Thinking-data-science-a-data-science-practitioners-guide

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xxContentsWorkflow-4: Features Engineering . . ......................... 355Workflow-5: Type of Task . . . ............................. 356Workflow-6: Preparing Datasets ............................. 356Workflow-7: Algorithm Selections . . ......................... 356Workflow-8: AutoML .................................... 356Workflow-9: Hyper-parameter Tuning . . ...................... 357Workflow-10: ANN Model Building ......................... 357Workflow-11: Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357Summary ............................................. 358
Chapter 1Data Science ProcessWith lucrative and millions of job offerings in data science, there are many whoaspire to be a data scientist. The assumption is you need to have a college degree instatistics, computer science, or engineering. However, today even the nontechnicalsthrive for a data scientist job. With the proper learning path, it is possible for most ofyou to become a data scientist, irrespective of your college education. This chapterstarts with the data science process and sets the path for your goal of becoming a datascientist. If you already hold a position of a data scientist, the book will provide youguidelines to become a “modern” data scientist.The first step toward becoming a data scientist is understanding the data scienceprocess. What briefly is the role of a modern data scientist? How has he developedthe AI applications with an outstanding success that we use in our everyday life? Weuse face tagging, Alexa, and autonomous cars—these are AI applications. As far asbusinesses are concerned, how do they use AI? Tech giants like Amazon build theirown recommendation systems for product selling. Banks use AI for fraud detectionand early warnings. Even governments use AI for surveillance, traffic monitoring,and so on. The applications are many. Modern data scientists developed all suchapplications.I have been using the adjective “modern” for a reason. Today, with the technologychange and the industry’s new demands, the role of a data scientist has changedappreciably. Traditionally, data scientists use the well-studied statistical solutions todevelop predictive machine learning models. They developed such models onmostly numeric data available in our traditional databases—relational,non-relational. Even if the data contained few character columns, we would convertthem into numeric, as the machine understands only the binary format. Number ofsuch columns and the number of data points in the early days of machine learningwas low enough for the machines in those days to handle. Today, the industryrequirements rely on developing machine learning (ML) models on an enormouscorpus of text and image data. Developing such models requires enormous computingresources and an altogether different data preprocessing compared to numericdata taken from traditional databases. Also, there are many technological advances© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023P. Sarang, Thinking Data Science, The Springer Series in Applied Machine Learning,https://doi.org/10.1007/978-3-031-02363-7_11
Page 1 and 2: The Springer Series in Applied Mach
Page 3 and 4: Editorial BoardOver the last decade
Page 5 and 6: Poornachandra SarangPracticing Data
Page 7 and 8: viPrefacemachine learning algorithm
Page 9 and 10: viiiPrefaceLarge Applications) is c
Page 11 and 12: Contents1 Data Science Process ....
Page 13 and 14: ContentsxiiiTree Traversal Algorith
Page 15 and 16: ContentsxvModel Fitting for Huge Da
Page 17 and 18: Contentsxvii13 BIRCH ..............
Page 19: Contentsxix18 ANN-Based Application
Page 23 and 24: AI on Image Datasets 3creates a fin
Page 25 and 26: Data Science Process 5(NLU). So, we
Page 27 and 28: Data Science Process 7data and do n
Page 29 and 30: Data Science Process 9Fig. 1.3 Data
Page 31 and 32: Data Science Process 11use techniqu
Page 33 and 34: AutoML 13Fig. 1.9 Exhaustive list o
Page 35 and 36: Hyper-Parameter Tuning 15Fig. 1.11
Page 37 and 38: Models Based on Transfer Learning 1
Page 39 and 40: Chapter 2Dimensionality ReductionCr
Page 41 and 42: Dimensionality Reduction Techniques
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Summary 51Fig. 2.25 LDA performance
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Part IClassical Algorithms: Overvie
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Chapter 3Regression AnalysisA Well-
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Regression Types 57Generally, these
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Regression Types 59Polynomial Regre
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Regression Types 61ElasticNet Regre
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Regression Types 63As before, you p
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Bayesian Linear Regression 65Fig. 3
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Bayesian Linear Regression 67You ma
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Logistic Regression 69Fig. 3.8 Sigm
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Logistic Regression 71Fig. 3.9 Asso
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Summary 73those. I discussed only a
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76 4 Decision Treegrades, etc. by b
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78 4 Decision TreeFig. 4.2 Balanced
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80 4 Decision Tree¼ 1X Ci¼1pi ð
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82 4 Decision TreeInformation gain
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84 4 Decision TreeTable 4.2 Subset
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86 4 Decision TreeTable 4.4 Subset
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88 4 Decision TreeFig. 4.6 Final tr
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90 4 Decision TreeFig. 4.7 Housing
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92 4 Decision TreeEvaluating Perfor
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94 4 Decision TreeFig. 4.9 Features
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96 4 Decision TreeFig. 4.11 Decisio
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98 5 Ensemble: Bagging and Boosting
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100 5 Ensemble: Bagging and Boostin
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Chapter 6K-Nearest NeighborsA Super
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KNN Working 133• Step-3: For each
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Advantages 135Table 6.2 Euclidean d
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Project 137those who are further aw
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Project 139#Setup arrays to store t
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Summary 141Fig. 6.7 Classification
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144 7 Naive BayesWhen to Use?A few
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146 7 Naive BayesNow we substitute
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148 7 Naive BayesNaive Bayes TypesD
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150 7 Naive BayesPreparing DatasetT
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152 7 Naive BayesThe output in this
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154 8 Support Vector MachinesFig. 8
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156 8 Support Vector MachinesComple
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164 8 Support Vector MachinesTable
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Part IIClustering: OverviewClusteri
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Part II Clustering: Overview 169fal
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Chapter 9Centroid-Based ClusteringC
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The K-Means Algorithm 173observatio
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The K-Means Algorithm 175• Elbow
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The K-Means Algorithm 177The Gap St
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The K-Means Algorithm 179ProjectWe
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The K-Medoids Algorithm 181in the c
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Summary 183Fig. 9.11 Clustering wit
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186 10 Connectivity-Based Clusterin
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Chapter 11Gaussian Mixture ModelA P
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Probability Distribution 199For a m
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Project 201Fig. 11.3 Features and t
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Determining Optimal Number of Clust
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Determining Optimal Number of Clust
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Summary 207SummaryThe Gaussian mixt
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210 12 Density-Based ClusteringFig.
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212 12 Density-Based ClusteringTher
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230 13 BIRCHTo understand the BIRCH
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232 13 BIRCH3. Apply existing clust
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234 13 BIRCHFig. 13.4 BIRCH cluster
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236 13 BIRCHYou observe that the cl
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238 14 CLARANS1. Select multiple su
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240 14 CLARANSProjectFor this proje
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242 14 CLARANSThe output shows the
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244 15 Affinity Propagation Cluster
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252 16 STING & CLIQUEFig. 16.1 STIN
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254 16 STING & CLIQUEPros/ConsHere
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256 16 STING & CLIQUEIntervals = 30
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Part IIIANN: OverviewSo far, you ha
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262 17 Artificial Neural NetworksTh
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264 17 Artificial Neural NetworksNe
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266 17 Artificial Neural NetworksIm
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268 17 Artificial Neural NetworksNo
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270 17 Artificial Neural NetworksFi
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272 17 Artificial Neural Networks
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280 17 Artificial Neural NetworksFo
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284 17 Artificial Neural Networksbi
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Chapter 18ANN-Based ApplicationsTex
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Developing NLP Applications 291This
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Developing NLP Applications 293The
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Developing NLP Applications 295bert
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Developing NLP Applications 297For
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Developing NLP Applications 299test
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Developing NLP Applications 301We w
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Developing NLP Applications 303Fig.
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Developing NLP Applications 305Mode
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Developing NLP Applications 307Fig.
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Developing NLP Applications 309Glov
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Fig. 18.15 Network summaryFig. 18.1
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Developing Image-Based Applications
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Summary 327SummaryWe have several p
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330 19 Automated Toolsseveral combi
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332 19 Automated Toolsauto-sklearn
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334 19 Automated ToolsIt produced t
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336 19 Automated ToolsFig. 19.3 Reg
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338 19 Automated Toolson the best p
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340 19 Automated ToolsFig. 19.6 Mod
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342 19 Automated Toolsestablish the
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344 19 Automated ToolsFig. 19.9 Net
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346 19 Automated ToolsThe plot for
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348 19 Automated ToolsTPOTThis is a
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Chapter 20Data Scientist’s Ultima
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Workflow-0: Quick Solution 353Workf
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Workflow-4: Features Engineering 35
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Workflow-11: Clustering 357Workflow
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