1. The document discusses how to think like a data scientist based on an HBR article. It provides two key takeaways: 2. The first is that thinking like a data scientist is accessible, involving breaking problems into chunks, identifying relevant metrics, and collecting data with quality controls to gain understanding and drive analysis. 3. The second takeaway is that data alone is not enough - insights must be actionable, validated by other sources, and used to evangelize behaviors to continuously improve the problem-solving process and findings.

1. How to think like a
Data Scientist?

2. Based on the HBR article ‘How to Start Thinking
Like a Data Scientist’
by Thomas C. Redman

3. 2 Key Takeaways

4. 1.
Thinking like a Data
Scientist is no Rocket
Science

5. Especially in an era where data is flowing
from every nook & corner

6. Start with a cause that matters to you and
break the problem down into chunks you can
easily solve piecewise

7. Think about KPIs which would help your cause and
how you can collect enough data points to answer
your key questions

8. Start collecting data and deploy proper QC
procedures in place so that you have enough trust
on your numbers

9. Graphical representations such as line charts, area
charts, scatter plots can help you have a better
understanding of your data set and make you more agile
through the analysis

10. 2.
Data is great, but data by itself is
not enough. Dive deep to uncover
actionable insights

11. Data is most useful when analytical findings drive
behavior to achieve the desired outcome

12. Look for a validation source to QC your results,
because no solution is better than a wrong one!

13. Evangelize the behavior required to solve your
cause and continue to look for deeper insights,
indirect correlations, and look for ways to improve
the dataset

14. Stay on a look out for help from domain experts
and accelerate the whole process

15. Disclaimer:
This presentation was created by Raminder
Singh, under an internship by Prof. Mathur,
IIM Lucknow