day. And that costs
the company $X/year.”
Develop Summary Statistics
Have you discovered an answer?
In this case, “Over a two-week period, 10% of the meetings I attended started on time. And on
average, they started 12 minutes late.”
If 80% of meetings start within a few minutes of their
scheduled start times, the answer to the original question is,
“No, meetings start pretty much on time,” and there is no need
to go further.
BUT
DON’T
STOP

11. Get a Feel for variation
Understanding variation leads to a better feel
for the overall problem, deeper insights, and
novel ideas for improvement.
Note on the picture that 8-20 minutes late is typical. A few
meetings start right on time, others nearly a full 30 minutes
late. It might be better if one could judge, “I can get to
meetings 10 minutes late, just in time for them to start,” but
the variation is too great.

12. What else does the data reveal?
Fun Fact
Five meetings began exactly on time, while every other
meeting began at least seven minutes late. In this case,
bringing meeting notes to bear reveals that all five meetings
were called by the Vice President of Finance. Evidently, she
starts all her meetings on time.
On the company level, results so far only pass the interesting test.
You don’t know whether your results are typical, nor whether others can be as hard-nosed
as the VP when it comes to starting meetings. But a deeper look is surely in order:

13. Explore Other Variables
 Are your results consistent with others’ experiences in
the company?
 Are some days worse than others?
 Which starts later: conference calls or face-to-face
meetings?
 Is there a relationship between meeting start time
and most senior attendee?
?
Return to step one, pose the next group of questions, and repeat the process. Keep the focus narrow
— two or three questions at most.

14. Have Fun With Data
Many find a primal joy in data
Hooked once, hooked for life
There are fewer
and fewer
places for the
“data illiterate”
and no more
excuses.