Fundamentals of data science /
Wagh, Sanjeev,
Fundamentals of data science / Sanjeev J. Wagh, Manisha S. Bhende, and Anuradha D. Thakare. - First edition. - 1 online resource (xiv, 282 pages)
"A Chapman & Hall book."
Includes bibliographical references.
Part-I Data Science IntroductionImportance of Data ScienceNeed for Data ScienceWhat is Data ScienceData Science ProcessBusiness Intelligence and Data SciencePrerequisite for Data ScientistComponents of Data ScienceTools and Skills NeedSummaryExerciseReferencesStatistics and Probability2.1 Data Types2.2. Variable Types2.3 Statistics2.4 Sampling Techniques and Probability2.5 Information Gain and Entropy2.6 Probability Theory2.7 Probability Types2.8 Probability Distribution2.9 Bayes Theorem2.10 Inferential Statistics 2.11 SummaryExercise References3. Databases for Data Science3.1 SQL-Tool for Data Science3.1.1 Basic Statistics with SQL3.1.2 Data Munging with SQL3.1.3 Filtering, Joins and Aggregation3.1.4 Window Functions and Ordered Data3.1.5 Preparing Data for Analytics Tool3.2 NoSQL for Data Science3.2.1 Why NoSQL3.2.2 Document databases for Data Science3.2.3 Wide-Column Databases for Data Science3.2.4 Graph Databases for Data Science3.3 SummaryExerciseReferencesPart II Data Modelling and AnalyticsChapter 4: Data Science Methodology4.1 Analytics for Data Science4.2 Data Analytics Examples4.3 Data Analytics Life Cycle4.3.1 Data Discovery4.3.2 Data preparation4.3.3 Model Planning4.3.4 Model Building4.3.5 Communicate Results4.3.6 Operationalization4.4 SummaryExerciseReferencesChapter 5: Data Science Methods and Machine learning5.1 Regression Analysis5.1.1 Linear Regression 5.1.2 Logistic Regression 5.1.3 Multinomial Logistic Regression5.1.4 Time Series Models5.2 Machine Learning5.2.1 Decision Trees 5.2.2 Naïve Bayes5.2.3 Support Vector Machines5.2.4 Nearest Neighbour learning5.2.5 Clustering5.2.6 Confusion Matrix5.3 SummaryExercise ReferencesChapter 6: Data Analytics and Text Mining6.1 Text Mining6.1.1 Major Text Mining Areas6.2 Text Analytics6.2.1 Text Analysis Subtasks6.2.2 Basic Text Analysis Steps6.3 Natural Language Processing6.3.1 Major Components of NLP6.3.2 Stages of NLP6.3.3 Statistical Processing of Natural Language6.3.4 Applications of NLP6.4 SummaryExercise ReferencesPart III: Platforms for Data ScienceChapter 7: Data Science Tool: Python Basics Of PythonPython libraries: Data Frame Manipulation with Pandas, NumpyData Analysis Exploration With PythonTime Series DataClustering with PythonArch & GarchDimensionality ReductionPython for Machine LearningAlgorithms: KNN, Decision Tree, Random Forest, SVMPython IDEs for Data ScienceSummaryExerciseReferencesChapter 8: Data Science Tool: R8.1 Reading and Getting Data into R8.1.1 Reading Data into R8.1.2 Writing Data into File8.1.3 Scan function8.1.4 Built-in Datasets8.2 Ordered and Unordered Factors8.3 Arrays and Matrices8.3.1 Arrays8.3.2 Matrices8.4 Lists and Data Frames8.4.1 Lists8.4.2 Data Frames8.5 Probability Distributions8.5.1 Normal Distribution8.6 Statistical Models in R8.6.1 Model Fitting8.6.2 Marginal Effects8.7 Manipulating Objects8.7.1 Viewing Objects8.7.2 Modifying Objects8.7.3 Appending Elements8.7.4 Deleting Objects8.8 Data Distribution8.8.1 Visualizing Distributions8.8.2 Statistics in Distributions8.9 SummaryExerciseReferencesChapter 9: Data Science Tool: MATLAB9.1 Data Science Workflow and MATLAB9.2 Importing Data9.2.1 How Data is stored9.2.2 How MATLAB Represents Data9.2.3 MATLAB Data Types9.2.4 Automating the Import Process9.3 Visualizing and Filtering Data9.3.1 Plotting Data Contained in Tables9.3.2 Selecting Data from Tables9.3.3 Accessing and Creating Table Variables9.4 Performing Calculations9.4.1 Basic Mathematical Operations9.4.2 Using Vectors9.4.3 Using Functions9.4.4 Calculating Summary Statistics9.4.5 Correlations between Variables9.4.6 Accessing Subsets of Data9.4.7 Performing Calculations by Category9.5 SummaryExerciseReferencesChapter 10 : GNU Octave as a Data Science Tool10.1 Vectors and Matrices10.2 Arithmetic Operations 10.3 Set Operations10.4 Plotting Data10.5 SummaryExerciseReferencesChapter 11: Data Visualization using Tableau11.1 Introduction to Data Visualization 11.2 Tableau Basics11.3 Dimensions, Measures and Descriptive Statistics11.4 Basic Charts11.5 Dashboard Design & Principles 11.6 Special Chart Types11.7 Integrate Tableau with Google Sheets11.8 SummaryExerciseReferencesIndex
Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science. Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes Readers will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue. This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge. Features : Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets. Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools. Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice. Information is presented in an accessible way for students, researchers and academicians and professionals.
Sanjeev Wagh, working as Professor and Head in Department of Information Technology at Govt. College of Engineering, Karad. He has completed his BE (1996), ME(2000) & PhD(2009) in Computer Science & Engineering from Govt. College of Engineering, Pune & Nanded. He was full time Post Doctorate fellow at Center for TeleInfrastructure, Aalborg University, Denmark during 2013-14. He has also completed MBA (IT) from NIBM (2015), Chennai. He has total 24 years of experience in academics & research. His research interest areas are Natural Science Computing, Internet technologies & Wireless Sensor networks, Data Sciences & Analytics. He has 100+ research papers to his credit, published in International /National Journals & conferences. 4 research scholars completed PhD under his supervision from Pune University. Currently 3 research scholars are pursuing PhD under his supervision in various Indian Universities. He is fellow member of ISTE, IETE, and member of IEEE, ACM & CSI. He is co-editor for International Journals in Engineering & Technology. He has visited to Denmark (Aalborg University, Aalborg & Copenhagen) Sweden (Gothenburg University, Gothenburg), Germany (Hamburg University, Hamburg), Norway (University of Oslo), France (University of London Institute in Paris), China (Shanghai Technology Innovation Center Shanghai, delegation visit), Thailand (Kasetsart University, Bangkok), Mauritius (University of Technology, Port Louis) for academic & research purpose. Manisha S. Bhende working as Associate Professor in Dr D Y Patil Institute of Engineering Management and Research, Pune. She has completed BE(1998), ME(2007) and PhD(2017) in Computer Engineering from University of Pune and bachelors degree from Government College of Engineering, Amravati, India. Her research interests are IoT and Wireless Networks, Network Security, Cloud Computing, Data science and Machine learning, Data analytics etc. She has 39 research papers/book chapters in International, National conferences and Journals. She delivered expert talk on various domain such as Wireless Communication, Wireless Sensor networks, Data Science, Cyber Security, IoT, Embedded and Real Time Operating Systen, IPR and Innovation etc. She has Published 4 Patents and 3 Copyrights Received on her credit. She is reviewer/Examiner for PhD thesis and ME dissertations for state/National University. She is associated with PhD research centers. She is working as Editor/reviewer for various national/International repute Journals and Conferences. She is coordinator of IQAC, IPR Cell, IIP Cell, Research Cell at institute level. She is working as Subject Chairman for various Computer Engineering subjects under SavitribaiPhule Pune University (SPPU). She contributed for SPPU syllabus Content designing and revision. She received Regional young IT Professional award by CSI in 2006. She is member of ISTE, ACM, CSI, IAENG, Internet Society etc. Anuradha D. Thakare received her Ph. D in Computer Science and Engineering from SGB Amravati University, M.E. degree in Computer Engineering from SavitribaiPhule Pune University and BE degree in Computer Science and Engineering from SantGadge Baba Amravati University, Amravati, India. She is working as a Professor in Computer Engineering Department of PimpriChinchwad College of Engineering, Pune. Dr. Anuradha is Secretary of Institution of engineering & Technology Pune LN, Member of IEEE and ACM. She is PhD guide in Computer Engineering in SPPU, Pune. She has been a General Chair of IEEE International Conference ICCUBEA 2018 and Advisory member for International Conferences. She worked as reviewer for Journal of International Blood Research, IEEE transactions and Scopus Journals. She is reviewer and Examiner for PhD defence for state/National University. She has published 78 research papers in reputed Conferences and Journals with indexing in Scopus, IEEE, Web of Science, Elsevier and Springer etc. She received Research project grant and workshop grants from AICTE-AQIS, QIP-SPPU, BCUD-SPPU Pune and Maharashtra State Commission for Women. She Received Best Women Researcher Award and Best Faculty Award from International Forum on Science, Health & Engineering. She received best paper award in International Conferences. Delivered 20 expert talks on Machine Learning, Evolutionary Algorithms, Outcome Based Education etc. she worked with industries like DRDO, NCL etc for research projects. She is working as Subject Chairman for various Computer Engineering subjects under SavitribaiPhule Pune University (SPPU). She contributed for SPPU syllabus Content designing and revision.
9780429443237 0429443234 9780429811470 0429811470 9780429811456 0429811454 9780429811463 0429811462
9780429443237 Taylor & Francis
--Statistics.
--Computer Graphics--Game Programming & Design.
--Database Management--General.
QA76.9.D32
Fundamentals of data science / Sanjeev J. Wagh, Manisha S. Bhende, and Anuradha D. Thakare. - First edition. - 1 online resource (xiv, 282 pages)
"A Chapman & Hall book."
Includes bibliographical references.
Part-I Data Science IntroductionImportance of Data ScienceNeed for Data ScienceWhat is Data ScienceData Science ProcessBusiness Intelligence and Data SciencePrerequisite for Data ScientistComponents of Data ScienceTools and Skills NeedSummaryExerciseReferencesStatistics and Probability2.1 Data Types2.2. Variable Types2.3 Statistics2.4 Sampling Techniques and Probability2.5 Information Gain and Entropy2.6 Probability Theory2.7 Probability Types2.8 Probability Distribution2.9 Bayes Theorem2.10 Inferential Statistics 2.11 SummaryExercise References3. Databases for Data Science3.1 SQL-Tool for Data Science3.1.1 Basic Statistics with SQL3.1.2 Data Munging with SQL3.1.3 Filtering, Joins and Aggregation3.1.4 Window Functions and Ordered Data3.1.5 Preparing Data for Analytics Tool3.2 NoSQL for Data Science3.2.1 Why NoSQL3.2.2 Document databases for Data Science3.2.3 Wide-Column Databases for Data Science3.2.4 Graph Databases for Data Science3.3 SummaryExerciseReferencesPart II Data Modelling and AnalyticsChapter 4: Data Science Methodology4.1 Analytics for Data Science4.2 Data Analytics Examples4.3 Data Analytics Life Cycle4.3.1 Data Discovery4.3.2 Data preparation4.3.3 Model Planning4.3.4 Model Building4.3.5 Communicate Results4.3.6 Operationalization4.4 SummaryExerciseReferencesChapter 5: Data Science Methods and Machine learning5.1 Regression Analysis5.1.1 Linear Regression 5.1.2 Logistic Regression 5.1.3 Multinomial Logistic Regression5.1.4 Time Series Models5.2 Machine Learning5.2.1 Decision Trees 5.2.2 Naïve Bayes5.2.3 Support Vector Machines5.2.4 Nearest Neighbour learning5.2.5 Clustering5.2.6 Confusion Matrix5.3 SummaryExercise ReferencesChapter 6: Data Analytics and Text Mining6.1 Text Mining6.1.1 Major Text Mining Areas6.2 Text Analytics6.2.1 Text Analysis Subtasks6.2.2 Basic Text Analysis Steps6.3 Natural Language Processing6.3.1 Major Components of NLP6.3.2 Stages of NLP6.3.3 Statistical Processing of Natural Language6.3.4 Applications of NLP6.4 SummaryExercise ReferencesPart III: Platforms for Data ScienceChapter 7: Data Science Tool: Python Basics Of PythonPython libraries: Data Frame Manipulation with Pandas, NumpyData Analysis Exploration With PythonTime Series DataClustering with PythonArch & GarchDimensionality ReductionPython for Machine LearningAlgorithms: KNN, Decision Tree, Random Forest, SVMPython IDEs for Data ScienceSummaryExerciseReferencesChapter 8: Data Science Tool: R8.1 Reading and Getting Data into R8.1.1 Reading Data into R8.1.2 Writing Data into File8.1.3 Scan function8.1.4 Built-in Datasets8.2 Ordered and Unordered Factors8.3 Arrays and Matrices8.3.1 Arrays8.3.2 Matrices8.4 Lists and Data Frames8.4.1 Lists8.4.2 Data Frames8.5 Probability Distributions8.5.1 Normal Distribution8.6 Statistical Models in R8.6.1 Model Fitting8.6.2 Marginal Effects8.7 Manipulating Objects8.7.1 Viewing Objects8.7.2 Modifying Objects8.7.3 Appending Elements8.7.4 Deleting Objects8.8 Data Distribution8.8.1 Visualizing Distributions8.8.2 Statistics in Distributions8.9 SummaryExerciseReferencesChapter 9: Data Science Tool: MATLAB9.1 Data Science Workflow and MATLAB9.2 Importing Data9.2.1 How Data is stored9.2.2 How MATLAB Represents Data9.2.3 MATLAB Data Types9.2.4 Automating the Import Process9.3 Visualizing and Filtering Data9.3.1 Plotting Data Contained in Tables9.3.2 Selecting Data from Tables9.3.3 Accessing and Creating Table Variables9.4 Performing Calculations9.4.1 Basic Mathematical Operations9.4.2 Using Vectors9.4.3 Using Functions9.4.4 Calculating Summary Statistics9.4.5 Correlations between Variables9.4.6 Accessing Subsets of Data9.4.7 Performing Calculations by Category9.5 SummaryExerciseReferencesChapter 10 : GNU Octave as a Data Science Tool10.1 Vectors and Matrices10.2 Arithmetic Operations 10.3 Set Operations10.4 Plotting Data10.5 SummaryExerciseReferencesChapter 11: Data Visualization using Tableau11.1 Introduction to Data Visualization 11.2 Tableau Basics11.3 Dimensions, Measures and Descriptive Statistics11.4 Basic Charts11.5 Dashboard Design & Principles 11.6 Special Chart Types11.7 Integrate Tableau with Google Sheets11.8 SummaryExerciseReferencesIndex
Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science. Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes Readers will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue. This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge. Features : Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets. Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools. Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice. Information is presented in an accessible way for students, researchers and academicians and professionals.
Sanjeev Wagh, working as Professor and Head in Department of Information Technology at Govt. College of Engineering, Karad. He has completed his BE (1996), ME(2000) & PhD(2009) in Computer Science & Engineering from Govt. College of Engineering, Pune & Nanded. He was full time Post Doctorate fellow at Center for TeleInfrastructure, Aalborg University, Denmark during 2013-14. He has also completed MBA (IT) from NIBM (2015), Chennai. He has total 24 years of experience in academics & research. His research interest areas are Natural Science Computing, Internet technologies & Wireless Sensor networks, Data Sciences & Analytics. He has 100+ research papers to his credit, published in International /National Journals & conferences. 4 research scholars completed PhD under his supervision from Pune University. Currently 3 research scholars are pursuing PhD under his supervision in various Indian Universities. He is fellow member of ISTE, IETE, and member of IEEE, ACM & CSI. He is co-editor for International Journals in Engineering & Technology. He has visited to Denmark (Aalborg University, Aalborg & Copenhagen) Sweden (Gothenburg University, Gothenburg), Germany (Hamburg University, Hamburg), Norway (University of Oslo), France (University of London Institute in Paris), China (Shanghai Technology Innovation Center Shanghai, delegation visit), Thailand (Kasetsart University, Bangkok), Mauritius (University of Technology, Port Louis) for academic & research purpose. Manisha S. Bhende working as Associate Professor in Dr D Y Patil Institute of Engineering Management and Research, Pune. She has completed BE(1998), ME(2007) and PhD(2017) in Computer Engineering from University of Pune and bachelors degree from Government College of Engineering, Amravati, India. Her research interests are IoT and Wireless Networks, Network Security, Cloud Computing, Data science and Machine learning, Data analytics etc. She has 39 research papers/book chapters in International, National conferences and Journals. She delivered expert talk on various domain such as Wireless Communication, Wireless Sensor networks, Data Science, Cyber Security, IoT, Embedded and Real Time Operating Systen, IPR and Innovation etc. She has Published 4 Patents and 3 Copyrights Received on her credit. She is reviewer/Examiner for PhD thesis and ME dissertations for state/National University. She is associated with PhD research centers. She is working as Editor/reviewer for various national/International repute Journals and Conferences. She is coordinator of IQAC, IPR Cell, IIP Cell, Research Cell at institute level. She is working as Subject Chairman for various Computer Engineering subjects under SavitribaiPhule Pune University (SPPU). She contributed for SPPU syllabus Content designing and revision. She received Regional young IT Professional award by CSI in 2006. She is member of ISTE, ACM, CSI, IAENG, Internet Society etc. Anuradha D. Thakare received her Ph. D in Computer Science and Engineering from SGB Amravati University, M.E. degree in Computer Engineering from SavitribaiPhule Pune University and BE degree in Computer Science and Engineering from SantGadge Baba Amravati University, Amravati, India. She is working as a Professor in Computer Engineering Department of PimpriChinchwad College of Engineering, Pune. Dr. Anuradha is Secretary of Institution of engineering & Technology Pune LN, Member of IEEE and ACM. She is PhD guide in Computer Engineering in SPPU, Pune. She has been a General Chair of IEEE International Conference ICCUBEA 2018 and Advisory member for International Conferences. She worked as reviewer for Journal of International Blood Research, IEEE transactions and Scopus Journals. She is reviewer and Examiner for PhD defence for state/National University. She has published 78 research papers in reputed Conferences and Journals with indexing in Scopus, IEEE, Web of Science, Elsevier and Springer etc. She received Research project grant and workshop grants from AICTE-AQIS, QIP-SPPU, BCUD-SPPU Pune and Maharashtra State Commission for Women. She Received Best Women Researcher Award and Best Faculty Award from International Forum on Science, Health & Engineering. She received best paper award in International Conferences. Delivered 20 expert talks on Machine Learning, Evolutionary Algorithms, Outcome Based Education etc. she worked with industries like DRDO, NCL etc for research projects. She is working as Subject Chairman for various Computer Engineering subjects under SavitribaiPhule Pune University (SPPU). She contributed for SPPU syllabus Content designing and revision.
9780429443237 0429443234 9780429811470 0429811470 9780429811456 0429811454 9780429811463 0429811462
9780429443237 Taylor & Francis
--Statistics.
--Computer Graphics--Game Programming & Design.
--Database Management--General.
QA76.9.D32