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Download Practical Statistics For Data Scientists 50 Essential Concepts PDF Subject: Download Practical Statistics For Data Scientists 50 Essential Concepts PDF Book on PDF Bank with Freely. For Practical Statistics For Data Scientists 50 Essential Concepts PDF More Information you can get it easily in this web. Created Date: 6/16/ AM Practical Statistics For Engineers And Scientists , , , This book provides direction in constructing regression routines that can be used with worksheet software on personal co Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python. by Peter Bruce, Andrew Bruce, and Peter Gedeck. Publisher: O'Reilly Media; 2 edition (June 9, ) ISBN Buy on Amazon
Practical statistics for data scientists pdf download
Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses an. English Pages [] Year DOWNLOAD FILE. This practical statistics for data scientists pdf download provides direction in constructing regression routines that can be used with worksheet software on personal co. This book focuses on practical statistics for data scientists pdf download implementation of statistics and data analysis through R.
It deals first with the Exploratory D. Introduces basic concepts in probability and statistics to data science students, as well as engineers and scientists A. Univariate, Bivariate, and Multivariate Statistics Using R offers a practical and very user-friendly introduction to the.
A practical source for performing essential statistical analyses and data management tasks in R Univariate, Bivariate. Gain a thorough understanding of supervised learning algorithms by developing use cases with Python.
You will study supe. This book was written to provide resource materials for teachers to use in their introductory or intermediate statistics. Exploratory Data Analysis Elements of Structured Data Further Reading Rectangular Data Data Frames and Practical statistics for data scientists pdf download Nonrectangular Data Structures Further Reading Estimates of Location Mean Median and Robust Estimates Example: Location Estimates of Population and Murder Rates Further Reading Estimates of Variability Standard Deviation and Related Estimates Estimates Based on Percentiles Example: Variability Estimates of State Population Further Reading Exploring the Data Distribution Percentiles and Boxplots Frequency Tables and Histograms Density Plots and Estimates Further Reading Exploring Binary and Categorical Data Mode Expected Value Probability Further Reading Correlation Scatterplots Further Reading Exploring Two or More Variables Hexagonal Binning and Contours Plotting Numeric Versus Numeric Data Two Categorical Variables Categorical and Numeric Data Visualizing Multiple Variables Further Reading Summary Chapter 2.
Data and Sampling Distributions Random Sampling and Sample Bias Bias Random Selection Size Versus Quality: When Does Size Matter? Why Not C, D,…? Regression and Prediction Simple Linear Regression The Regression Equation Fitted Values and Residuals Least Squares Prediction Versus Explanation Profiling Further Reading Multiple Linear Regression Example: King County Housing Data Assessing the Model Cross-Validation Model Selection and Stepwise Regression Weighted Regression Further Reading Prediction Using Regression The Dangers of Extrapolation Confidence and Prediction Intervals Factor Variables in Regression Dummy Variables Representation Factor Variables with Many Levels Ordered Factor Variables Interpreting the Regression Equation Correlated Predictors Multicollinearity Confounding Variables Interactions and Main Effects Regression Diagnostics Outliers Influential Values Heteroskedasticity, Non-Normality, and Correlated Errors Partial Residual Plots and Nonlinearity Polynomial and Spline Regression Polynomial Splines Generalized Additive Models Further Reading Summary Chapter 5.
Statistical Machine Learning K-Nearest Neighbors A Small Example: Predicting Loan Default Distance Metrics One Hot Encoder Standardization Normalization, z-Scores Choosing K KNN as a Feature Engine Tree Models A Simple Example The Recursive Partitioning Algorithm Measuring Homogeneity or Impurity Stopping the Tree from Growing Predicting a Continuous Value How Trees Are Used Further Reading Bagging and the Random Forest Bagging Random Forest Variable Importance Hyperparameters Boosting The Boosting Algorithm XGBoost Regularization: Avoiding Overfitting Hyperparameters and Cross-Validation Summary Chapter 7.
All rights reserved. Printed in the United States of America. While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, practical statistics for data scientists pdf download, including without limitation responsibility for damages resulting from the use of or reliance on this work.
Use of the information and instructions contained in this work is at your own risk. Bruce and Nancy C. Bruce, who cultivated a passion for math and science; and to our early mentors John W. Tukey and Julian Simon and our lifelong friend Geoff Watson, who helped inspire us to pursue a career in statistics.
Peter Gedeck would like to dedicate this book to Tim Clark and Christian Kramer, with deep thanks for their scientific collaboration and friendship. Table of Contents Preface. xiii 1. Exploratory Data Analysis. Data and Sampling Distributions.
Statistical Experiments and Significance Testing. Regression and Prediction. Statistical Machine Learning. Unsupervised Learning. Two of the authors came to the world of data science from the world of statistics, and have some appreciation of the contribution that statistics can make to the art of data science.
At the same time, we are well aware of the limitations of traditional statistics instruction: statistics as a discipline is a century and a half old, and most statistics textbooks and courses are laden with the momentum and inertia of an ocean liner.
All the methods in this book have some connection—historical or methodological—to the discipline of statistics. Methods that evolved mainly out of computer science, such as neural nets, are not included. Conventions Used in This Book The following typographical conventions are used in this book: Italic Indicates new terms, URLs, email addresses, filenames, and file extensions.
xiii Constant width bold Shows commands or other text that should be typed literally by the user. Key Terms Data science is a fusion of multiple disciplines, including statistics, computer science, information technology, and domain-specific fields. As a result, several different terms could be used to reference a given concept. Key terms and their synonyms will be highlighted throughout the book in a sidebar such as this.
This element signifies a tip or suggestion. This element signifies a general note. This element indicates a warning or caution. Using Code Examples In all cases, this book gives code examples first in R and then in Python. In order to avoid unnecessary repetition, we generally show only output and plots created by the R code. We also skip the code required to load the required packages and data sets. This book is here to help you get your job done.
In practical statistics for data scientists pdf download, if example code is offered with this book, you may use it in your programs and documentation.
For example, writing a program that uses several chunks of code from this book does not require permission. Answering a question by citing this book and quoting example code does not require permission. We appreciate, but do not require, attribution, practical statistics for data scientists pdf download.
An attribution usually includes the title, author, publisher, and ISBN. Copyright Peter Bruce, Andrew Bruce, and Peter Gedeck, Our unique network of experts and innovators share their knowledge and expertise through books, articles, and our online learning platform. Email [email protected] to comment or ask technical questions about this book. Gerhard Pilcher, CEO of the data mining firm Elder Research, saw early drafts of the book and gave us detailed and helpful corrections and comments.
Toshiaki Kurokawa, who translated the first edition into Japanese, did a comprehensive job of reviewing and correcting in the process. Aaron Schumacher and Walter Paczkowski thoroughly reviewed the second edition of the book and provided numerous helpful and valuable suggestions for which practical statistics for data scientists pdf download are extremely grateful. Needless to say, any errors that remain are ours alone.
Nicole Tache took over the reins for the second edition and has both guided the process effectively and provided many good editorial suggestions to improve the readability of the book for a broad audience.
We, and this book, have also benefited from the many conversations Peter has had over the years with Galit Shmueli, coauthor on other book projects. Finally, we would like to especially thank Elizabeth Bruce practical statistics for data scientists pdf download Deborah Donnell, whose patience and support made this endeavor possible. xvi Preface CHAPTER 1 Exploratory Data Analysis This chapter focuses on the first step in any data science project: exploring the data.
InJohn W. Tukey forged links to the engineering and computer science communities he coined the terms bit, short for binary digit, practical statistics for data scientists pdf download, and softwareand his original tenets are practical statistics for data scientists pdf download durable and form part of the foundation for data science. Tukey presented simple plots e. With the ready availability of computing power and expressive data analysis software, exploratory data analysis has evolved well beyond its original scope.
Key drivers of this discipline have been the rapid development of new technology, access to more and bigger data, and the greater use of quantitative analysis in a variety of disciplines.
The Internet of Things IoT is spewing out streams of information. Much of this data is unstructured: images are a collection of pixels, with each pixel containing RGB red, green, practical statistics for data scientists pdf download, blue color information. To apply the statistical concepts covered in this book, unstructured raw data must be processed and manipulated into a structured form.
One of the commonest forms of structured data is a table with rows and columns—as data might emerge from a relational database or be collected for a study. There are two basic types of structured data: numeric and categorical. Numeric data comes in two forms: continuous, such as wind speed or time duration, and discrete, such as the count of the occurrence of an event, practical statistics for data scientists pdf download.
Categorical data takes only a fixed set of values, such as a type of TV screen plasma, LCD, LED, etc. Another useful type of categorical data is ordinal data in which the categories are ordered; an example of this is a numerical rating 1, 2, 3, 4, or 5. Why do we bother with a taxonomy of data types? It turns out that for the purposes of data analysis and predictive modeling, the data type is important to help determine the type of visual display, data analysis, or statistical model.
More important, the data type for a variable determines how software will handle computations for that variable. Continuous Data that can take on any value in an interval. Synonyms: interval, float, numeric Discrete Data that can take on only integer values, such as counts. Synonyms: integer, count Categorical Data that can take on only a specific set of values representing a set of possible categories.
Synonyms: enums, enumerated, factors, nominal Binary A special case of categorical data with just two categories of values, e. Synonyms: dichotomous, logical, indicator, boolean Ordinal Categorical data that has an explicit ordering. Synonym: ordered factor Software engineers and database programmers may wonder why we even need the notion of categorical and ordinal data for analytics.
factor in R, preserving a user-specified ordering in charts, tables, practical statistics for data scientists pdf download, and models. In Python, scikit-learn supports ordinal data with the sklearn. csv is to automatically convert a text column into a factor. Subsequent operations on that column will assume that the only allowable values for that column are the ones originally imported, and assigning a new text value will introduce a warning and produce an NA missing Elements of Structured Data 3 value.
The pandas package in Python will not make such a conversion automatically. The R Tutorial website covers the taxonomy for R. Practical statistics for data scientists pdf download pandas documentation describes the different data types and how they can be manipulated in Python.
Rectangular Data The typical frame of reference for an analysis in data science is a rectangular data object, like a spreadsheet or database table, practical statistics for data scientists pdf download. Data in relational databases must be extracted and put into a single table for most data analysis and modeling tasks. Feature A column within a table practical statistics for data scientists pdf download commonly referred to as a feature.
Synonyms dependent variable, response, target, output Records A row within a table is commonly referred to as a record.
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Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide--now including examples in Python as well as R- Download Practical Statistics For Data Scientists 50 Essential Concepts PDF Subject: Download Practical Statistics For Data Scientists 50 Essential Concepts PDF Book on PDF Bank with Freely. For Practical Statistics For Data Scientists 50 Essential Concepts PDF More Information you can get it easily in this web. Created Date: 6/16/ AM Practical Statistics For Engineers And Scientists , , , This book provides direction in constructing regression routines that can be used with worksheet software on personal co
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