Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

advanced analytics

using, in business 4

Akaike’s Information Criteria (AIC) 92

analysis of variance (ANOVA) 26, 27

Apriori algorithm

used, for performing market basket analysis 143-148

used, for product bundling 142, 143

B

Bayesian Information Criteria (BIC) 92

binary logistic regression 95

business operations

improving, with web analytics 235

business-to-business (B2B) 176

C

causality 32

causation 32-37

choice-based conjoint 81

churn 154

client segments

creating 190-196

clustering 41, 42, 193

clusters

as customer segments 196-206

conjoint analysis 80, 81

conjoint studies 80

uses 80

conjoint experiment

designing 82, 83

correlation 29

correlation heatmap 32

correlation matrix 29-31

corr method 30

cross-validation 47

Curry, Michael

opinion, on working with data 263

perspective, on creating data teams 277

perspective, on future of data 279

Customer Acquisition Cost (CAC) 154

customer attrition 154

customer churn 154

customer data

exploring 154-163

customer lifetime value (CLV) 246

calculating 246-251

customer revenue

predicting 251-256

customer segmentation 176

data, exploring 177-182

customers, with decreasing purchases

identifying 128-137

D

data

analyzing 211-227

exploring 104, 177-182, 235-246

manipulating, with pandas 6, 7

storing, with pandas 6, 7

data science

using, in business 4

demand curve

exploring, in code 116-119

finding 115

used, for optimizing revenue 119-125

dimensionality reduction 42, 43, 191

Durbin-Watson 88

E

exploratory data analysis (EDA) 197

F

feature engineering

applying, to structure data 182-190

features

scaling, to range 38-41

Flink, Patrick

perspective, on implementing data-driven culture 283-285

F-statistic 87

G

Godau, Jack

perspective, on creating data teams 278

perspective, on data-driven benefits 272

perspective, on data-driven strategy challenges 275

perspective, on using data in organization 270, 271

Google Analytics

providing, information 234

H

Hernandez, Agustina

perspective, on implementing data-driven culture 281-283

I

Intercept 87

K

kernel density estimation (KDE) 35

K-means clustering 41, 42, 194

Kulesz, Micaela

perspective, on creating data teams 277

perspective, on data-driven strategy challenges 275

perspective, on future of data 280

perspective, on using data in organization 267, 268

perspective, on working with data 263, 264

L

LightGBM 254

benefits 255

logarithmic scale 239

M

machine learning (ML) 23, 177

machine learning models

building 43-48

markdown 210

creating 210

market basket analysis

performing, with Apriori algorithm 143-148

market rankings 56-61

Matplotlib 177, 235

Michael Curry

opinion, on working with data 263

perspective, on data-driven benefits 271, 272

perspective, on using data in organization 269, 270

mpl_toolkits 177

multinomial logistic regression 96

multiple linear regression

relationships, modeling with 28, 29

N

NumPy 4, 83, 105, 155, 177, 235

used, for statistics and algebra 4-6

NumPy array 5

O

Omnibus 88

ordinal logistic regression 96

ordinary least squares (OLS) 27

using, with Python 85, 86

using, with Statsmodels 85, 86

Ordinary Least Squares (OLS) module 116

P

paired t-test 26

pandas 6, 83, 105, 155, 177, 235

used, for data manipulating 6-8

used, for data storing 7, 8

patterns

visualizing, with Seaborn 8-21

Prem, Florian

perspective, on creating data teams 276

perspective, on data-driven benefits 273

perspective, on data-driven strategy challenges 274

perspective, on future of data 279

perspective, on using data in organization 266

perspective, on working with data 265, 266

price demand elasticity 104

principal component analysis (PCA) 42, 190

Prob (Omnibus) 88

product

relevant attributes, determining 83-85

product bundling

with Apriori algorithm 142, 143

product features

combinations, predicting 95-101

working with 90-94

product recommendation systems 137

creating 138-142

Prophet

sales, predicting with 227-230

p-value 25

Python

ordinary least squares (OLS), using with 85-90

Python modules 177

Matplotlib 155

mpl_toolkits 155

NumPy 105, 155

pandas 105, 155

seaborn 155

Seaborn and Matplotlib 105-115

statsmodels 105, 155

using 235

Pytrends

data limitations 55

installing 55, 56

search trends 54

Q

queries

performance, analyzing over time 75-77

R

Random Forest 99

range

features, scaling to 38-41

relationships

modeling, with multiple linear regression 28, 29

retention rate 154

Rodriguez Martino, Julio

perspective, on creating data teams 278, 279

perspective, on data-driven strategy challenges 276

perspective, on future of data 280

perspective, on using data in organization 268

perspective, on working with data 262

R-squared 87

S

sales

predicting, with Prophet 227-230

seaborn 8, 177, 235

patterns, visualizing with 8-21

Seaborn and Matplotlib 105-115

search trend patterns

changes, finding with 62-69

using, with Pytrends 54, 55

segmentation 190

slicing 5

statsmodels 83, 105, 155, 177, 235

ordinary least squares (OLS), using with 85-90

std error 87

strengths, weakness, opportunities, and threats (SWOT) analysis 235

supervised learning (SL) algorithm 99

Support Vector Classifier (SVC) 172

T

target variables

predicting 167-173

trends

insights, obtaining with similar queries 69-75

t-test 24

used, for validating effect of changes 24-27

t-value 26

U

unpaired t-test 26

V

Van Der, Wim

perspective, on creating data teams 279

perspective, on data-driven benefits 271

perspective, on data-driven strategy challenges 274, 275

perspective, on using data in organization 268

perspective, on working with data 265

variables relationships

exploring 163-167

W

web analytics 234

used, for improving business operations 235

Wuisman, Bob

perspective, on creating data teams 278

perspective, on data-driven benefits 273

perspective, on data-driven strategy challenges 273

perspective, on future of data 280

perspective, on using data in organization 270

perspective, on working with data 264

Y

yellowbrick 177