1) The document discusses how to think like a data scientist by walking through collecting and analyzing data to answer a question about meeting start times. 2) It recommends starting with an interesting or bothersome work question, defining relevant data to collect, and then gathering that data over a period of time while modifying definitions as needed. 3) The example analyzes meeting start times over two weeks, finds that on average meetings started 12 minutes late with 10% starting on time, and discusses what else the data might reveal and whether others would believe the results.

1. Then think like a
DATA SCIENTIST
Want to join meetings late?
Prepared by: Abhishek Rana

2. You don’t have to
become data scientist..

3. You don’t have to
become data scientist..
You have to become
data literate..

4. You
need
only
this.

5. Let’s plan, what's FIRST?
Start with something interesting or somethings that
bothers you at work..

6. “Meetings always seem to start
late. Is that really true?”

7. Now Think..
What type of
data will
help your
question.
Write down all the relevant
definitions and your protocol
for collecting the data.

8. When meeting starts?

9. When meeting starts?

10. When meeting starts?

11. Third. Collect DATA.
Modify your definition and
protocol as you go along.
You may find that even though a meeting has
started, it starts a new when a more senior
person joins in.

12. Start drawing a picture.
It will make your like
easier.
Use tools.

13. See.

14. Get back from where you started.
Does
this
answer
your
question
?

15. Develop summary statistics..
“Over a two-week period,
10% of the meetings I
attended started on time.
And on average, they started
12 minutes late.”

16. But don’t stop there..
Answer the “so what?”
“Yes, If those two
weeks are typical, I waste an hour
a day. And that costs the
company $X/year.”
“No, meetings start pretty much
on time,” and there is no need to
go further.”
Get a feel for variation.

17. Ask, “what else does the data reveal?”
Meeting starts early when a senior manager is
involved.
Type of meeting?
Are these result
consistent?
Are other believe this
too?
Other relationship?

18. And this is how you start thinking like a
data scientist..