Statistics for data science: leverage the power of statistics for data analysis, classification, regression, machine learning, and neural networks
Get your statistics basics right before diving into the world of data science About This Book No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs; Implement statistics in data science tasks such as data cleaning, mining, an...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Birmingham, UK
Packt Publishing
2017
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| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781788290678/?ar |
| Zusammenfassung: | Get your statistics basics right before diving into the world of data science About This Book No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs; Implement statistics in data science tasks such as data cleaning, mining, and analysis Learn all about probability, statistics, numerical computations, and more with the help of R programs Who This Book Is For This book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful. What You Will Learn Analyze the transition from a data developer to a data scientist mindset Get acquainted with the R programs and the logic used for statistical computations Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks Get comfortable with performing various statistical computations for data science programmatically In Detail Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks. By the end of the book, you will be comfortab ... |
| Beschreibung: | Online resource; title from title page (viewed January 2, 2018) |
| Umfang: | 1 Online-Ressource (1 volume) illustrations |
| ISBN: | 9781788295345 178829534X 1788290674 9781788290678 |
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| spelling | Miller, James D. VerfasserIn aut Statistics for data science leverage the power of statistics for data analysis, classification, regression, machine learning, and neural networks James D. Miller Birmingham, UK Packt Publishing 2017 1 Online-Ressource (1 volume) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Online resource; title from title page (viewed January 2, 2018) Get your statistics basics right before diving into the world of data science About This Book No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs; Implement statistics in data science tasks such as data cleaning, mining, and analysis Learn all about probability, statistics, numerical computations, and more with the help of R programs Who This Book Is For This book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful. What You Will Learn Analyze the transition from a data developer to a data scientist mindset Get acquainted with the R programs and the logic used for statistical computations Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks Get comfortable with performing various statistical computations for data science programmatically In Detail Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks. By the end of the book, you will be comfortab ... Statistics Big data Données volumineuses Statistique statistics COMPUTERS ; Data Processing |
| spellingShingle | Miller, James D. Statistics for data science leverage the power of statistics for data analysis, classification, regression, machine learning, and neural networks Statistics Big data Données volumineuses Statistique statistics COMPUTERS ; Data Processing |
| title | Statistics for data science leverage the power of statistics for data analysis, classification, regression, machine learning, and neural networks |
| title_auth | Statistics for data science leverage the power of statistics for data analysis, classification, regression, machine learning, and neural networks |
| title_exact_search | Statistics for data science leverage the power of statistics for data analysis, classification, regression, machine learning, and neural networks |
| title_full | Statistics for data science leverage the power of statistics for data analysis, classification, regression, machine learning, and neural networks James D. Miller |
| title_fullStr | Statistics for data science leverage the power of statistics for data analysis, classification, regression, machine learning, and neural networks James D. Miller |
| title_full_unstemmed | Statistics for data science leverage the power of statistics for data analysis, classification, regression, machine learning, and neural networks James D. Miller |
| title_short | Statistics for data science |
| title_sort | statistics for data science leverage the power of statistics for data analysis classification regression machine learning and neural networks |
| title_sub | leverage the power of statistics for data analysis, classification, regression, machine learning, and neural networks |
| topic | Statistics Big data Données volumineuses Statistique statistics COMPUTERS ; Data Processing |
| topic_facet | Statistics Big data Données volumineuses Statistique statistics COMPUTERS ; Data Processing |
| work_keys_str_mv | AT millerjamesd statisticsfordatascienceleveragethepowerofstatisticsfordataanalysisclassificationregressionmachinelearningandneuralnetworks |