{"product_id":"data-engineering-and-data-science-9781119841876","title":"Data Engineering and Data Science","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eDATA ENGINEERING and DATA SCIENCE\u003c\/b\u003e \u003cp\u003e\u003cb\u003eWritten and edited by one of the most prolific and well-known experts in the field and his team, this exciting new volume is the one-stop shop for the concepts and applications of data science and engineering for data scientists across many industries.\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eThe field of data science is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it is rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesn't need to know the whole spectrum of skills. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Quality Assurance in Data Science: Need, Challenges and Focus 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJasmine K.S., Ajay D. K. and Aditya Raj\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Testing and Quality Assurance 3\u003c\/p\u003e \u003cp\u003e1.3 Product Quality and Test Efforts 4\u003c\/p\u003e \u003cp\u003e1.4 Data Masking in Data Model and Associated Risks 8\u003c\/p\u003e \u003cp\u003e1.5 Prediction in Data Science 9\u003c\/p\u003e \u003cp\u003e1.6 Role of Metrics in Evaluation 20\u003c\/p\u003e \u003cp\u003e1.7 Quantity of Data in Quality Assurance 20\u003c\/p\u003e \u003cp\u003e1.8 Identifying the Right Data Sources 20\u003c\/p\u003e \u003cp\u003e1.9 Conclusion 21\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Design and Implementation of Social Media Mining -- Knowledge Discovery Methods for Effective Digital Marketing Strategies 23\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePrashant Bhat and Pradnya Malaganve\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 24\u003c\/p\u003e \u003cp\u003e2.2 Literature Review 26\u003c\/p\u003e \u003cp\u003e2.3 Novel Framework for Social Media Data Mining and Knowledge Discovery 29\u003c\/p\u003e \u003cp\u003e2.4 Classification for Comparison Analysis 34\u003c\/p\u003e \u003cp\u003e2.5 Clustering Methodology to Provide Digital Marketing Strategies 38\u003c\/p\u003e \u003cp\u003e2.6 Experimental Results 43\u003c\/p\u003e \u003cp\u003e2.7 Conclusion 45\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 A Study on Big Data Engineering Using Cloud Data Warehouse 49\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eManjunath T. N., Pushpa S. K., Ravindra S. Hegadi and Ananya Hathwar K. S.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 50\u003c\/p\u003e \u003cp\u003e3.2 Comparison Study of Different Cloud Data Warehouses 51\u003c\/p\u003e \u003cp\u003e3.3 Snowflake Cloud Data Warehouse 55\u003c\/p\u003e \u003cp\u003e3.4 Google BigQuery Cloud Data Warehouse 58\u003c\/p\u003e \u003cp\u003e3.5 Microsoft Azure Synapse Cloud Data Warehouse 61\u003c\/p\u003e \u003cp\u003e3.6 Informatica Intelligent Cloud Services (IICS) 64\u003c\/p\u003e \u003cp\u003e3.7 Conclusion 67\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Data Mining with Cluster Analysis Through Partitioning Approach of Huge Transaction Data 71\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSampath Kini K. and Karthik Pai B.H.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 72\u003c\/p\u003e \u003cp\u003e4.2 Methodology Used in Proposed Cluster Analysis System 75\u003c\/p\u003e \u003cp\u003e4.3 Literature Survey on Existing Systems 80\u003c\/p\u003e \u003cp\u003e4.4 Conclusion 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Application of Data Science in Macromodeling of Nonlinear Dynamical Systems 85\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eNagaraj S., Seshachalam D. and Jayalatha G.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 86\u003c\/p\u003e \u003cp\u003e5.2 Nonlinear Autonomous Dynamical System 89\u003c\/p\u003e \u003cp\u003e5.3 Nonlinear System - MOR 90\u003c\/p\u003e \u003cp\u003e5.4 Data Science Life Cycle 92\u003c\/p\u003e \u003cp\u003e5.5 Artificial Neural Network in Modeling 94\u003c\/p\u003e \u003cp\u003e5.6 Neuron Spiking Model Using FitzHugh-Nagumo (F-N) System 99\u003c\/p\u003e \u003cp\u003e5.7 Ring Oscillator Model 104\u003c\/p\u003e \u003cp\u003e5.8 Nonlinear VLSI Interconnect Model Using Telegraph Equation 108\u003c\/p\u003e \u003cp\u003e5.9 Macromodel Using Machine Learning 112\u003c\/p\u003e \u003cp\u003e5.10 MOR of Dynamical Systems Using POD-ANN 115\u003c\/p\u003e \u003cp\u003e5.11 Numerical Results 117\u003c\/p\u003e \u003cp\u003e5.12 Conclusion 126\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Comparative Analysis of Various Ensemble Approaches for Web Page Classification 137\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJ. Dutta, Yong Woon Kim and Dalia Dominic\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 138\u003c\/p\u003e \u003cp\u003e6.2 Literature Survey 139\u003c\/p\u003e \u003cp\u003e6.3 Material and Methods 144\u003c\/p\u003e \u003cp\u003e6.4 Ensemble Classifiers 146\u003c\/p\u003e \u003cp\u003e6.5 Results 148\u003c\/p\u003e \u003cp\u003e6.6 Conclusion 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Feature Engineering and Selection Approach Over Malicious Image 173\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eP.M. Kavitha and B. Muruganantham\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 173\u003c\/p\u003e \u003cp\u003e7.2 Feature Engineering Techniques 176\u003c\/p\u003e \u003cp\u003e7.3 Malicious Feature Engineering 182\u003c\/p\u003e \u003cp\u003e7.4 Image Processing Technique 183\u003c\/p\u003e \u003cp\u003e7.5 Image Processing Techniques for Analysis on Malicious Images 185\u003c\/p\u003e \u003cp\u003e7.6 Conclusion 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Cubic-Regression and Likelihood Based Boosting GAM to Model Drug Sensitivity for Glioblastoma 195\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSatyawant Kumar, Vinai George Biju, Ho-Kyoung Lee and Blessy Baby Mathew\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 196\u003c\/p\u003e \u003cp\u003e8.2 Literature Survey 198\u003c\/p\u003e \u003cp\u003e8.3 Materials and Methods 201\u003c\/p\u003e \u003cp\u003e8.4 Evaluations, Results and Discussions 209\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Unobtrusive Engagement Detection through Semantic Pose Estimation and Lightweight ResNet for an Online Class Environment 225\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMichael Moses Thiruthuvanathan, Balachandran Krishnan and Madhavi Rangaswamy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 226\u003c\/p\u003e \u003cp\u003e9.2 Related Work 230\u003c\/p\u003e \u003cp\u003e9.3 Proposed Methodology 234\u003c\/p\u003e \u003cp\u003e9.4 Experimentation 241\u003c\/p\u003e \u003cp\u003e9.5 Results and Discussions 245\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Building Rule Base for Decision Making -- A Fuzzy-Rough Approach 255\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSabu M. K., Neeraj Krishna M. S. and Reshmi R.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 256\u003c\/p\u003e \u003cp\u003e10.2 Literature Review 258\u003c\/p\u003e \u003cp\u003e10.3 Discretization of the Dataset Using Fuzzy Set Theory 260\u003c\/p\u003e \u003cp\u003e10.4 Description of the Dataset 260\u003c\/p\u003e \u003cp\u003e10.5 Process Involved in Proposed Work 261\u003c\/p\u003e \u003cp\u003e10.6 Experiment 262\u003c\/p\u003e \u003cp\u003e10.7 Evaluation Result 267\u003c\/p\u003e \u003cp\u003e10.8 Discussion 273\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 An Effective Machine Learning Approach to Model Healthcare Data 279\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eShaila H. Koppad, S. Anupama Kumar and Mohan Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 280\u003c\/p\u003e \u003cp\u003e11.2 Types of Data in Healthcare 281\u003c\/p\u003e \u003cp\u003e11.3 Big Data in Healthcare 283\u003c\/p\u003e \u003cp\u003e11.4 Different V’s of Big Data 284\u003c\/p\u003e \u003cp\u003e11.5 About COPD 285\u003c\/p\u003e \u003cp\u003e11.6 Methodology Implemented 290\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Recommendation Engine for Retail Domain Using Machine Learning Techniques 303\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChandrashekhara K. T., Gireesh Babu C. N. and Thungamani M.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 304\u003c\/p\u003e \u003cp\u003e12.2 Proposed System 304\u003c\/p\u003e \u003cp\u003e12.3 Results 312\u003c\/p\u003e \u003cp\u003e12.3.1 ARIMA Forecasting 312\u003c\/p\u003e \u003cp\u003e12.4 Conclusion 313\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Mining Heterogeneous Lung Cancer from Computer Tomography (CT) Scan with the Confusion Matrix 317\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDenny Dominic and Krishnan Balachandran\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 317\u003c\/p\u003e \u003cp\u003e13.2 Literature Review 319\u003c\/p\u003e \u003cp\u003e13.3 Methodology 320\u003c\/p\u003e \u003cp\u003e13.4 Result 326\u003c\/p\u003e \u003cp\u003e13.5 Conclusion and Future Scope 332\u003c\/p\u003e \u003cp\u003eReferences 332\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 ML Algorithms and Their Approach on COVID-19 Data Analysis 335\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eKambaluru Ashok, Penumalli Anvesh Reddy and Kukatlapalli Pradeep Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 336\u003c\/p\u003e \u003cp\u003e14.2 DataSet 336\u003c\/p\u003e \u003cp\u003e14.3 Types of Machine Learning Algorithms 338\u003c\/p\u003e \u003cp\u003e14.4 Conclusion 348\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Analysis and Design for the Early Stage Detection of Lung Diseases Using Machine Learning Algorithms 351\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSindhu Madhuri, Mahesh T. R., Vivek V., Shashikala H. K. and C. Saravanan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 352\u003c\/p\u003e \u003cp\u003e15.2 Machine Learning Algorithms 358\u003c\/p\u003e \u003cp\u003e15.3 Evaluation Metrics and Comparative Results for Early Detection of Lung Diseases 364\u003c\/p\u003e \u003cp\u003e15.4 Conclusion 369\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Estimation of Cancer Risk through Artificial Neural Network 373\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eK. Aditya Shastry, Sanjay H. A., Balaji N. and Karthik Pai B. H.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 373\u003c\/p\u003e \u003cp\u003e16.2 Case Studies Related to Cancer Risk Estimation Using ANN 375\u003c\/p\u003e \u003cp\u003e16.3 Datasets Used in Cancer Risk Estimation 388\u003c\/p\u003e \u003cp\u003e16.4 Discussion 397\u003c\/p\u003e \u003cp\u003e16.5 Future Scope 400\u003c\/p\u003e \u003cp\u003e16.6 Conclusion 400\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Applications and Advancements in Data Science and Analytics 409\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eT. Mamatha, A. Balaram, B. Rama Subba Reddy, C. Shoba Bindu and M. Niranjanamurthy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Data Science and Analytics in Software Testing 410\u003c\/p\u003e \u003cp\u003e17.2 Applications of Data Science and Analytics 411\u003c\/p\u003e \u003cp\u003e17.3 Selenium Testing Tool in Data Science 419\u003c\/p\u003e \u003cp\u003e17.4 Challenges and Advancements in Data Science 425\u003c\/p\u003e \u003cp\u003e17.5 Data Science and Analytics Tools 430\u003c\/p\u003e \u003cp\u003e17.6 Conclusion 438\u003c\/p\u003e \u003cp\u003eReferences 439\u003c\/p\u003e \u003cp\u003eAbout the Editors 441\u003c\/p\u003e \u003cp\u003eIndex 443\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407168938327,"sku":"9781119841876","price":168.26,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119841876.jpg?v=1730498413","url":"https:\/\/bookcurl.com\/products\/data-engineering-and-data-science-9781119841876","provider":"Book Curl","version":"1.0","type":"link"}