The use of machine learning and data mining to create value from corporate or public data is nothing new. It is not the first time that these technologies are in the spotlight. Many remember the late ‘80s and the early ‘90s when machine learning techniques—in particular neural networks—had become very popular. Data mining was at a rise. There were talks everywhere about advanced analysis of data for decision making. Even the popular android character in “Star Trek: The Next Generation” had been named appropriately as “Data.” Data science has been the cornerstone of many data products and applications for more than two decades, e.g., in finance, Telco, and retail. Credit scores have been in use for decades to assess credit worthiness of people when applying for credit or loan. Sophisticated real-time fraud scores based on individual’s transaction spending patterns have been used since early ‘90s to protect credit card holders from a variety of fraud schemes. However, the popularity of web products from the likes of Google, Linked-in, Amazon, and Facebook has helped analytics become a household name. Every new technology comes with lots of hype and many new buzzwords. Often, fact and fiction get mixed-up making it impossible for outsiders to assess the technology’s true relevance. Due to the exponential growth of data, today there is an ever increasing need to process and analyze big data which has required a rethinking of every aspect of the data science life cycle, from data management, to data mining and analysis, to deployment. The purpose of this talk is first to describe what data science is and how it has evolved historically. Second, I share my own experiences as a data scientist across different industries and through time with the audience emphasizing the challenges and rewards.

1. Khosrow Hassibi, PhD
CWRU EECS Seminar Series

3. 3

5. • "Data Science(DS)" is nothing new but the term itself and the
recent level of interest in it.

6. 6

9. • Data mining
• Predictive analytics / Advanced analytics
• Machine learning

11. Machine Learning (ML) Traditional statistics (TS)
Goal: “learning” from data of all sorts Goal: Analyzing and summarizing data
No rigid pre-assumptions about the problem and data
distributions in general
Tight assumptions about the problem and data
distributions
More liberal in the techniques and approaches Conservative in techniques and approaches
Generalization is pursued empirically through training,
validation and test datasets
Generalization is pursued using statistical tests on the
training dataset
Not shy of using heuristics in approaches in search of
a “good solution”
Using tight initial assumptions about data and the problem,
typically in search of an optimal solution under those
assumptions
Redundancy in features (variables) is okay, and often
helpful. Preferable to use algorithms designed to
handle large number of features
Often requires independent features. Preferable to use
less number of input features
Does not promote data reduction prior to learning.
Promotes a culture of abundance: “the more data, the
better”
Promotes data reduction as much as possible before
modeling (sampling, less inputs, …)
Has faced with solving more complex problems in
learning, reasoning, perception, knowledge
presentation, …
Mainly focused on traditional data analysis

15. 15
CompetitiveAdvantage
Analytic Sophistication
Std.
Reports
Ad hoc
Reports
OLAP
Drill Down
Dashboard
& Visualize
Alerts
Statistical
Analysis
Forecasting
Predictive
Modeling
Optimization
What Happened?
What Happened specifically?
Where exactly is the problem?
What is happening overall?
What actions are needed?
Why is this happening?
What is the trend?
What will happen?
What is the best that can happen given constraints?
Advanced Analytics:
Predictive & Proactive
Basic Analytics:
Descriptive & Reactive

16. 16

18. • Speed of processing response
advanced analytics
real-time
• Data preparation
advanced
analytics
18

19. 19
Organization
Category
Information
Management
Proficiency
Analytics
Proficiency
Data Culture
Aspirational Low Low
Line of business
driven
Experienced Medium Medium
Moving toward
enterprise driven
Transformed High High Enterprise driven
Source: MIT Sloan, “Analytics: The Widening Divide,” 2011.
• Three progressive levels of analytics sophistication

20. Data
Scenario
Big Data? Storage Analysis Business Value
1 No Standard Standard Known
2 Yes Possible Nonstandard Somewhat known
3 Yes Possible Not possible Not known
4 Yes Not possible — Not known
20
• Big Data Scenarios in Transformed or Experienced Analytics
Environments

21. 21
Platform Architecture Storage
1 Workstation Multicore Local
2 Enterprise Server SMP Shared
3 Cluster or Grid CCSS Shared
4 General MPP Database SN Distributed data
5 Hadoop SN Distributed data
6 MPP Analytics Appliance SN Distributed data
7 MPP In-Memory Analytics
Appliance
SNIM Distributed in-memory
data (volatile)
SMP: Symmetric Multi-Processing
SN: Shared Nothing Distributed Computing
CCSS: Cluster Computing with Shared Storage
SNIM: Shared Nothing In-Memory Distributed Computing

23. 23

25. 25

26. • Myth #1:
• Myth #2:
• Myth #3:
• Myth #4:
27
Aggregations, Joins,
Sorts, Transformations
v

27. 28

29. 31
Product holdings
Banking tenure
Account Balances
Checking account
Data
Demographic/
Formographic data
Web Data
ATM
transaction
Discount
Brokerage Data
Online/Bill pay Data
The warehouse/data lake hosts data from different sources which provide a
comprehensive view of customer information.
Call Center Data
Other Accounts
Data
Savings Account
Data
Marketing
Response Data ……..

30. 32

31. 33

32. 34

33. 35
Debt<10% of Income Debt=0%
Good
Credit
Risks
Bad
Credit
Risks
Good
Credit
Risks
Yes
YesYes
NO
NONO
Income>$40K
Development ADS
Production
ADS Model
Model
Development
Model
Deployment
Scores

34. 36
Debt<10% of Income Debt=0%
Good
Credit
Risks
Bad
Credit
Risks
Good
Credit
Risks
Yes
YesYes
NO
NONO
Income>$40K
Development ADS
Production
ADS Model
Model
Development
Model
Deployment
Scores
Data Store/
Warehouse

35. 37
Debt<10% of Income Debt=0%
Good
Credit
Risks
Bad
Credit
Risks
Good
Credit
Risks
Yes
YesYes
NO
NONO
Income>$40K
Production
ADS
Model
Model
Development
Model
Deployment
Scores
Data Store/
Warehouse
Development ADS

36. 38

37. 39

38. 40

39. 41
Model Development with
Reusable ADS
Analytic Server for
Model Development
Aggregations, Joins, Sorts
Transformations
Aggregations, Joins,
Sorts, Transformations
v

40. 42

41. 43

42. 44

43. 45
Horizontals: Applications
Customer
Lifestyle
Life-stage
Lifetime
Value
Customer
Satisfaction
Survey
Analysis
Customer
Acquisition
Campaign
Effectiveness
Customer
Retention
Cross Sell
Up Sell
Propensity
To Buy
Market
Segmentation
Identity
Theft
Failure/
Defect
Detection
Fraud
Prevention
Six Sigma
Process
Yield
Optimization
Demand
Forecasting
Risk
Management
Financial
Forecast
Pricing
Analysis
Customer
Segmentation
Product
Recommen-
dations
Customer Marketing Sales Operations Finance

44. 46
Cost
Revenue
Customer leavesCustomer joins
(or rejoins)
Subscription life (months)
0
2 3 4
Create
Interest
Acquisition
Cost
Recurring Revenue
1
2 3
4 Direct Cost
to Serve
5
Cross-sell
Upsell
6Renewal
7
Migration
8 9
10
Churn Bad debt
Win back
12
Lifetime Value
-500
-1,000
1,000
500
24 Source: McKinsey & Co

45. 47
Offer 1
Offer 2
Offer 3
Offer 4
Offer 5
Offer 6
Call Centres
Face 2 Face: Retail /
Dealer / Sales force
Web Presence / Email
SMS / MMS/ WAP
Direct Mail / Bill Inserts /
Bill Messages
Product
Targets
Contact Centre
Capacity
Contact Frequency /
Permissions /
Preferences
Min List
Sizes
Constraints
Saturation
Wrong Timing
Missed
Opportunity
Many Offers Many Channels Millions of Customers

47. Predictive
Model
(ANNs)
Feature
Computation
ScoreRaw Inputs Cooked inputs
Profiles
(Memory)
Cursive machine-print text
Real-time Fraud Detection
* See Hinton lecture at Google
Real-time Transactional Fraud Detection
using Neural Networks
OCR of machine-print cursive text using
neural networks (typically using hundreds of
thousand of weights.

49. Recent Book: “High-Performance Data Mining and Big Data
Analytics”
My Blog