April 2012
Beginner to intermediate
304 pages
8h 5m
English
Index
A
Abandoned basket statistics
Advanced analytics
analytic team responsibilities
core analytics compared to
Advertising results, assessment of
Analysis
analytic data set (ADS)
business importance of
“cherry picking” of findings
cloud computing
core versus advanced analytics
determination of
embedded scoring
enterprise analytic data set (EADS)
Enterprise Data Warehouse (EDW)
extract, transform, and load (ETL) process
framing the problem
G.R.E.A.T. criteria
grid computing
inferences versus computing statistics
MapReduce
massively parallel processing (MPP) database systems
processes
reporting compared to
samples versus population
sandbox environments
scalability
statistical significance
tools and methods for
Analytic data set (ADS). See also Enterprise analytic data set (EADS)
development
embedded scoring, inputs for
enterprise (EADS)
production
traditional
Analytic innovation center
commitment
failures, dealing with
guiding principles of
innovation council
scope of
sponsorship
team strength
technology platform
third-party products and services
Analytic methods
collaborative filtering
commodity models
ensemble models
page rank
text analysis
Analytic professionals
analytic teams of
business savvy and
business value of
certification of, need for
clean data and
commitment of
common misconceptions about
communication skills
creativity of
cross training
cultural awareness of
data scientists as
decisions, granularity of
education of
experience in industry
focus on importance of data by
information technology (IT) compared to
innovation and
intuition of ...
