AAVE, 122
academic connection, 90
accounting, 33–34
active learning, 27
Amazon, 26, 72–74
anomaly detection, 22–23
area bias, 41
artificial intelligence (AI), 4, 12–15
agile for, 60, 61
classic, 12, 15, 18
to COVID-19, 45–46
forecast, 59
maturity, 150–153
product management, 56–57
progress, 60
artificial neural networks, 17
association mining, 23
asymmetric encryption cryptography, 111–112
Axie Infinity, 123
bartering, 104
Big Data, 4
Big Five Personality Inventory, 23
Bitcoin, 106–108, 118
blockchain, 5–6, 103, 106–109
applications of, 5–6, 126, 137–139
capabilities and potential, 154–156
in capital markets, 128–130
cryptography and, 110–112
in cybersecurity, 140–141
and file sharing, 141–143
in finance, 125–128
history of, 104
implementations, 117–124
IoT and, 135–137
in KYC, notarization, and certificates, 133–135
and metaverse, 143–148
in shipping, 135–139
in supply chains, 130–133
types of, 112–117
working, 109–112
Bloktopia, 145–146
Bored Ape Yacht Club, 146–147
business strategy, 51–53
business understanding, 63–64, 68
Buterin, V., 107
CargoX, 138
charts, 36–38
classification models, 20
clearing, 129
ClearWay, 132
clustering, 22
coins, 104
collateral management, 129
compensation, 88–89
compliance, 6
computational intelligence, 17
computer scientists, 92–93
computer vision and algorithmic bias, 80–81
consortium/federated blockchain, 116–117
convergence point, 95–96
Corda, 119–120
COVID-19, 31, 32, 45–46
credit card, 105
Cross-Industry Standard Process for Data Mining (CRISP-DM), 62–67, 76–78
cryptography, 110–112
Cryptopunks, 147–148
cultural resistance to change, 100
culture, 151–152
currency, 105
Curve Finance, 121
customer insights, 48–49
cybernetics, 17
cybersecurity, 140–141
data, 150–151, 153
architecture, 54–55
driven, 98, 153
governance, 55–56
informed, 98, 101
management, 71, 82
mining, 17
preparation, 64–65
privacy, 82
understanding, 64
use case, 53–54
data-centric
culture, 97–98
organization, 99
data science, 4
core fields of, 12–18
to COVID-19, 45–46
culture, 100–102
hierarchy of needs, 62
history of, 11–12
life cycle, 67
process, 60, 62–72
subfields of, 17–18
data scientist, 70
challenge, 89
hiring, 88–90
mindset, 90–91
motivations, 87–88
skills, 85–87
teams, 89
tribes, 91–97
data strategy, 48–51
definition, 47
situations, 50
Decentraland, 122, 145
decentralization, 140
decentralized finance, 121–122
deep learning, 25–26
deployment, 66
descriptive statistics, 30–31, 38–40
deterioration of models, 58–59
Distributed Denial of Service (DDoS) attacks, 141
distributed ledger technology (DLT), 106
divorce rate, 36
DNV, 134
domain expert, 70–72
Domain Name System (DNS), 141
economics, 35
emerging technologies, 1–2
employee level, 98
encryption key, 111
ERC-721, 120
Ethereum, 107, 108, 118
evaluation, 65
exchanges, 129
file sharing, 141–143
financial analysis, 34
First Industrial Revolution, 2
5G technology, 7–8
forecasting demand, 76
Fourth Industrial Revolution, 1, 3–10
Fr8, 139
fraud prevention, 6
fund managers, 126–127
Future of Analysis, The (Tukey), 11
gene therapy, 1
gold, 105
governance, 144–145
GPT-3, 26
Haber, S., 106
hacking skills, 85–86
Hanseaticsoft, 134–135
hashing, 112
hybrid blockchain, 115–116
Hyperledger Foundation, 118–119
Hypertext Transfer Protocol (HTTP), 141–142
hypothesis testing, 32
IBM, 132–133
IMMLA, 138–139
inaccurate supervision, 27
incomplete supervision, 27
Industry 4.0, 5, 9
inexact supervision, 27
inferential statistics, 31–32, 42–43
information systems, 35
information technology (IT), 6–7
intellectual resistance to change, 100–101
Internet of Things (IoT), 6–7, 135–136, 140
interoperability, 144
InterPlanetary File System (IPFS), 119
investors, 127
issuance, 128
issuers, 125–126
iXledger, 139
IXT token, 139
journey to change, 101
Kaggle, 45, 93–96
knowledge, 150, 152
latent variable models, 23–24
leading questions bias, 41–42
Lighthill, J., 14
logical reasoning, 13
machine learning (ML), 4, 15–16
algorithm, 4
reinforcement, 18, 24–25
statistics and, 17, 29–30
supervised, 18, 19–21
types of, 18–27
unsupervised, 18, 21–24
use of, 27–28
Maersk, 132–133
major tribes, 92–96
management level, 97
marketing, 34
math and statistics knowledge, 86
maturity curve, 149
metaverse, 122, 143–148
metrology, 8–9
Microsoft Team Data Science Process, 66–69
Minsky, M., 12–14
mobile payments, 106
modeling, 65
money and payment systems, 103–106
MYCIN, 13, 14
Nakamoto, S., 103, 106
natural language processing, 27, 78–80
Netflix, 27, 48, 73
nodes, 110
nonfungible token (NFT), 120, 122–123, 145–148
online payment systems, 106
operational improvements, 48
operational technology (OT), 6–7
organizational level, 98
ownership, proof of, 144
paper-based process, 136
paper money, 105
peer-to-peer (P2P) sharing system, 142
personal resistance to change, 100
predictive maintenance, 44
preventive maintenance, 43
private blockchain, 114–115
problem definition, 70–71, 73–83
production, 34–35
public blockchain, 112–114
quality control, 5, 34
quantitative specialists, 94–95
quantum computing, 8–9
recommender system, 72–73
regression models, 20–21
regulators, 127–128
reinforcement learning, 18, 24–25
resistance to change, 100–101
robotics, 9, 10
sales and trading, 128–129
sampling biases, 40
Samuel, A., 15
Sandbox, 123, 146
Second Industrial Revolution, 2
selection bias, 40–41
self-selection bias, 41
semisupervised learning, 27
sentiment analysis, 79–80
settlement, 130
Shipowner.io, 139
SHIP token, 139
small/minor tribes, 96–97
social desirability bias, 42
Speech Understanding Research program, 14
Spotify, 27, 48–49, 72, 74
Star Atlas, 123–124
statisticians, 93–94
statistics, 17, 28–29
applications of, 33–35
benefits of, 32–33
descriptive, 30–31, 38–40
history about, 29–30
inferential, 31–32, 42–43
pitfalls of, 35–43
Stornetta, W. S., 106
substantive expertise, 86
supervised learning, 18, 19–21
supply chains, 130–133
symmetric encryption cryptography, 111
talent, 152, 153
Team Data Science Process (TDSP), 66, 78–79
technological disruption, 1–3
technology stack, 89–90
Tesseract Academy’s 2-actor process, 69–73, 81–83
Third Industrial Revolution, 3
3D printing
tokenization, 137
traceability, 5
TradeLens, 132–133
Tukey, J., 11
uniqueness, 145
Uniswap, 121
unpredictability, 57–58
unsupervised learning, 18, 21–24
U.S. dollar, 105–106
User Interface/User Experience (UI/UX), 74
value transfer, 144
Vitess, 54
Wald, A., 41
weak supervision/weakly supervised learning, 27
Yearn Finance, 121
Zappo, 78–79
Zookla, 73