This presentation covers data science buzz words, big data introduction, predictive analytics, and model building methods. Structured vs unstructured. Supervised learning vs unsupervised learning.

1. Ben Taylor @bentaylordata
Predictive Analytics / Data Science

2. Presentation Objectives
• Enable you to be smarter than your prospect (data history / lingo)
• Motivate you to be unstoppable and hyper-confident
• Motivate you to begin looking for data driven opportunities
• Motivate you to become a data scientist

3. "What the hell is cloud computing?"
-Larry Ellison, CEO Oracle

4. What is cloud computing?
?

5. What is big data?
 Big data includes datasets or problems which exceed the
capacity of a single computer and require a distributed data
access system.
 The concept of "big" is relative to the conventional systems
and technology and is subject to change in the future with
advances in memory and storage solutions.
http://www.pcmag.com/article2/0,2817,2453838,00.asp

6. Big data trends

7. What is a data scientist?

8. What is a data scientist?
Engineering Finance Economics Mathematics Computer Science Physics
Data Science
6-10yrs
Python Bootcamp $8,000 (3 months)
$16,000-$4,000 (3 months)
$115K avg

9. What is a data scientist?

10. What is a data scientist?
Master Builder

11. What is a data scientist?
Reality distortion: Hyper-confidence

13. Data Scientist = Peacock

14. @bentaylordata
Humans Algorithms
VS

15. Smartest pirate
0 20 40 60 80 100 120 140 160 180 200
0
200
400
600
800
1000
1200
1400
Ships captured
Treasurechests

16. Humans Algorithms
VS
0 20 40 60 80 100 120 140 160 180 200
Ships captured
NA

17. Humans Algorithms
VS
0 20 40 60 80 100 120 140 160 180 200
Ships captured
German (1795), French (1806)
0 20 40 60 80 100 120 140 160 180 2
0
200
400
600
800
1000
1200
1400
Ships captured
Treasurechests

18. Humans Algorithms
VS
1997, IBM deep blue
Kasparov

19. Humans Algorithms
VS
2011, IBM Watson
Ken Jennings & Brad Rutter

20. Humans Algorithms
VS
2014, HireVue Iris
Hiring Panel

21. Prediction process
Raw data
Data munging
Training
Model

22. Data
munging
Prediction process
Raw data
Feature selection
Training
Model
Data cleaning
Clean data

23. Numeric Excel example
@bentaylordata

24. Data
munging
Prediction process
Raw data
Feature selection
Training
Model
Data cleaning
LSR, SVM, RANDOM FOREST,
NAÏVE BAYESIAN, NEURAL NET

25. Missing values + categorical
@bentaylordata

27. Data
munging
Prediction process
Raw data
Feature selection
Training
Model
Data cleaning
LSR, SVM, RANDOM FOREST,
NAÏVE BAYESIAN, NEURAL NET
Retail > 15, Engineering > 95
> 5.67

28. Resume model

29. Resume model

30. Data
munging
Prediction process
Raw data
Feature selection
Training
Model
Data cleaning
LSR, SVM, RANDOM FOREST,
NAÏVE BAYESIAN, NEURAL NET
Retail > 15, Engineering > 95
GPA, Colleges, Hobbies
> 5.67

31. Text deeper dive

32. Sentiment example

33. Sentiment example

34. Sentiment

35. Given data, find cat? dog?
@bentaylordata

36. Talk like a data nerd
@bentaylordata

37. Confidence & Over-fitting

38. Confidence & Over-fitting

39. Data Lingo
 Supervised vs unsupervised learning
 Supervised: Training set provided.
 Unsupervised: No training set, clustering based on
similar attributes.

40. Data Lingo
 Analytic Layers
 Descriptive Analytics: Telling a data story, plotting, or
visualization.
 Predictive Analytics: Predict future outcomes, usually
trained on a historical training set
 Prescriptive Analytics: Using the insight from your
predictive model to proactively change something
 Interview/Interaction Analytics: Any analytics
surrounding the interview or interaction.

41. Data Lingo
 Prediction methods
 Regression: Predicting a continuous output (stock)
 Classification: Predicting discrete category outputs.
i.e. Yes/Maybe/No

42. Data Lingo
 Data Types
 Structured: Does it play well in Excel?
 Unstructured: Raw text (Twitter), audio, video,
photos, resumes, etc…