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Data Science, Analytics and Machine Learning with R
- 1st Edition - January 23, 2023
- Authors: Luiz Paulo Favero, Patricia Belfiore, Rafael de Freitas Souza
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 4 2 7 1 - 1
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 8 5 9 2 3 - 3
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 multivari… Read more
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Request a sales quoteData 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.
- Presents a comprehensive and practical overview of machine learning, data mining and AI techniques for a broad multidisciplinary audience
- Serves readers who are interested in statistics, analytics and modeling, and those who wish to deepen their knowledge in programming through the use of R
- Teaches readers how to apply machine learning techniques to a wide range of data and subject areas
- Presents data in a graphically appealing way, promoting greater information transparency and interactive learning
Data science and data engineering professionals, as well as researchers and engineers who need to ensure consistent, accurate and reliable data across their company, lab, hospital, etc. Graduate students in data science, compute science, and big data
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
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
- No. of pages: 660
- Language: English
- Edition: 1
- Published: January 23, 2023
- Imprint: Academic Press
- Paperback ISBN: 9780128242711
- eBook ISBN: 9780323859233
LF
Luiz Paulo Favero
Dr. Fávero 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 Getúlio 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, Getúlio 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. Fávero 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 Getúlio 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, Getúlio 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.
Affiliations and expertise
Economics, Business Administration and Accounting College of the University of Sao Paulo, Brazil/ Faculdade de Economia, Administracao e Contabilidade, Universidade de Sao Paulo, BrazilPB
Patricia Belfiore
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.
Affiliations and expertise
Associate Professor, Federal University of ABC (UFABC)/ Federal University of ABC, BrazilRd
Rafael de Freitas Souza
Dr. Freitas Souza is Assistant Professor at the Economics, Business Administration and Accounting College of Ribeirao Preto of the University of São 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 São 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.
Affiliations and expertise
Economics, Business Administration and Accounting College of Ribeirao Preto, University of São Paulo, BrazilRead Data Science, Analytics and Machine Learning with R on ScienceDirect