The Data Science Handbook, 2nd Edition

Index

a

• Absorbing Markov chain 334
• Adjusted rand index 123, 124, 133
• Agglomerative clustering 131
• AGILE development 194
• Amortized complexity 276–277
• Anonymous function 32
• Area under the ROC curve 104–105
• Artificial intelligence 77–83
• ASCII 159–161

b

• Bag‐of‐words 199, 201–203
• Bayesian network 99–100, 253–254
• Bayesian statistics 244, 252, 253
• Bernoulli random variable 225–226
• Big data 2, 12, 37, 41, 149, 163–175, 183, 243, 246, 260, 343
• Big‐O notation 272, 273, 276, 277
• Binomial distribution 234
• Bit 157, 158
• Bonferroni correction 246, 247
• BSON 184
• Byte 157, 160, 161, 287–289

c

• Cache (computer memory) 155, 278, 280
• Central limit theorem 235
• Cluster (analytics) 121, 123, 124, 128–133
• Cluster (of computers) 86, 163–168, 170–175
• Combiner 174
• Comic Sans 114
• Compiler 260, 264–266, 288, 290
• Complexity 272
• Compression 148–149, 154–155
• Confusion matrix 103–104
• Consistent estimator 247
• Constructor 263
• Continuous random variable 226, 227
• Convex function 306, 307
• Convex optimization 306–307
• Convex region 306, 307
• Convolutional neural network 315–316
• Correlation of residuals 142
• Covariance matrix 130, 236
• C.R.A.P design 112–114
• Cumulative distribution function 227

d

• DataFrame 36–38
• Decision tree 92–94
• Deep learning 101, 309–329
• Denoising 213
• Dict (Python class) 24, 30, 32
• Diffusion model 82, 320, 323
• Discrete random variable 226
• Document store 184–186
• Duck typing 266
• Dummy variables 85
• Dynamic typing 266

e

• Eigenface 123–125
• Ensemble classifier 95
• Entity recognition