Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data

Index

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

Numbers & Symbols

\ (backward slash) as separator, 69

/ (forward slash) as separator, 69

1-itemsets, 147

2-itemsets, 148–149

3 Vs (volume, variety, velocity), 2–3

3-itemsets, 149–150

4-itemsets, 150–151

A

accuracy, 225

ACF (autocorrelation function), 236–237

ACME text analysis example, 259–260

raw text collection, 260–263

aggregates (SQL)

ordered, 351–352

user-defined, 347–351

aggregators of data, 18

AIE (Applied Information Economics), 28

algorithms

clustering, 134–135

decision trees, 197–200

C4.5, 203–204

CART, 204

ID3, 203

Alphine Miner, 42

alternative hypothesis, 102–103

analytic projects

Approach, 369–371

BI analyst, 362

business users, 361

code, 362, 376–377

communication, 360–361

data engineer, 362

data scientists, 362

DBA (Database Administrator), 362

deliverables, 362–364

audiences, 364–365

core material, 364–365

key points, 372

Main Findings, 367–369

model description, 371

model details, 372–374

operationalizing, 360–361

outputs, 361

presentations, 362

Project Goals, 365–367

project manager, 362

project sponsor, 361

recommendations, 374–375

stakeholders, 361–362

technical specifications, 376–377

analytic sandboxes. See sandboxes

analytical architecture, 13–15

analytics

business drivers, 11

examples, 22–23

new approaches, 16–19

ANOVA, 110–114

Anscombe's quartet, 82–83

aov( ) function, 78

Apache Hadoop. See Hadoop

APIs (application programming interfaces), Hadoop, 304–305

apriori( ) function, 146, 152–157 ...