Building the Analyst of the Future. Presentation by Jeff Camm, Director of the center for Business Analytics at the University of Cincinnati.

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What’s New with
Analytics in Academia?
Building the Analyst
of the Future
DR. JEFF CAMM

2. Interest in Analytics
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3. What's New with Analytics
in Academia?
Building the Analyst of the Future
Jeffrey D. Camm
Director, Center for Business Analytics
University of Cincinnati
Lindner College of Business
Department of Operations, Business Analytics & Information Systems
Jeff.Camm@uc.edu 3

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6. Why now?
l Big Data
l Better Software
l Better/cheaper computing
We create as much information in
two days now as we did from the
dawn of man through 2003.
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7. Big Data
l Social Media
l GE Aviation
l dunnhumby
l IRI
l Healthcare
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8. Competing on Analytics
Some companies have developed a corporate-wide
analytical mindset and are now competing
based on analytics.
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9. What is Analytics?
Our working definition:
Analytics is the scientific process of
transforming data into insights for making
better decisions.
This includes descriptive, predictive and
prescriptive models.
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10. What does it mean to be
scientific?
The Scientific Method
– Ask a Question
– Do Background
Research
– Construct a Hypothesis
– Test Your Hypothesis
by Doing an
Experiment
– Analyze Your Data and
Draw a Conclusion
– Communicate Your
Results
The Engineering
Design Process
– Define the Problem
– Do Background
Research
– Specify Requirements
– Brainstorm Solutions
– Choose the Best
Solution
– Do Development Work
– Build a Prototype
– Test and Redesign
Source:
Source:
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11. Source:
Source:
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12. Categorization
l Descriptive – what happened?
l data queries, reports, descriptive statistics,
data visualization
l Predictive – what will happen?
l linear regression, time series analysis, data
mining, simulation
l Prescriptive – what should we do?
l optimization, simulation/optimization,
decision analysis
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13. Descriptive Analytics
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14. Predictive Analytics
Cincinnati Zoo:
l # Donors = 0.0213*(Zip Code Population) – 26.941
– For every increase of 100 people in a zip code, we expect
about 2 more donors
– Adjusted R2 = 0.3847
l # Donors = 0.0196*(Zip Code Population) +
0.0026*(Avg Home Price in Zip Code) – 372.15
– For every $1000 increase in average home price in a zip
code, we expect about 2.6 more donors
– Adjusted R2 = 0.4857
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15. Prescriptive Analytics
North American Product
Supply Study
l $1B + NPV
l $250M
savings per
year
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16. Analytics Maturity
Source: SASSAS
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17. What will be the life cycle of
this movement?
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18. McKinsey Report
By 2018, the U.S.
could face a shortage
of 190,000 data
scientists and another
1.5 million managers
and analysts who
know how to use big
data to make
effective decisions.
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19. Gartner defines 3
Analytics Personas:
l Evangelists (me J)
l Enablers (Analytics Graduates)
l Consumers (Management)
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20. How has academia
responded to the
demand for analytics?
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21. l Enablers (Masters Programs in
Analytics)
l Consumers (MBA core courses,
electives in analytics, MBA tracks)
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22. New Programs:
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23. Data Informed’s Map of University
Programs in Big Data Analytics
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26. Source: NC State
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27. UC MS-Business Analytics
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28. Our MS Business Analytics Program
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29. Curriculum for Enablers
Based on Klimberg, Business
Intelligence, INFORMS 2011
(Hinrichs, SEDSI, 2012)
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30. Prerequisites:
UC MS Bus Analytics
l Multivariate Calc.
l Linear Algebra
l Programming
l Business Core
NC State MS Analytics
l Statistical Methods
l Regression
l Statistical Computing &
Data Management
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31. UC Electives
(Basic Business Knowledge)
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32. Core Courses:
UC MS Bus Analytics
l Probability Modeling
l Statistical Methods
l Data Management
l Statistical Computing
l Statistical Modeling
l Optimization Modeling
l Simulation Modeling
l Optimization Methods
NC State MS Analytics
l Analytics Tools and
Techniques
l Analytics Foundations
l Analytics Methods &
Applications I
l Analytics Practicum I
l Analytics Methods &
Applications II
l Analytics Practicum II
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33. NC State:
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34. UC Electives
(10 credit hours)
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35. UC Capstone
l Individual Project
l Case Studies in Analytics
l Internships
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36. Other Programs
l Some are more focused:
– Northwestern: MS Predictive Analytics
– UCONN: MS Business Analytics and
Project Mgt.
– Wash U. St. Louis: MS Customer
Analytics
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37. Payoff
l Starting Salaries:
– $65k to $135K
– Virtually 100% placement
l Positions
– Analyst
– Data scientist
– Application Area Specific
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38. Possible Pitfalls
l Software vs Methodology
l Consulting vs Analyst
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39. Analytics vs
Data Science
l What’s the difference?
– Business Knowledge?
– Hard Coding?
– Statistics
– Optimization and Simulation?
– Traditional vs Machine Learning
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40. Columbia: MS Data Science
30 credit hours
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41. Columbia: MS Data Science
Algorithms for Data Science
l Methods for organizing data, e.g. hashing, trees, queues, lists,
priority queues. Streaming algorithms for computing statistics on
the data. Sorting and searching. Basic graph models and
algorithms for searching, shortest paths, and matching. Dynamic
programming. Linear and convex programming. Floating point
arithmetic, stability of numerical algorithms, Eigenvalues, singular
values, PCA, gradient descent, stochastic gradient descent, and
block coordinate descent. Conjugate gradient, Newton and quasi-
Newton methods. Large scale applications from signal processing,
collaborative filtering, recommendations systems, etc.
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42. Analytics vs Data Science
Source: Jerry Smith, datascientistinsights.com
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43. What does the future hold?
l Analytics 1.0 - - the era of “business
intelligence.
l Analytics 2.0 - - big data analytics
(with small math)
l Analytics 3.0 - - the intersection of
the two, with every company joining
the data economy
Source: Davenport
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44. Analytics 3.0
§ Mixture of data types
§ More analytics than in the 2.0 big data world
§ Everything faster—technology, methods
§ Analytics baked into processes and decisions
§ Chief Analytics Officers emerge
§ Analytics become prescriptive
§ Data science gets mixed in
§ Many data integration options
Source: Davenport
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45. Thanks!
Questions?
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