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[null,"1.","Record Nr.",null,"UNINA9910743264503321"]
[null,null,"Autore",null,"Mahalle Parikshit N."]
[null,null,"Titolo",null,"Foundations of data science for engineering problem solving / / Parikshit N. Mahalle [et al]"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"981-16-5160-4"]
[{"$nulls":4},"981-16-5159-0"]
[null,null,"Descrizione fisica",null,"1 online resource (125 pages)"]
[null,null,"Collana",null,"Studies in Big Data ; ; Volume 94"]
[null,null,"Disciplina",null,"620.00285"]
[null,null,"Soggetti",null,"Engineering - Data processing"]
[{"$nulls":4},"Big data"]
[{"$nulls":4},"Information visualization"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- 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 "]
[{"$nulls":4},"-- 4.2.5 Datawrapper -- 4.2.6 Microsoft PowerBI -- 4.3 Summary -- References -- 5 Data Science in Information, Communication and Technology -- 5.1 Introduction -- 5.2 Motivation -- 5.3 Case Study in Computer Engineering -- 5.3.1 To Choose Fastest Route to Reach Destination -- 5.3.2 To Get Food Recipe Recommendations of Our Interest -- 5.3.3 The Famous Netflix Case Study."]
[{"$nulls":4},"5.3.4 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."]