Data Science with R: Beginner to Expert (eBook, ePUB)
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About the bookData Science with R covers the R language, statistics, graphing, and machine learning. It is beginner-friendly and easy to follow. The book explains data science concepts in simple language.The book takes you through data analysis, underlying mathematics, and prediction modeling. The mathematics chapter covers linear algebra, probability, and probability distributions. These are the foundations for learning machine learning. The book covers all the aspects of data science, from data collection to the presentation of prediction models. It covers data preparation, Regression, and C...
About the book
Data Science with R covers the R language, statistics, graphing, and machine learning. It is beginner-friendly and easy to follow. The book explains data science concepts in simple language.
The book takes you through data analysis, underlying mathematics, and prediction modeling. The mathematics chapter covers linear algebra, probability, and probability distributions. These are the foundations for learning machine learning. The book covers all the aspects of data science, from data collection to the presentation of prediction models. It covers data preparation, Regression, and Classification extensively. The chapters have to do exercises to practice machine learning.
The book has real datasets examples. The hands-on projects are a detailed step-by-step guide for data analysis and model creation. They provide a practical approach to applying data science techniques.
The book concludes with interview questions.
About the target audience
The book is a beginner-level that requires no prior knowledge.
Data Science with R covers the R language, statistics, graphing, and machine learning. It is beginner-friendly and easy to follow. The book explains data science concepts in simple language.
The book takes you through data analysis, underlying mathematics, and prediction modeling. The mathematics chapter covers linear algebra, probability, and probability distributions. These are the foundations for learning machine learning. The book covers all the aspects of data science, from data collection to the presentation of prediction models. It covers data preparation, Regression, and Classification extensively. The chapters have to do exercises to practice machine learning.
The book has real datasets examples. The hands-on projects are a detailed step-by-step guide for data analysis and model creation. They provide a practical approach to applying data science techniques.
The book concludes with interview questions.
About the target audience
The book is a beginner-level that requires no prior knowledge.
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