LEADER 04085nam 2200517 450 001 9910743264503321 005 20220816144639.0 010 $a981-16-5160-4 010 $a981-16-5159-0 010 $a981-16-5160-4 035 $a(CKB)4100000012008395 035 $a(MiAaPQ)EBC6710600 035 $a(Au-PeEL)EBL6710600 035 $a(OCLC)1334951996 035 $a(PPN)257353011 035 $a(EXLCZ)994100000012008395 100 $a20220513d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFoundations of data science for engineering problem solving /$fParikshit N. Mahalle [et al] 210 1$aSingapore :$cSpringer,$d[2022] 210 4$d©2022 215 $a1 online resource (125 pages) 225 1 $aStudies in Big Data ;$vVolume 94 311 0 $a981-16-5159-0 327 $aIntro -- Preface -- Contents -- About the Authors -- 1 Introduction to Data Science -- 1.1 What is Data Science? -- 1.2 Evolution with a Need for Data Science -- 1.3 Applications of Data Science -- 1.3.1 Use of Data Science in D-Mart (E-commerce and Retail Management) -- 1.3.2 Narrow Artificial Intelligence (AI) -- 1.3.3 Trustworthy Artificial Intelligence (AI) -- 1.4 Summary -- References -- 2 Data Collection and Preparation -- 2.1 Types of Data -- 2.2 Datasets -- 2.3 Taxonomy of Dataset -- 2.4 Statistical Perspective -- 2.5 Dataset Pre-processing -- 2.6 Data Cleaning -- 2.6.1 Handling Missing Values -- 2.6.2 Removing Noisy Data -- 2.7 Data Transformation -- 2.7.1 Normalization -- 2.7.2 Encoding -- 2.8 Data Reduction -- 2.8.1 Attribute Feature Selection -- 2.8.2 Dimensionality Reduction -- 2.8.3 Numerosity Reduction -- 2.9 Web Scrapping Tools -- 2.10 Summary -- References -- 3 Data Analytics and Learning Techniques -- 3.1 Data Analytics Overview -- 3.2 Machine Learning Approaches -- 3.2.1 Supervised Learning -- 3.2.2 Unsupervised Learning -- 3.2.3 Reinforcement Learning -- 3.3 Deep Learning Approaches -- 3.4 Data Science Roles -- References -- 4 Data Visualization Tools and Data Modelling -- 4.1 Need of Visualization of Data -- 4.1.1 Challenges of Data Visualization -- 4.1.2 Steps of Data Visualization -- 4.2 Visualization Tools -- 4.2.1 Importance of Usage of Tools for Visualization -- 4.2.2 MS Excel -- 4.2.3 Tableau -- 4.2.4 Matplotlib -- 4.2.5 Datawrapper -- 4.2.6 Microsoft PowerBI -- 4.3 Summary -- References -- 5 Data Science in Information, Communication and Technology -- 5.1 Introduction -- 5.2 Motivation -- 5.3 Case Study in Computer Engineering -- 5.3.1 To Choose Fastest Route to Reach Destination -- 5.3.2 To Get Food Recipe Recommendations of Our Interest -- 5.3.3 The Famous Netflix Case Study. 327 $a5.3.4 Case Study of Amazon Using Data Science -- 5.3.5 Case Study on KCC (Kisaan Call Center) -- 5.4 Summary -- References -- 6 Data Science in Civil Engineering and Mechanical Engineering -- 6.1 Introduction -- 6.2 Motivation -- 6.3 Case Studies in Civil Engineering -- 6.4 Case Studies in Mechanical Engineering -- 6.5 Summary -- References -- 7 Data Science in Clinical Decision System -- 7.1 Introduction -- 7.2 Motivation -- 7.3 Case Study in Clinical Decision System -- 7.3.1 Preventive Measures for Cardiovascular Disease Using Data Science -- 7.3.2 Case Study on COVID-19 Prediction -- 7.4 Summary -- References -- 8 Conclusions -- 8.1 Conclusions -- 8.2 Open Research Issues -- 8.3 Future Outlook -- References. 410 0$aStudies in big data ;$v94. 606 $aEngineering$xData processing 606 $aBig data 606 $aInformation visualization 615 0$aEngineering$xData processing. 615 0$aBig data. 615 0$aInformation visualization. 676 $a620.00285 700 $aMahalle$b Parikshit N.$0947748 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910743264503321 996 $aFoundations of data science for engineering problem solving$93560859 997 $aUNINA