The handbook of data science and AI: generate value from data with machine learning and data analytics
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Munich
Carl Hanser Verlag
[2022]
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| Beschreibung: | XX, 553 Seiten Illustrationen, Diagramme 24 cm Enthält: Online-Ressource |
| ISBN: | 9781569908860 1569908869 |
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| 100 | 1 | |a Papp, Stefan |e Verfasser |0 (DE-588)1161133895 |4 aut | |
| 245 | 1 | 0 | |a The handbook of data science and AI |b generate value from data with machine learning and data analytics |c Stefan Papp, Wolfgang Weidinger, Katherine Munro, Bernhard Ortner, Annalisa Cadonna [und weitere] |
| 264 | 1 | |a Munich |b Carl Hanser Verlag |c [2022] | |
| 264 | 4 | |c © 2022 | |
| 300 | |a XX, 553 Seiten |b Illustrationen, Diagramme |c 24 cm |e Enthält: Online-Ressource | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
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| 650 | 0 | 7 | |a Data Science |0 (DE-588)1140936166 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 653 | |a Algorithmen | ||
| 653 | |a Business Intelligence | ||
| 653 | |a Data Engineering | ||
| 653 | |a Data Scientist | ||
| 653 | |a Datenanalyse | ||
| 653 | |a Datenstrategie | ||
| 653 | |a Deep Learning | ||
| 653 | |a Machine Learning | ||
| 653 | |a Statistik | ||
| 653 | |a INF2022 | ||
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| 700 | 1 | |a Munro, Katherine |e Verfasser |0 (DE-588)1257369903 |4 aut | |
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Datensatz im Suchindex
| _version_ | 1814260963375841280 |
|---|---|
| adam_text |
TABLE
OF
CONTENTS
FOREWORD
.
XV
PREFACE
.
XVIII
ACKNOWLEDGMENTS
.
XX
1
INTRODUCTION
.
1
1.1
WHAT
ARE
DATA
SCIENCE,
MACHINE
LEARNING
AND
ARTIFICIAL
INTELLIGENCE?
.
2
1.2
DATA
STRATEGY
.
8
1.3
FROM
STRATEGY
TO
USE
CASES
.
10
1.3.1
DATA
TEAMS
.
11
1.3.2
DATA
AND
PLATFORMS
.
16
1.3.3
MODELING
AND
ANALYSIS
.
17
1.4
USE
CASE
IMPLEMENTATION
.
18
1.4.1
ITERATIVE
EXPLORATION
OF
USE
CASES
.
19
1.4.2
END-TO-END
DATA
PROCESSING
.
21
1.4.3
DATAPRODUCTS
.
22
1.5
REAL-LIFE
USE
CASE
EXAMPLES
.
22
1.5.1
VALUE
CHAIN
DIGITIZATION
(VCD)
.
22
1.5.2
MARKETING
SEGMENT
ANALYTICS
.
23
1.5.3
360
VIEW
OF
THE
CUSTOMER
.
23
1.5.4
NGO
AND
SUSTAINABILITY
USE
CASES
.
24
1.6
DELIVERING
RESULTS
.
25
1.7
IN
A
NUTSHELL
.
27
2
INFRASTRUCTURE
.
29
STEFAN
PAPP
2.1
INTRODUCTION
.
29
2.2
HARDWARE
.
31
2.2.1
DISTRIBUTED
SYSTEMS
.
34
2.2.2
HARDWARE
FOR
AL
APPLICATIONS
.
37
2.3
LINUX
ESSENTIALS
FOR
DATA
PROFESSIONALS
.
38
2.4
TERRAFORM
.
54
2.5
CLOUD
.
58
2.5.1
BASIC
SERVICES
.
61
2.5.2
CLOUD-NATIVE
SOLUTIONS
.
65
2.6
IN
A
NUTSHELL
.
68
3
DATA
ARCHITECTURE
.
69
ZOLTAN
C.
TOTH
3.1
OVERVIEW
.
69
3.1.1
MASLOW
'
S
HIERARCHY
OF
NEEDS
FOR
DATA
.
69
3.1.2
DATA
ARCHITECTURE
REQUIREMENTS
.
71
3.1.3
THE
STRUCTURE
OF
A
TYPICAL
DATA
ARCHITECTURE
.
71
3.1.4
ETL
(EXTRACT,
TRANSFORM,
LOAD)
.
72
3.1.5
ELT
(EXTRACT,
LOAD,
TRANSFORM)
.
73
3.1.6
ETLT
.
73
3.2
DATA
INGESTION
AND
INTEGRATION
.
74
3.2.1
DATASOURCES
.
74
3.2.2
TRADITIONAL
FILE
FORMATS
.
75
3.2.3
MODERN
FILE
FORMATS
.
77
3.2.4
SUMMARY
.
79
3.3
DATA
WAREHOUSES,
DATA
LAKES,
AND
LAKEHOUSES
.
79
3.3.1
DATAWAREHOUSES
.
79
3.3.2
DATA
LAKES
AND
THE
LAKEHOUSE
.
83
3.3.3
SUMMARY:
COMPARING
DATA
WAREHOUSES
TO
LAKEHOUSES
.
85
3.4
DATA
PROCESSING
AND
TRANSFORMATION
.
86
3.4.1
BIG
DATA
&
APACHE
SPARK
.
86
3.4.2
DATABRICKS
.
93
3.5
WORKFLOW
ORCHESTRATION
.
94
3.6
A
DATA
ARCHITECTURE
USE
CASE
.
96
3.7
IN
A
NUTSHELL
.
100
4
DATA
ENGINEERING
.
101
STEFAN
PAPP,
BERNHARD
ORTNER
4.1
DATA
INTEGRATION
.
102
4.1.1
DATA
PIPELINES
.
102
4.1.2
DESIGNING
DATA
PIPELINES
.
108
4.1.3
CI/CD
.
110
4.1.4
PROGRAMMING
LANGUAGES
.
112
4.1.5
KAFKA
AS
REFERENCE
ETL
TOOL
.
115
4.1.6
DESIGN
PATTERNS
.
119
4.1.7
AUTOMATION
OF
THE
STAGES
.
120
4.1.8
SIX
BUILDING
BLOCKS
OF
THE
DATA
PIPELINE
.
120
4.2
MANAGING
ANALYTICAL
MODELS
.
125
4.2.1
MODEL
DELIVERY
.
126
4.2.2
MODEL
UPDATE
.
127
4.2.3
MODEL
OR
PARAMETER
UPDATE
.
128
4.2.4
MODEL
SCALING
.
128
4.2.5
FEEDBACK
INTO
THE
OPERATIONAL
PROCESSES
.
129
4.3
IN
A
NUTSHELL
.
130
5
DATA
MANAGEMENT
.
131
STEFAN
PAPP,
BERNHARD
ORTNER
5.1
DATA
GOVERNANCE
.
133
5.1.1
DATA
CATALOG
.
134
5.1.2
DATA
DISCOVERY
.
136
5.1.3
DATA
QUALITY
.
140
5.1.4
MASTER
DATA
MANAGEMENT
.
141
5.1.5
DATA
SHARING
.
142
5.2
INFORMATION
SECURITY
.
143
5.2.1
DATA
CLASSIFICATION
.
144
5.2.2
PRIVACY
PROTECTION
.
145
5.2.3
ENCRYPTION
.
147
5.2.4
SECRETS
MANAGEMENT
.
149
5.2.5
DEFENSE
IN
DEPTH
.
150
5.3
IN
A
NUTSHELL
.
151
6
MATHEMATICS
.
153
ANNALISA
CADONNA
6.1
LINEAR
ALGEBRA
.
154
6.1.1
VECTORS
AND
MATRICES
.
154
6.1.2
OPERATIONS
BETWEEN
VECTORS
AND
MATRICES
.
157
6.1.3
LINEAR
TRANSFORMATIONS
.
160
6.1.4
EIGENVALUES,
EIGENVECTORS,
AND
EIGENDECOMPOSITION
.
161
6.1.5
OTHER
MATRIX
DECOMPOSITIONS
.
162
6.2
CALCULUS
AND
OPTIMIZATION
.
163
6.2.1
DERIVATIVES
.
164
6.2.2
GRADIENT
AND
HESSIAN
.
166
6.2.3
GRADIENT
DESCENT
.
167
6.2.4
CONSTRAINED
OPTIMIZATION
.
169
6.3
PROBABILITY
THEORY
.
170
6.3.1
DISCRETE
AND
CONTINUOUS
RANDOM
VARIABLES
.
171
6.3.2
EXPECTED
VALUE,
VARIANCE,
AND
COVARIANCE
.
174
6.3.3
INDEPENDENCE,
CONDITIONAL
DISTRIBUTIONS,
AND
BAYES
'
THEOREM
.
176
6.4
IN
A
NUTSHELL
.
177
7
STATISTICS
-
BASICS
.
179
RANIA
WAZIR,
GEORG
LANGS,
ANNALISA
CADONNA
7.1
DATA
.
180
7.2
SIMPLE
LINEAR
REGRESSION
.
181
7.3
MULTIPLE
LINEAR
REGRESSION
.
189
7.4
LOGISTIC
REGRESSION
.
191
7.5
HOW
GOOD
IS
OUR
MODEL?
.
198
7.6
IN
A
NUTSHELL
.
199
8
MACHINE
LEARNING
.
201
GEORG
LANGS,
KATHERINE
MUNRO,
RANIA
WAZIR
8.1
INTRODUCTION
.
201
8.2
BASICS:
FEATURE
SPACES
.
203
8.3
CLASSIFICATION
MODELS
.
206
8.3.1
K-NEAREST-NEIGHBOR-CLASSIFIER
.
206
8.3.2
SUPPORT
VECTOR
MACHINE
.
207
8.3.3
DECISION
TREE
.
208
8.4
ENSEMBLE
METHODS
.
209
8.4.1
BIAS
AND
VARIANCE
.
210
8.4.2
BAGGING:
RANDOM
FORESTS
.
211
8.4.3
BOOSTING:
ADABOOST
.
215
8.5
ARTIFICIAL
NEURAL
NETWORKS
AND
THE
PERCEPTRON
.
215
8.6
LEARNING
WITHOUT
LABELS
-
FINDING
STRUCTURE
.
218
8.6.1
CLUSTERING
.
218
8.6.2
MANIFOLD
LEARNING
.
219
8.6.3
GENERATIVE
MODELS
.
220
8.7
REINFORCEMENT
LEARNING
.
221
8.8
OVERARCHING
CONCEPTS
.
223
8.9
INTO
THE
DEPTH
-
DEEP
LEARNING
.
224
8.9.1
CONVOLUTIONAL
NEURAL
NETWORKS
.
224
8.9.2
TRAINING
CONVOLUTIONAL
NEURAL
NETWORKS
.
225
8.9.3
RECURRENT
NEURAL
NETWORKS
.
227
8.9.4
LONG
SHORT-TERM
MEMORY
.
228
8.9.5
AUTOENCODERS
AND
U-NETS
.
230
8.9.6
ADVERSARIAL
TRAINING
APPROACHES
.
231
8.9.7
GENERATIVE
ADVERSARIAL
NETWORKS
.
232
8.9.8
CYCLE
GANS
AND
STYLE
GANS
.
234
8.9.9
OTHER
ARCHITECTURES
AND
LEARNING
STRATEGIES
.
235
8.10
VALIDATION
STRATEGIES
FOR MACHINE
LEARNING
TECHNIQUES
.
235
8.11
CONCLUSION
.
237
8.12
IN
A
NUTSHELL
.
237
9
BUILDING
GREAT
ARTIFICIAL
INTELLIGENCE
.
239
DANKO
NIKOLIC
9.1
HOW
AL
RELATES
TO
DATA
SCIENCE
AND
MACHINE
LEARNING
.
239
9.2
A
BRIEF
HISTORY
OF
AL
.
243
9.3
FIVE
RECOMMENDATIONS
FOR
DESIGNING
AN
AL
SOLUTION
.
245
9.3.1
RECOMMENDATION
NO.
1:
BE
PRAGMATIC
.
245
9.3.2
RECOMMENDATION
NO.
2:
MAKE
IT
EASIER
FOR
MACHINES
TO
LEARN
-
CREATE
INDUCTIVE
BIASES
.
247
9.3.3
RECOMMENDATION
NO.
3:
PERFORM
ANALYTICS
.
252
9.3.4
RECOMMENDATION
NO.
4:
BEWARE
OF
THE
SCALING
TRAP
.
254
9.3.5
RECOMMENDATION
NO.
5:
BEWARE
OF
THE
GENERALITY
TRAP
(THERE
IS
NO
SUCH
A
THING
AS
FREE
LUNCH)
.
263
9.4
HUMAN-LEVEL
INTELLIGENCE
.
268
9.5
IN
A
NUTSHELL
.
270
10
NATURAL
LANGUAGE
PROCESSING
(NLP)
.
273
KATHERINE
MUNRO
10.1
WHAT
IS
NLP
AND
WHY
IS
IT
SO
VALUABLE?
.
273
10.2
NLP
DATA
PREPARATION
TECHNIQUES
.
275
10.2.1
THE
NLP
PIPELINE
.
275
10.2.2
CONVERTING
THE
INPUT
FORMAT
FOR
MACHINE
LEARNING
.
281
10.3
NLP
TASKS
AND
METHODS
.
283
10.3.1
RULE-BASED
(SYMBOLIC)
NLP
.
284
10.3.2
STATISTICAL
MACHINE
LEARNING
APPROACHES
.
287
10.3.3
NEURAL
NLP
.
295
10.3.4
TRANSFER
LEARNING
.
301
10.4
AT
THE
CUTTING
EDGE:
CURRENT
RESEARCH
FOCUSES
FOR
NLP
.
312
10.5
IN
A
NUTSHELL
.
314
11
COMPUTER
VISION
.
317
ROXANE
LICANDRO
11.1
WHAT
IS
COMPUTER
VISION?
.
317
11.2
A
PICTURE
PAINTS
A
THOUSAND
WORDS
.
319
11.2.1
THE
HUMAN
EYE
.
319
11.2.2
IMAGE
ACQUISITION
PRINCIPLE
.
321
11.2.3
DIGITAL
FILE
FORMATS
.
326
11.2.4
IMAGE
COMPRESSION
.
327
11.3
I
SPY
WITH
MY
LITTLE
EYE
SOMETHING
THAT
IS
.
328
11.3.1
COMPUTATIONAL
PHOTOGRAPHY
AND
IMAGE
MANIPULATION
.
330
11.4
COMPUTER
VISION
APPLICATIONS
&
FUTURE
DIRECTIONS
.
334
11.4.1
IMAGE
RETRIEVAL
SYSTEMS
.
334
11.4.2
OBJECT
DETECTION,
CLASSIFICATION
AND
TRACKING
.
337
11.4.3
MEDICAL
COMPUTER
VISION
.
338
11.5
MAKING
HUMANS
SEE
.
341
11.6
IN
A
NUTSHELL
.
343
12
MODELLING
AND
SIMULATION
-
CREATE
YOUR
OWN
MODELS
.
347
GUNTHER
ZAUNER,
WOLFGANG
WEIDINGER
12.1
INTRODUCTION
.
347
12.2
GENERAL
ASPECTS
.
349
12.3
MODELLING
TO
ANSWER
QUESTIONS
.
349
12.4
REPRODUCIBILITY
AND
MODEL
LIFECYCLE
.
351
12.4.1
THE
LIFECYCLE
OF
A
MODELLING
AND
SIMULATION
QUESTION
.
352
12.4.2
PARAMETER
AND
OUTPUT
DEFINITION
.
354
12.4.3
DOCUMENTATION
.
357
12.4.4
VERIFICATION
AND
VALIDATION
.
357
12.5
METHODS
.
361
12.5.1
ORDINARY
DIFFERENTIAL
EQUATIONS
(DDES)
.
361
12.5.2
SYSTEM
DYNAMICS
(SD)
.
362
12.5.3
DISCRETE
EVENT
SIMULATION
.
365
12.5.4
AGENT-BASED
MODELLING
.
368
12.6
MODELLING
AND
SIMULATION
EXAMPLES
.
371
12.6.1
DYNAMIC
MODELLING
OF
RAILWAY
NETWORKS
FOR
OPTIMAL
PATHFINDING
USING
AGENT-BASED
METHODS
AND
REINFORCEMENT
LEARNING
.
371
12.6.2
AGENT-BASED
COVID
MODELLING
STRATEGIES
.
373
12.6.3
DEEP
REINFORCEMENT
LEARNING
APPROACH
FOR
OPTIMAL
REPLENISHMENT
POLICY
IN
A
VMI
SETTING
.
378
12.7
SUMMARY
AND
LESSONS
LEARNED
.
381
12.8
IN
A
NUTSHELL
.
381
13
DATA
VISUALIZATION
.
385
BARBORA
VESELA
13.1
HISTORY
.
386
13.2
WHICH
TOOLS
TO
USE
.
391
13.3
TYPES
OF
DATA
VISUALIZATIONS
.
393
13.3.1
SCATTER
PLOT
.
394
13.3.2
LINE
CHART
.
394
13.3.3
COLUMN
AND
BAR
CHARTS
.
395
13.3.4
HISTOGRAM
.
396
13.3.5
PIE
CHART
.
397
13.3.6
BOX
PLOT
.
398
13.3.7
HEAT
MAP
.
398
13.3.8
TREE
DIAGRAM
.
399
13.3.9
OTHER
TYPES
OF
VISUALIZATIONS
.
400
13.4
SELECT
THE
RIGHT
DATA
VISUALIZATION
.
400
13.5
TIPS
AND
TRICKS
.
402
13.6
PRESENTATION
OF
DATA
VISUALIZATION
.
407
13.7
IN
A
NUTSHELL
.
407
14
DATA
DRIVEN
ENTERPRISES
.
411
MARIO
MEIR-HUBER,
STEFAN
PAPP
14.1
THE
THREE
LEVELS
OF
A
DATA
DRIVEN
ENTERPRISE
.
412
14.2
CULTURE
.
412
14.2.1
CORPORATE
STRATEGY
FOR
DATA
.
413
14.2.2
THE
CURRENT
STATE
ANALYSIS
.
415
14.2.3
CULTURE
AND
ORGANIZATION
OF
A
SUCCESSFUL
DATA
ORGANISATION
.
417
14.2.4
CORE
PROBLEM:
THE
SKILLS
GAP
.
424
14.3
TECHNOLOGY
.
426
14.3.1
THE
IMPACT
OF
OPEN
SOURCE
.
426
14.3.2
CLOUD
.
426
14.3.3
VENDOR
SELECTION
.
427
14.3.4
DATA
LAKE
FROM
A
BUSINESS
PERSPECTIVE
.
427
14.3.5
THE
ROLE
OF
IT
.
428
14.3.6
DATA
SCIENCE
LABS
.
428
14.3.7
REVOLUTION
IN
ARCHITECTURE:
THE
DATA
MESH
.
429
14.4
BUSINESS
.
431
14.4.1
BUY
AND
SHARE
DATA
.
431
14.4.2
ANALYTICAL
USE
CASE
IMPLEMENTATION
.
432
14.4.3
SELF-SERVICE
ANALYTICS
.
433
14.5
IN
A
NUTSHELL
.
433
15
LEGAL
FOUNDATION
OF
DATA
SCIENCE
.
435
BERNHARD
ORTNER
15.1
INTRODUCTION
.
435
15.2
CATEGORIES
OF
DATA
.
436
15.3
GENERAL
DATA
PROTECTION
REGULATION
.
437
15.3.1
FUNDAMENTAL
RIGHTS
OF
GDPR
.
437
15.3.2
DECLARATION
OF
CONSENT
.
438
15.3.3
RISK-ASSESSMENT
.
440
15.3.4
ANONYMIZATION
UND
PSEUDO-ANONYMIZATION
.
441
15.3.5
TYPES
OF
ANONYMIZATION
.
442
15.3.6
LAWFUL
AND
TRANSPARENT
DATA
PROCESSING
.
444
15.3.7
RIGHT
TO
DATA
DELETION
AND
CORRECTION
.
445
15.3.8
PRIVACY
BY
DESIGN
.
446
15.3.9
PRIVACY
BY
DEFAULT
.
446
15.4
EPRIVACY-REGULATION
.
446
15.5
DATA
PROTECTION
OFFICER
.
447
15.5.1
INTERNATIONAL
DATA
EXPORT
IN
FOREIGN
COUNTRIES
.
447
YYI
15.6
SECURITY
MEASURES
.
448
15.6.1
DATA
ENCRYPTION
.
449
15.7
CCPA
COMPARED
TO
GDPR
.
449
15.7.1
TERRITORIAL
SCOPE
.
450
15.7.2
OPT-IN
VS.
OPT-OUT
.
450
15.7.3
RIGHT
OF
DATA
EXPORT
.
450
15.7.4
RIGHT
NOT
TO
BE
DISCRIMINATED
AGAINST
.
451
15.8
IN
A
NUTSHELL
.
451
16
AL
IN
DIFFERENT
INDUSTRIES
.
453
STEFAN
PAPP,
MARIO
MEIR-HUBER,
WOLFGANG
WEIDINGER,
THOMAS
TREND,
MAREK
DANIS
16.1
AUTOMOTIVE
.
456
16.1.1
VISION
.
457
16.1.2
DATA
.
458
16.1.3
USE
CASES
.
458
16.1.4
CHALLENGES
.
459
16.2
AVIATION
.
461
16.2.1
VISION
.
461
16.2.2
DATA
.
462
16.2.3
USE
CASES
.
462
16.2.4
CHALLENGES
.
463
16.3
ENERGY
.
463
16.3.1
VISION
.
464
16.3.2
DATA
.
464
16.3.3
USE
CASES
.
465
16.3.4
CHALLENGES
.
466
16.4
FINANCE
.
466
16.4.1
VISION
.
466
16.4.2
DATA
.
467
16.4.3
USE
CASES
.
467
16.4.4
CHALLENGES
.
469
16.5
HEALTH
.
469
16.5.1
VISION
.
470
16.5.2
DATA
.
471
16.5.3
USE
CASES
.
471
16.5.4
CHALLENGES
.
471
16.6
GOVERNMENT
.
472
16.6.1
VISION
.
472
16.6.2
DATA
.
473
16.6.3
USE
CASES
.
473
16.6.4
CHALLENGES
.
476
16.7
ART
.
476
16.7.1
VISION
.
477
16.7.2
DATA
.
477
16.7.3
USE
CASES
.
477
16.7.4
CHALLENGES
.
478
16.8
MANUFACTURING
.
478
16.8.1
VISION
.
479
16.8.2
DATA
.
479
16.8.3
USE
CASES
.
479
16.8.4
CHALLENGES
.
480
16.9
OIL
AND
GAS
.
481
16.9.1
VISION
.
481
16.9.2
DATA
.
481
16.9.3
USE
CASES
.
482
16.9.4
CHALLENGES
.
484
16.10
SAFETY
AT
WORK
.
484
16.10.1
VISION
.
484
16.10.2
DATA
.
485
16.10.3
USE
CASES
.
485
16.10.4
CHALLENGES
.
486
16.11
RETAIL
.
487
16.11.1
VISION
.
487
16.11.2
DATA
.
487
16.11.3
USE
CASES
.
488
16.11.4
CHALLENGES
.
488
16.12
TELECOMMUNICATIONS
PROVIDER
.
489
16.12.1
VISION
.
489
16.12.2
DATA
.
490
16.12.3
USE
CASES
.
490
16.12.4
CHALLENGES
.
492
16.13
TRANSPORT
.
492
16.13.1
VISION
.
492
16.13.2
DATA
.
493
16.13.3
USE
CASES
.
493
16.13.4
CHALLENGES
.
494
16.14
TEACHING
AND
TRAINING
.
494
16.14.1
VISION
.
495
16.14.2
DATA
.
496
16.14.3 USE
CASES
.
496
16.14.4
CHALLENGES
.
497
16.15
THE
DIGITAL
SOCIETY
.
497
16.16
IN
A
NUTSHELL
.
499
17
MINDSET
AND
COMMUNITY
.
501
STEFAN
PAPP
17.1
DATA-DRIVEN
MINDSET
.
501
17.2
DATA
SCIENCE
CULTURE
.
504
17.2.1
START-UP
OR
CONSULTING
FIRM?
.
504
17.2.2
LABS
INSTEAD
OF
CORPORATE
POLICY
.
505
17.2.3
KEIRETSU
INSTEAD
OF
LONE
WOLF
.
505
17.2.4
AGILE
SOFTWARE
DEVELOPMENT
.
507
17.2.5
COMPANY
AND
WORK
CULTURE
.
507
17.3
ANTIPATTERNS
.
510
17.3.1
DEVALUATION
OF
DOMAIN
EXPERTISE
.
510
17.3.2
IT
WILL
TAKE
CARE
OF
IT
.
511
17.3.3
RESISTANCE
TO
CHANGE
.
511
17.3.4
KNOW-IT-ALL
MENTALITY
.
512
17.3.5
DOOM
AND
GLOOM
.
513
17.3.6
PENNY-PINCHING
.
513
17.3.7
FEAR
CULTURE
.
514
17.3.8
CONTROL
OVER
RESOURCES
.
514
17.3.9
BLIND
FAITH
IN
RESOURCES
.
515
17.3.10 THE
SWISS
ARMY
KNIFE
.
516
17.3.11
OVER-ENGINEERING
.
516
17.4
IN
A
NUTSHELL
.
517
18
TRUSTWORTHY
AL
.
519
RANIA
WAZIR
18.1
LEGAL
AND
SOFT-LAW
FRAMEWORK
.
520
18.1.1
STANDARDS
.
522
18.1.2
REGULATIONS
.
522
18.2
AL
STAKEHOLDERS
.
524
18.3
FAIRNESS
IN
AL
.
525
18.3.1
BIAS
.
526
18.3.2
FAIRNESS
METRICS
.
529
18.3.3
MITIGATING
UNWANTED
BIAS
IN
AL
SYSTEMS
.
532
18.4
TRANSPARENCY
OF
AL
SYSTEMS
.
533
18.4.1
DOCUMENTING
THE
DATA
.
534
18.4.2
DOCUMENTING
THE
MODEL
.
535
18.4.3
EXPLAINABILITY
.
536
18.5
CONCLUSION
.
538
18.6
IN
A
NUTSHELL
.
538
19
THE
AUTHORS
.
539
INDEX
.
545 |
| adam_txt |
TABLE
OF
CONTENTS
FOREWORD
.
XV
PREFACE
.
XVIII
ACKNOWLEDGMENTS
.
XX
1
INTRODUCTION
.
1
1.1
WHAT
ARE
DATA
SCIENCE,
MACHINE
LEARNING
AND
ARTIFICIAL
INTELLIGENCE?
.
2
1.2
DATA
STRATEGY
.
8
1.3
FROM
STRATEGY
TO
USE
CASES
.
10
1.3.1
DATA
TEAMS
.
11
1.3.2
DATA
AND
PLATFORMS
.
16
1.3.3
MODELING
AND
ANALYSIS
.
17
1.4
USE
CASE
IMPLEMENTATION
.
18
1.4.1
ITERATIVE
EXPLORATION
OF
USE
CASES
.
19
1.4.2
END-TO-END
DATA
PROCESSING
.
21
1.4.3
DATAPRODUCTS
.
22
1.5
REAL-LIFE
USE
CASE
EXAMPLES
.
22
1.5.1
VALUE
CHAIN
DIGITIZATION
(VCD)
.
22
1.5.2
MARKETING
SEGMENT
ANALYTICS
.
23
1.5.3
360
VIEW
OF
THE
CUSTOMER
.
23
1.5.4
NGO
AND
SUSTAINABILITY
USE
CASES
.
24
1.6
DELIVERING
RESULTS
.
25
1.7
IN
A
NUTSHELL
.
27
2
INFRASTRUCTURE
.
29
STEFAN
PAPP
2.1
INTRODUCTION
.
29
2.2
HARDWARE
.
31
2.2.1
DISTRIBUTED
SYSTEMS
.
34
2.2.2
HARDWARE
FOR
AL
APPLICATIONS
.
37
2.3
LINUX
ESSENTIALS
FOR
DATA
PROFESSIONALS
.
38
2.4
TERRAFORM
.
54
2.5
CLOUD
.
58
2.5.1
BASIC
SERVICES
.
61
2.5.2
CLOUD-NATIVE
SOLUTIONS
.
65
2.6
IN
A
NUTSHELL
.
68
3
DATA
ARCHITECTURE
.
69
ZOLTAN
C.
TOTH
3.1
OVERVIEW
.
69
3.1.1
MASLOW
'
S
HIERARCHY
OF
NEEDS
FOR
DATA
.
69
3.1.2
DATA
ARCHITECTURE
REQUIREMENTS
.
71
3.1.3
THE
STRUCTURE
OF
A
TYPICAL
DATA
ARCHITECTURE
.
71
3.1.4
ETL
(EXTRACT,
TRANSFORM,
LOAD)
.
72
3.1.5
ELT
(EXTRACT,
LOAD,
TRANSFORM)
.
73
3.1.6
ETLT
.
73
3.2
DATA
INGESTION
AND
INTEGRATION
.
74
3.2.1
DATASOURCES
.
74
3.2.2
TRADITIONAL
FILE
FORMATS
.
75
3.2.3
MODERN
FILE
FORMATS
.
77
3.2.4
SUMMARY
.
79
3.3
DATA
WAREHOUSES,
DATA
LAKES,
AND
LAKEHOUSES
.
79
3.3.1
DATAWAREHOUSES
.
79
3.3.2
DATA
LAKES
AND
THE
LAKEHOUSE
.
83
3.3.3
SUMMARY:
COMPARING
DATA
WAREHOUSES
TO
LAKEHOUSES
.
85
3.4
DATA
PROCESSING
AND
TRANSFORMATION
.
86
3.4.1
BIG
DATA
&
APACHE
SPARK
.
86
3.4.2
DATABRICKS
.
93
3.5
WORKFLOW
ORCHESTRATION
.
94
3.6
A
DATA
ARCHITECTURE
USE
CASE
.
96
3.7
IN
A
NUTSHELL
.
100
4
DATA
ENGINEERING
.
101
STEFAN
PAPP,
BERNHARD
ORTNER
4.1
DATA
INTEGRATION
.
102
4.1.1
DATA
PIPELINES
.
102
4.1.2
DESIGNING
DATA
PIPELINES
.
108
4.1.3
CI/CD
.
110
4.1.4
PROGRAMMING
LANGUAGES
.
112
4.1.5
KAFKA
AS
REFERENCE
ETL
TOOL
.
115
4.1.6
DESIGN
PATTERNS
.
119
4.1.7
AUTOMATION
OF
THE
STAGES
.
120
4.1.8
SIX
BUILDING
BLOCKS
OF
THE
DATA
PIPELINE
.
120
4.2
MANAGING
ANALYTICAL
MODELS
.
125
4.2.1
MODEL
DELIVERY
.
126
4.2.2
MODEL
UPDATE
.
127
4.2.3
MODEL
OR
PARAMETER
UPDATE
.
128
4.2.4
MODEL
SCALING
.
128
4.2.5
FEEDBACK
INTO
THE
OPERATIONAL
PROCESSES
.
129
4.3
IN
A
NUTSHELL
.
130
5
DATA
MANAGEMENT
.
131
STEFAN
PAPP,
BERNHARD
ORTNER
5.1
DATA
GOVERNANCE
.
133
5.1.1
DATA
CATALOG
.
134
5.1.2
DATA
DISCOVERY
.
136
5.1.3
DATA
QUALITY
.
140
5.1.4
MASTER
DATA
MANAGEMENT
.
141
5.1.5
DATA
SHARING
.
142
5.2
INFORMATION
SECURITY
.
143
5.2.1
DATA
CLASSIFICATION
.
144
5.2.2
PRIVACY
PROTECTION
.
145
5.2.3
ENCRYPTION
.
147
5.2.4
SECRETS
MANAGEMENT
.
149
5.2.5
DEFENSE
IN
DEPTH
.
150
5.3
IN
A
NUTSHELL
.
151
6
MATHEMATICS
.
153
ANNALISA
CADONNA
6.1
LINEAR
ALGEBRA
.
154
6.1.1
VECTORS
AND
MATRICES
.
154
6.1.2
OPERATIONS
BETWEEN
VECTORS
AND
MATRICES
.
157
6.1.3
LINEAR
TRANSFORMATIONS
.
160
6.1.4
EIGENVALUES,
EIGENVECTORS,
AND
EIGENDECOMPOSITION
.
161
6.1.5
OTHER
MATRIX
DECOMPOSITIONS
.
162
6.2
CALCULUS
AND
OPTIMIZATION
.
163
6.2.1
DERIVATIVES
.
164
6.2.2
GRADIENT
AND
HESSIAN
.
166
6.2.3
GRADIENT
DESCENT
.
167
6.2.4
CONSTRAINED
OPTIMIZATION
.
169
6.3
PROBABILITY
THEORY
.
170
6.3.1
DISCRETE
AND
CONTINUOUS
RANDOM
VARIABLES
.
171
6.3.2
EXPECTED
VALUE,
VARIANCE,
AND
COVARIANCE
.
174
6.3.3
INDEPENDENCE,
CONDITIONAL
DISTRIBUTIONS,
AND
BAYES
'
THEOREM
.
176
6.4
IN
A
NUTSHELL
.
177
7
STATISTICS
-
BASICS
.
179
RANIA
WAZIR,
GEORG
LANGS,
ANNALISA
CADONNA
7.1
DATA
.
180
7.2
SIMPLE
LINEAR
REGRESSION
.
181
7.3
MULTIPLE
LINEAR
REGRESSION
.
189
7.4
LOGISTIC
REGRESSION
.
191
7.5
HOW
GOOD
IS
OUR
MODEL?
.
198
7.6
IN
A
NUTSHELL
.
199
8
MACHINE
LEARNING
.
201
GEORG
LANGS,
KATHERINE
MUNRO,
RANIA
WAZIR
8.1
INTRODUCTION
.
201
8.2
BASICS:
FEATURE
SPACES
.
203
8.3
CLASSIFICATION
MODELS
.
206
8.3.1
K-NEAREST-NEIGHBOR-CLASSIFIER
.
206
8.3.2
SUPPORT
VECTOR
MACHINE
.
207
8.3.3
DECISION
TREE
.
208
8.4
ENSEMBLE
METHODS
.
209
8.4.1
BIAS
AND
VARIANCE
.
210
8.4.2
BAGGING:
RANDOM
FORESTS
.
211
8.4.3
BOOSTING:
ADABOOST
.
215
8.5
ARTIFICIAL
NEURAL
NETWORKS
AND
THE
PERCEPTRON
.
215
8.6
LEARNING
WITHOUT
LABELS
-
FINDING
STRUCTURE
.
218
8.6.1
CLUSTERING
.
218
8.6.2
MANIFOLD
LEARNING
.
219
8.6.3
GENERATIVE
MODELS
.
220
8.7
REINFORCEMENT
LEARNING
.
221
8.8
OVERARCHING
CONCEPTS
.
223
8.9
INTO
THE
DEPTH
-
DEEP
LEARNING
.
224
8.9.1
CONVOLUTIONAL
NEURAL
NETWORKS
.
224
8.9.2
TRAINING
CONVOLUTIONAL
NEURAL
NETWORKS
.
225
8.9.3
RECURRENT
NEURAL
NETWORKS
.
227
8.9.4
LONG
SHORT-TERM
MEMORY
.
228
8.9.5
AUTOENCODERS
AND
U-NETS
.
230
8.9.6
ADVERSARIAL
TRAINING
APPROACHES
.
231
8.9.7
GENERATIVE
ADVERSARIAL
NETWORKS
.
232
8.9.8
CYCLE
GANS
AND
STYLE
GANS
.
234
8.9.9
OTHER
ARCHITECTURES
AND
LEARNING
STRATEGIES
.
235
8.10
VALIDATION
STRATEGIES
FOR MACHINE
LEARNING
TECHNIQUES
.
235
8.11
CONCLUSION
.
237
8.12
IN
A
NUTSHELL
.
237
9
BUILDING
GREAT
ARTIFICIAL
INTELLIGENCE
.
239
DANKO
NIKOLIC
9.1
HOW
AL
RELATES
TO
DATA
SCIENCE
AND
MACHINE
LEARNING
.
239
9.2
A
BRIEF
HISTORY
OF
AL
.
243
9.3
FIVE
RECOMMENDATIONS
FOR
DESIGNING
AN
AL
SOLUTION
.
245
9.3.1
RECOMMENDATION
NO.
1:
BE
PRAGMATIC
.
245
9.3.2
RECOMMENDATION
NO.
2:
MAKE
IT
EASIER
FOR
MACHINES
TO
LEARN
-
CREATE
INDUCTIVE
BIASES
.
247
9.3.3
RECOMMENDATION
NO.
3:
PERFORM
ANALYTICS
.
252
9.3.4
RECOMMENDATION
NO.
4:
BEWARE
OF
THE
SCALING
TRAP
.
254
9.3.5
RECOMMENDATION
NO.
5:
BEWARE
OF
THE
GENERALITY
TRAP
(THERE
IS
NO
SUCH
A
THING
AS
FREE
LUNCH)
.
263
9.4
HUMAN-LEVEL
INTELLIGENCE
.
268
9.5
IN
A
NUTSHELL
.
270
10
NATURAL
LANGUAGE
PROCESSING
(NLP)
.
273
KATHERINE
MUNRO
10.1
WHAT
IS
NLP
AND
WHY
IS
IT
SO
VALUABLE?
.
273
10.2
NLP
DATA
PREPARATION
TECHNIQUES
.
275
10.2.1
THE
NLP
PIPELINE
.
275
10.2.2
CONVERTING
THE
INPUT
FORMAT
FOR
MACHINE
LEARNING
.
281
10.3
NLP
TASKS
AND
METHODS
.
283
10.3.1
RULE-BASED
(SYMBOLIC)
NLP
.
284
10.3.2
STATISTICAL
MACHINE
LEARNING
APPROACHES
.
287
10.3.3
NEURAL
NLP
.
295
10.3.4
TRANSFER
LEARNING
.
301
10.4
AT
THE
CUTTING
EDGE:
CURRENT
RESEARCH
FOCUSES
FOR
NLP
.
312
10.5
IN
A
NUTSHELL
.
314
11
COMPUTER
VISION
.
317
ROXANE
LICANDRO
11.1
WHAT
IS
COMPUTER
VISION?
.
317
11.2
A
PICTURE
PAINTS
A
THOUSAND
WORDS
.
319
11.2.1
THE
HUMAN
EYE
.
319
11.2.2
IMAGE
ACQUISITION
PRINCIPLE
.
321
11.2.3
DIGITAL
FILE
FORMATS
.
326
11.2.4
IMAGE
COMPRESSION
.
327
11.3
I
SPY
WITH
MY
LITTLE
EYE
SOMETHING
THAT
IS
.
328
11.3.1
COMPUTATIONAL
PHOTOGRAPHY
AND
IMAGE
MANIPULATION
.
330
11.4
COMPUTER
VISION
APPLICATIONS
&
FUTURE
DIRECTIONS
.
334
11.4.1
IMAGE
RETRIEVAL
SYSTEMS
.
334
11.4.2
OBJECT
DETECTION,
CLASSIFICATION
AND
TRACKING
.
337
11.4.3
MEDICAL
COMPUTER
VISION
.
338
11.5
MAKING
HUMANS
SEE
.
341
11.6
IN
A
NUTSHELL
.
343
12
MODELLING
AND
SIMULATION
-
CREATE
YOUR
OWN
MODELS
.
347
GUNTHER
ZAUNER,
WOLFGANG
WEIDINGER
12.1
INTRODUCTION
.
347
12.2
GENERAL
ASPECTS
.
349
12.3
MODELLING
TO
ANSWER
QUESTIONS
.
349
12.4
REPRODUCIBILITY
AND
MODEL
LIFECYCLE
.
351
12.4.1
THE
LIFECYCLE
OF
A
MODELLING
AND
SIMULATION
QUESTION
.
352
12.4.2
PARAMETER
AND
OUTPUT
DEFINITION
.
354
12.4.3
DOCUMENTATION
.
357
12.4.4
VERIFICATION
AND
VALIDATION
.
357
12.5
METHODS
.
361
12.5.1
ORDINARY
DIFFERENTIAL
EQUATIONS
(DDES)
.
361
12.5.2
SYSTEM
DYNAMICS
(SD)
.
362
12.5.3
DISCRETE
EVENT
SIMULATION
.
365
12.5.4
AGENT-BASED
MODELLING
.
368
12.6
MODELLING
AND
SIMULATION
EXAMPLES
.
371
12.6.1
DYNAMIC
MODELLING
OF
RAILWAY
NETWORKS
FOR
OPTIMAL
PATHFINDING
USING
AGENT-BASED
METHODS
AND
REINFORCEMENT
LEARNING
.
371
12.6.2
AGENT-BASED
COVID
MODELLING
STRATEGIES
.
373
12.6.3
DEEP
REINFORCEMENT
LEARNING
APPROACH
FOR
OPTIMAL
REPLENISHMENT
POLICY
IN
A
VMI
SETTING
.
378
12.7
SUMMARY
AND
LESSONS
LEARNED
.
381
12.8
IN
A
NUTSHELL
.
381
13
DATA
VISUALIZATION
.
385
BARBORA
VESELA
13.1
HISTORY
.
386
13.2
WHICH
TOOLS
TO
USE
.
391
13.3
TYPES
OF
DATA
VISUALIZATIONS
.
393
13.3.1
SCATTER
PLOT
.
394
13.3.2
LINE
CHART
.
394
13.3.3
COLUMN
AND
BAR
CHARTS
.
395
13.3.4
HISTOGRAM
.
396
13.3.5
PIE
CHART
.
397
13.3.6
BOX
PLOT
.
398
13.3.7
HEAT
MAP
.
398
13.3.8
TREE
DIAGRAM
.
399
13.3.9
OTHER
TYPES
OF
VISUALIZATIONS
.
400
13.4
SELECT
THE
RIGHT
DATA
VISUALIZATION
.
400
13.5
TIPS
AND
TRICKS
.
402
13.6
PRESENTATION
OF
DATA
VISUALIZATION
.
407
13.7
IN
A
NUTSHELL
.
407
14
DATA
DRIVEN
ENTERPRISES
.
411
MARIO
MEIR-HUBER,
STEFAN
PAPP
14.1
THE
THREE
LEVELS
OF
A
DATA
DRIVEN
ENTERPRISE
.
412
14.2
CULTURE
.
412
14.2.1
CORPORATE
STRATEGY
FOR
DATA
.
413
14.2.2
THE
CURRENT
STATE
ANALYSIS
.
415
14.2.3
CULTURE
AND
ORGANIZATION
OF
A
SUCCESSFUL
DATA
ORGANISATION
.
417
14.2.4
CORE
PROBLEM:
THE
SKILLS
GAP
.
424
14.3
TECHNOLOGY
.
426
14.3.1
THE
IMPACT
OF
OPEN
SOURCE
.
426
14.3.2
CLOUD
.
426
14.3.3
VENDOR
SELECTION
.
427
14.3.4
DATA
LAKE
FROM
A
BUSINESS
PERSPECTIVE
.
427
14.3.5
THE
ROLE
OF
IT
.
428
14.3.6
DATA
SCIENCE
LABS
.
428
14.3.7
REVOLUTION
IN
ARCHITECTURE:
THE
DATA
MESH
.
429
14.4
BUSINESS
.
431
14.4.1
BUY
AND
SHARE
DATA
.
431
14.4.2
ANALYTICAL
USE
CASE
IMPLEMENTATION
.
432
14.4.3
SELF-SERVICE
ANALYTICS
.
433
14.5
IN
A
NUTSHELL
.
433
15
LEGAL
FOUNDATION
OF
DATA
SCIENCE
.
435
BERNHARD
ORTNER
15.1
INTRODUCTION
.
435
15.2
CATEGORIES
OF
DATA
.
436
15.3
GENERAL
DATA
PROTECTION
REGULATION
.
437
15.3.1
FUNDAMENTAL
RIGHTS
OF
GDPR
.
437
15.3.2
DECLARATION
OF
CONSENT
.
438
15.3.3
RISK-ASSESSMENT
.
440
15.3.4
ANONYMIZATION
UND
PSEUDO-ANONYMIZATION
.
441
15.3.5
TYPES
OF
ANONYMIZATION
.
442
15.3.6
LAWFUL
AND
TRANSPARENT
DATA
PROCESSING
.
444
15.3.7
RIGHT
TO
DATA
DELETION
AND
CORRECTION
.
445
15.3.8
PRIVACY
BY
DESIGN
.
446
15.3.9
PRIVACY
BY
DEFAULT
.
446
15.4
EPRIVACY-REGULATION
.
446
15.5
DATA
PROTECTION
OFFICER
.
447
15.5.1
INTERNATIONAL
DATA
EXPORT
IN
FOREIGN
COUNTRIES
.
447
YYI
15.6
SECURITY
MEASURES
.
448
15.6.1
DATA
ENCRYPTION
.
449
15.7
CCPA
COMPARED
TO
GDPR
.
449
15.7.1
TERRITORIAL
SCOPE
.
450
15.7.2
OPT-IN
VS.
OPT-OUT
.
450
15.7.3
RIGHT
OF
DATA
EXPORT
.
450
15.7.4
RIGHT
NOT
TO
BE
DISCRIMINATED
AGAINST
.
451
15.8
IN
A
NUTSHELL
.
451
16
AL
IN
DIFFERENT
INDUSTRIES
.
453
STEFAN
PAPP,
MARIO
MEIR-HUBER,
WOLFGANG
WEIDINGER,
THOMAS
TREND,
MAREK
DANIS
16.1
AUTOMOTIVE
.
456
16.1.1
VISION
.
457
16.1.2
DATA
.
458
16.1.3
USE
CASES
.
458
16.1.4
CHALLENGES
.
459
16.2
AVIATION
.
461
16.2.1
VISION
.
461
16.2.2
DATA
.
462
16.2.3
USE
CASES
.
462
16.2.4
CHALLENGES
.
463
16.3
ENERGY
.
463
16.3.1
VISION
.
464
16.3.2
DATA
.
464
16.3.3
USE
CASES
.
465
16.3.4
CHALLENGES
.
466
16.4
FINANCE
.
466
16.4.1
VISION
.
466
16.4.2
DATA
.
467
16.4.3
USE
CASES
.
467
16.4.4
CHALLENGES
.
469
16.5
HEALTH
.
469
16.5.1
VISION
.
470
16.5.2
DATA
.
471
16.5.3
USE
CASES
.
471
16.5.4
CHALLENGES
.
471
16.6
GOVERNMENT
.
472
16.6.1
VISION
.
472
16.6.2
DATA
.
473
16.6.3
USE
CASES
.
473
16.6.4
CHALLENGES
.
476
16.7
ART
.
476
16.7.1
VISION
.
477
16.7.2
DATA
.
477
16.7.3
USE
CASES
.
477
16.7.4
CHALLENGES
.
478
16.8
MANUFACTURING
.
478
16.8.1
VISION
.
479
16.8.2
DATA
.
479
16.8.3
USE
CASES
.
479
16.8.4
CHALLENGES
.
480
16.9
OIL
AND
GAS
.
481
16.9.1
VISION
.
481
16.9.2
DATA
.
481
16.9.3
USE
CASES
.
482
16.9.4
CHALLENGES
.
484
16.10
SAFETY
AT
WORK
.
484
16.10.1
VISION
.
484
16.10.2
DATA
.
485
16.10.3
USE
CASES
.
485
16.10.4
CHALLENGES
.
486
16.11
RETAIL
.
487
16.11.1
VISION
.
487
16.11.2
DATA
.
487
16.11.3
USE
CASES
.
488
16.11.4
CHALLENGES
.
488
16.12
TELECOMMUNICATIONS
PROVIDER
.
489
16.12.1
VISION
.
489
16.12.2
DATA
.
490
16.12.3
USE
CASES
.
490
16.12.4
CHALLENGES
.
492
16.13
TRANSPORT
.
492
16.13.1
VISION
.
492
16.13.2
DATA
.
493
16.13.3
USE
CASES
.
493
16.13.4
CHALLENGES
.
494
16.14
TEACHING
AND
TRAINING
.
494
16.14.1
VISION
.
495
16.14.2
DATA
.
496
16.14.3 USE
CASES
.
496
16.14.4
CHALLENGES
.
497
16.15
THE
DIGITAL
SOCIETY
.
497
16.16
IN
A
NUTSHELL
.
499
17
MINDSET
AND
COMMUNITY
.
501
STEFAN
PAPP
17.1
DATA-DRIVEN
MINDSET
.
501
17.2
DATA
SCIENCE
CULTURE
.
504
17.2.1
START-UP
OR
CONSULTING
FIRM?
.
504
17.2.2
LABS
INSTEAD
OF
CORPORATE
POLICY
.
505
17.2.3
KEIRETSU
INSTEAD
OF
LONE
WOLF
.
505
17.2.4
AGILE
SOFTWARE
DEVELOPMENT
.
507
17.2.5
COMPANY
AND
WORK
CULTURE
.
507
17.3
ANTIPATTERNS
.
510
17.3.1
DEVALUATION
OF
DOMAIN
EXPERTISE
.
510
17.3.2
IT
WILL
TAKE
CARE
OF
IT
.
511
17.3.3
RESISTANCE
TO
CHANGE
.
511
17.3.4
KNOW-IT-ALL
MENTALITY
.
512
17.3.5
DOOM
AND
GLOOM
.
513
17.3.6
PENNY-PINCHING
.
513
17.3.7
FEAR
CULTURE
.
514
17.3.8
CONTROL
OVER
RESOURCES
.
514
17.3.9
BLIND
FAITH
IN
RESOURCES
.
515
17.3.10 THE
SWISS
ARMY
KNIFE
.
516
17.3.11
OVER-ENGINEERING
.
516
17.4
IN
A
NUTSHELL
.
517
18
TRUSTWORTHY
AL
.
519
RANIA
WAZIR
18.1
LEGAL
AND
SOFT-LAW
FRAMEWORK
.
520
18.1.1
STANDARDS
.
522
18.1.2
REGULATIONS
.
522
18.2
AL
STAKEHOLDERS
.
524
18.3
FAIRNESS
IN
AL
.
525
18.3.1
BIAS
.
526
18.3.2
FAIRNESS
METRICS
.
529
18.3.3
MITIGATING
UNWANTED
BIAS
IN
AL
SYSTEMS
.
532
18.4
TRANSPARENCY
OF
AL
SYSTEMS
.
533
18.4.1
DOCUMENTING
THE
DATA
.
534
18.4.2
DOCUMENTING
THE
MODEL
.
535
18.4.3
EXPLAINABILITY
.
536
18.5
CONCLUSION
.
538
18.6
IN
A
NUTSHELL
.
538
19
THE
AUTHORS
.
539
INDEX
.
545 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Papp, Stefan Weidinger, Wolfgang Munro, Katherine |
| author_GND | (DE-588)1161133895 (DE-588)1190386240 (DE-588)1257369903 |
| author_facet | Papp, Stefan Weidinger, Wolfgang Munro, Katherine |
| author_role | aut aut aut |
| author_sort | Papp, Stefan |
| author_variant | s p sp w w ww k m km |
| building | Verbundindex |
| bvnumber | BV047960371 |
| classification_rvk | ST 302 |
| classification_tum | DAT 700 |
| ctrlnum | (OCoLC)1288569424 (DE-599)DNB1247124304 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| format | Book |
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| id | DE-604.BV047960371 |
| illustrated | Illustrated |
| index_date | 2024-07-03T19:40:19Z |
| indexdate | 2024-10-29T15:01:23Z |
| institution | BVB |
| institution_GND | (DE-588)1064064051 |
| isbn | 9781569908860 1569908869 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033341574 |
| oclc_num | 1288569424 |
| open_access_boolean | |
| owner | DE-210 DE-12 DE-91 DE-BY-TUM DE-20 |
| owner_facet | DE-210 DE-12 DE-91 DE-BY-TUM DE-20 |
| physical | XX, 553 Seiten Illustrationen, Diagramme 24 cm Enthält: Online-Ressource |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Carl Hanser Verlag |
| record_format | marc |
| spelling | Papp, Stefan Verfasser (DE-588)1161133895 aut The handbook of data science and AI generate value from data with machine learning and data analytics Stefan Papp, Wolfgang Weidinger, Katherine Munro, Bernhard Ortner, Annalisa Cadonna [und weitere] Munich Carl Hanser Verlag [2022] © 2022 XX, 553 Seiten Illustrationen, Diagramme 24 cm Enthält: Online-Ressource txt rdacontent n rdamedia nc rdacarrier Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Big Data (DE-588)4802620-7 gnd rswk-swf Data Science (DE-588)1140936166 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Algorithmen Business Intelligence Data Engineering Data Scientist Datenanalyse Datenstrategie Deep Learning Machine Learning Statistik INF2022 Data Science (DE-588)1140936166 s Big Data (DE-588)4802620-7 s Künstliche Intelligenz (DE-588)4033447-8 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Weidinger, Wolfgang Verfasser (DE-588)1190386240 aut Munro, Katherine Verfasser (DE-588)1257369903 aut Hanser Publications (DE-588)1064064051 pbl Erscheint auch als Online-Ausgabe, PDF 978-1-56990-887-7 (DE-604)BV047961078 Erscheint auch als Online-Ausgabe, EPUB 978-1-56990-888-4 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033341574&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p vlb 20211208 DE-101 https://d-nb.info/provenance/plan#vlb |
| spellingShingle | Papp, Stefan Weidinger, Wolfgang Munro, Katherine The handbook of data science and AI generate value from data with machine learning and data analytics Künstliche Intelligenz (DE-588)4033447-8 gnd Big Data (DE-588)4802620-7 gnd Data Science (DE-588)1140936166 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4802620-7 (DE-588)1140936166 (DE-588)4193754-5 |
| title | The handbook of data science and AI generate value from data with machine learning and data analytics |
| title_auth | The handbook of data science and AI generate value from data with machine learning and data analytics |
| title_exact_search | The handbook of data science and AI generate value from data with machine learning and data analytics |
| title_exact_search_txtP | The handbook of data science and AI generate value from data with machine learning and data analytics |
| title_full | The handbook of data science and AI generate value from data with machine learning and data analytics Stefan Papp, Wolfgang Weidinger, Katherine Munro, Bernhard Ortner, Annalisa Cadonna [und weitere] |
| title_fullStr | The handbook of data science and AI generate value from data with machine learning and data analytics Stefan Papp, Wolfgang Weidinger, Katherine Munro, Bernhard Ortner, Annalisa Cadonna [und weitere] |
| title_full_unstemmed | The handbook of data science and AI generate value from data with machine learning and data analytics Stefan Papp, Wolfgang Weidinger, Katherine Munro, Bernhard Ortner, Annalisa Cadonna [und weitere] |
| title_short | The handbook of data science and AI |
| title_sort | the handbook of data science and ai generate value from data with machine learning and data analytics |
| title_sub | generate value from data with machine learning and data analytics |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Big Data (DE-588)4802620-7 gnd Data Science (DE-588)1140936166 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Künstliche Intelligenz Big Data Data Science Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033341574&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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