The document discusses the importance of exploratory data analysis (EDA) as a foundational element of data science, emphasizing its role in scientific inquiry and hypothesis testing. It highlights key insights from John Tukey, who is recognized as the father of modern data science, and promotes the use of Jupyter Notebook as a tool for iterative data workflows. The document serves as a guide to EDA techniques and resources for further exploration.

1. Alfred Essa, Shirin Mojarad
McGraw-Hill Education
shirin.mojarad@gmail.com
twitter: @shirinmojarad
Exploratory Data Analysis

2. ::
Learning Science Company

3. ::
intelligent, personalized, adaptive
software

4. Data Science

5. Sir Arthur C. Clarke
1917-2008
“Any sufficiently advanced technology is
indisguishable from magic.”

6. Three Takeaways
• Science (Structure)
• Exploratory Data Analysis
(Elements)
• Killer Application for EDA
(Demo)

7. Grammar of
Science

8. Questions
NATURE
Experiments Data
Iterate
Predictions!
Interpret
Natural Laws
Hypotheses

9. Exploratory
Data
Analysis

10. John Tukey
1915-2000
Father of Modern
Data Science

11. Exploratory vs Confirmatory
Data Analysis

12. “Exploratory data analysis is
detective work.”
“Exploratory data analysis
can never be the whole
story, but nothing else can
serve as the foundation
stone.”

13. “There is no excuse for
failing to plot and look (if
you have graph paper).”
“Graphs are friendly. Graphs force us
to note the unexpected, nothing could
be more important…There is no more
reason to expect one graph to “tell all”
than to expect one number to do the
same ”

14. Coined the term “software” and also the term “bit”

15. Questions
Domain
Exploratory
Data
Analysis
Models
Data
Iterate
Confirmatory
Data
Analysis
Predictions!
Visualizations

16. Is there a tool that
enables this iterative
workflow and which can
be shared with other
researchers and teams?

17. Jupiter
Notebook

18. Introduction &
Demonstration

19. Goo.gl/2jSCwQ

20. Complete notebook: goo.gl/Qib7Vz
Check out more tutorials: http://alfredessa.com