Index

A

Agile terminology
Artificial intelligence (AI)
Art of closing
Auditing models
accuracy metric
CRISP-DM process
framework
predetermined steps
systematic way

B

Benchmarking inputs
database maintainers
data quality
data warehouse
dimensions
ethnic/socioeconomic subgroups
partial interruption
post-implementation
statistical measures
Benefit sales
analytic solutions
customer’s business
inspecting data sets
management consultants
team sales approach

C

Cascading strategies
Costs of failure, projects
accurate models, building
Big Data
error post-release
overlapping context
risk
Type I/II Error
Type III Error
unintended consequences
CRISP-DM
Agile philosophy
business understanding phase
cycle
evaluation phase
user’s problem

D

Data science
strategies
close the loop
create document
military strategy
mission statements
North Vietnamese strategy
organization’s goals
shared culture
team strategy
typical decisions
tools
value in organization
Data science project
human side, problems
models
opportunities
risk
tool, solve problems
trust
Data science work
audiences
delivering presentation
Git repository
to groups
non-data science component
prepare documentation
straightforward failures
whitepaper
Decision makers
Define, Measure, Analyze, Improve, and Control (DMAIC)
Descriptive statistics
Design thinking approach
empathizing with the user
five stages
Stanford process
Documentation
comprehensive plans
correct objective and framing
existence of electronic
waterfall project management paradigms

E

External validation

F

Failure mode and effects analysis (FMEA)
Flat maximum effect

G, H

Generalized additive model (GAM)
Generalized linear models (GLM)
Global financial crisis (GFC)
Gradient boosting machines

I, J

Intelligibility model
black box
explainable model
GAM
global surrogate
linear models
local surrogate
machine learning algorithms
presentation
shrinkage method
standards/style guides
surrogate models

K

Kaggle competitions

L

Least squares regression
LIME visualization’s format
Local Interpretable Model-Agnostic Explanation (LIME)
Logistic regression

M

Machine learning algorithms
Military strategies
Model
attributes
audience or users
consistency
nonlinear relationship
nonmonotonic
overfitting
GLM
intelligibility
SeeIntelligibility model
maintaining users’ trust
ML
perform
SMEs
Model agnostic assessment
Model credibility checklist
Model maintenance
attribute/categorical data
degradation
SPC
Model risk assessment

N

North Vietnamese strategy

O

Objective
data science solution
dependent variable
numerical definition
qualitative viewpoint
regression model
Six Sigma
Opaque model

P

Poisson regression
Project checklist
Project Hopper
Project management
Agile
different processes
disadvantage
hybrid approaches
PMBOK
risk and quality management
waterfall approach
Project management body of knowledge (PMBOK)
Project resources
means, available data
ways, data science skills
Promoting data science
AI
boxing game
branding, personal/shared
digital marketing
Double-Edged Sword, hype cycle
hyped’s benefits

Q

Quality function deployment (QFD)
House of Quality
system thinking and psychology
total quality control

R

Random forests and gradient boosting machines
Random forest/neural network
Reliability
concept
consistent/dependable
data quality
development phase
worst outcomes, models
financial crash
flow on effects
GFC
retail/corporate credit
Reliable models checklist
Rigidity

S

Sales checklist
Self-promotion
communicate
data science function
documentation
effective leader
individual successes
solutioning
Selling data science projects
analysis
barest minimum
data pipeline
evaluation methods
robust methods
sales documents/presentations
time and money commitment
Six Sigma approach
disadvantage
ease of fixing
QFD
voice of the customer
Soft standardization
Standard processes
Statistical process control (SPC)
Strategic alignment
Subject matter experts (SMEs)
Systematic data monitoring
control charts
external data provider
FMEA
quality control monitoring
specific charts

T, U, V

Tailored approach
audience types
Conger’s tips, data scientists
intrinsic quality
persuasion effort
reason-based case
Team efficiency
Agile
human element
retrospectives
skills
soft standardization
standard processes
Team strategy
Trust
accuracy measure
credibility/reliability
design thinking process
earning
five-step process
honest truth
inverse relationship
listening
paramount importance
statistical models
Trusted advisor

W, X, Y, Z

Wicked problem
data science tools
long track records
numerical solutions
solvable state
strategic assumption surfacing and testing
structuring methods