The handbook of data science and AI generate value from data with machine learning and data analytics
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[2022]
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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 | ||
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| 650 | 0 | 7 | |a Data Science |0 (DE-588)1140936166 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Big Data |0 (DE-588)4802620-7 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |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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| 689 | 0 | 1 | |a Big Data |0 (DE-588)4802620-7 |D s |
| 689 | 0 | 2 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |D s |
| 689 | 0 | 3 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Weidinger, Wolfgang |e Verfasser |0 (DE-588)1190386240 |4 aut | |
| 700 | 1 | |a Munro, Katherine |e Verfasser |0 (DE-588)1257369903 |4 aut | |
| 710 | 2 | |a Hanser Publications |0 (DE-588)1064064051 |4 pbl | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe, PDF |z 978-1-56990-887-7 |w (DE-604)BV047961078 |
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| 883 | 1 | |8 1\p |a vlb |d 20211208 |q DE-101 |u https://d-nb.info/provenance/plan#vlb | |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-033341574 | |
Datensatz im Suchindex
| DE-BY-TUM_call_number | 0001 2023 A 2057 0002 DAT 700 2023 A 569 |
|---|---|
| DE-BY-TUM_katkey | 2708233 |
| DE-BY-TUM_location | Mag 00 |
| DE-BY-TUM_media_number | 040011121461 040009468055 |
| _version_ | 1820809137232019456 |
| 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
|
| any_adam_object | 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 |
| format | Book |
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| id | DE-604.BV047960371 |
| illustrated | Illustrated |
| indexdate | 2024-12-24T09:15:41Z |
| 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 |
| spellingShingle | Papp, Stefan Weidinger, Wolfgang Munro, Katherine The handbook of data science and AI generate value from data with machine learning and data analytics Data Science (DE-588)1140936166 gnd Big Data (DE-588)4802620-7 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)1140936166 (DE-588)4802620-7 (DE-588)4193754-5 (DE-588)4033447-8 |
| 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_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 | Data Science (DE-588)1140936166 gnd Big Data (DE-588)4802620-7 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Data Science Big Data Maschinelles Lernen Künstliche Intelligenz |
| 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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