Data Science, Analytics and Machine Learning with R explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning.
In addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear.
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Table of Contents
Part I: Introduction 1. Overview of Data Science, Analytics, and Machine Learning 2. Introduction to the R Language
Part II: Applied Statistics and Data Visualization 3. Variables and Measurement Scales 4. Descriptive and Probabilistic Statistics 5. Hypotheses Tests 6. Data Visualization and Multivariate Graphs
Part III: Data Mining and Preparation 7. Building Handcrafted Robots 8. Using APIs to Collect Data 9. Managing Data
Part IV: Unsupervised Machine Learning Techniques 10. Cluster Analysis 11. Factorial and Principal Component Analysis (PCA) 12. Association Rules and Correspondence Analysis
Part V: Supervised Machine Learning Techniques 13. Simple and Multiple Regression Analysis 14. Binary, Ordinal and Multinomial Regression Analysis 15. Count-Data and Zero-Inflated Regression Analysis 16. Generalized Linear Mixed Models
Part VI: Improving Performance and Introduction to Deep Learning 17. Support Vector Machine 18. CART (Classification and Regression Trees) 19. Bagging, Boosting and Uplift (Persuasion) Modeling 20. Random Forest 21. Artificial Neural Network 22. Introduction to Deep Learning
Part VII: Spatial Analysis 23. Working on Shapefiles 24. Dealing with Simple Features Objects 25. Raster Objects 26. Exploratory Spatial Analysis
Part VII: Adding Value to your Work 27. Enhanced and Interactive Graphs 28. Dashboards with R
Authors
Luiz Paulo Favero Economics, Business Administration and Accounting College of the University of Sao Paulo, Brazil/ Faculdade de Economia, Administracao e Contabilidade, Universidade de Sao Paulo, Brazil.
Dr. F�vero is a Full Professor at the Economics, Business Administration and Accounting College and at the Polytechnic School of the University of Sao Paulo (FEAUSP and EPUSP), where he teaches Data Science, Data Analysis, Multivariate Modeling, Machine and Deep Learning and Operational Research to undergraduate, Master's and Doctorate students. He has a Post-Doctorate degree in Data Analysis and Econometrics from Columbia University in New York. He is a tenured Professor by FEA/USP (with greater focus on Quantitative Modeling). He has a degree in Engineering from USP Polytechnic School, a post-graduate degree in Business Administration from Get�lio Vargas Foundation (FGV/SP), and he has received the titles of Master and PhD in Data Science and Quantitative Methods applied to Organizational Economics from FEA/USP. He is a Visiting Professor at the Federal University of Sao Paulo (UNIFESP), Dom Cabral Foundation, Get�lio Vargas Foundation, FIA, FIPE and MONTVERO. He has authored or co-authored 9 books and he is the founder and former editor-in-chief of the International Journal of Multivariate Data Analysis. He is member and founder of the Latin American Academy of Data Science. He is a consultant to companies operating in sectors such as retail, industry, mining, banks, insurance and healthcare, with the use of Data Analysis, Machine and Deep Learning, Big Data and AI platforms, such as R, Python, SAS, Stata and IBM SPSS. Dr. F�vero is a Full Professor at the Economics, Business Administration and Accounting College and at the Polytechnic School of the University of Sao Paulo (FEAUSP and EPUSP), where he teaches Data Science, Data Analysis, Multivariate Modeling, Machine and Deep Learning and Operational Research to undergraduate, Master's and Doctorate students. He has a Post-Doctorate degree in Data Analysis and Econometrics from Columbia University in New York. He is a tenured Professor by FEA/USP (with greater focus on Quantitative Modeling). He has a degree in Engineering from USP Polytechnic School, a post-graduate degree in Business Administration from Get�lio Vargas Foundation (FGV/SP), and he has received the titles of Master and PhD in Data Science and Quantitative Methods applied to Organizational Economics from FEA/USP. He is a Visiting Professor at the Federal University of Sao Paulo (UNIFESP), Dom Cabral Foundation, Get�lio Vargas Foundation, FIA, FIPE and MONTVERO. He has authored or co-authored 9 books and he is the founder and former editor-in-chief of the International Journal of Multivariate Data Analysis. He is member and founder of the Latin American Academy of Data Science. He is a consultant to companies operating in sectors such as retail, industry, mining, banks, insurance and healthcare, with the use of Data Analysis, Machine and Deep Learning, Big Data and AI platforms, such as R, Python, SAS, Stata and IBM SPSS.
Patricia Belfiore Associate Professor, Federal University of ABC (UFABC)/ Federal University of ABC, Brazil.
Dr. Belfiore is Associate Professor at the Federal University of ABC (UFABC), where she teaches Data Science, Statistics, Operational Research, Production Planning and Control, and Programming and Algorithms Development to Engineering students. She has a master's in electrical engineering and a PhD in production engineering from the Polytechnic School of the University of Sao Paulo (EPUSP). She has a post-doctorate degree in Operational Research and Computer Programming from Columbia University in New York. She takes part in several research and consultancy projects in the fields of modeling, optimization and programming. She has taught Operational Research, Multivariate Data Analysis and Operations Research and Logistics to undergraduate and master's students at FEI University Center and at the Arts, Sciences and Humanities College of the University of Sao Paulo (EACH/USP). Her main research interests are in the fields of modeling, simulation, combinatorial optimization, heuristics and computer programming. She is the author/co-author of 9 books. She is a consultant to companies operating in sectors such as retail, industry, banks, insurance and healthcare, with the use of Process Simulation and Optimization, Data Analysis, and Machine and Deep Learning platforms, such as R, Python, Stata, IBM SPSS and ProModel.
Rafael de Freitas Souza Economics, Business Administration and Accounting College of Ribeirao Preto, University of S�o Paulo, Brazil.
Dr. Freitas Souza is Assistant Professor at the Economics, Business Administration and Accounting College of Ribeirao Preto of the University of S�o Paulo (FEARPUSP), where he teaches Programming Languages, Data Science and Analytics, Algorithm Design and Algorithm Development. He has a PhD in Business Management from the Economics, Business Administration and Accounting College of the University of S�o Paulo (FEAUSP). His main research interests are in the fields of Performance Management (Private and Public sectors) using Multivariate Modeling, Machine and Deep Learning techniques, including Spatial Analysis.
