Practical statistics for data scientists pdf download

Ellison Publisher: Royal Society of Chemistry ISBN: Category: Science Page: View: "Completely revised and updated, the second edition contains new sections on method validation, measurement uncertainty, effective experimental design and proficiency testing. Hence, a formal training in statistics is indispensable for data scientists. If you are keen on getting your foot into the lucrative data science and analysis universe, you need to have a fundamental understanding of statistical analysis.

Besides, Python is a versatile programming language you need to master to become a career data scientist. As a data scientist, you will identify, clean, explore, analyze, and interpret trends or possible patterns in complex data sets.

The explosive growth of Big Data means you have to manage enormous amounts of data, clean it, manipulate it, and process it. Only then the most relevant data can be used. And Python's focus on simplicity makes it relatively easy for you to learn. Importantly, the ease of performing repetitive tasks saves you precious time. You will learn how to implement elementary data science tools and algorithms from scratch.

The book contains an in-depth theoretical and analytical explanation of all data science concepts and also includes dozens of hands-on, real-life projects that will help you understand the concepts better.

The ready-to-access Python codes at various places right through the book are aimed at shortening your learning curve. The main goal is to present you with the concepts, the insights, the inspiration, and the right tools needed to dive into coding and analyzing data in Python. The main benefit of purchasing this book is you get quick access to all the extra content provided with this book--Python codes, exercises, references, and PDFs--on the publisher's website, at no extra price.

You get to experiment with the practical aspects of Data Science right from page 1. Beginners in Python and statistics will find this book extremely informative, practical, and helpful. Even if you aren't new to Python and data science, you'll find the hands-on projects in this book immensely helpful. The aim is to explain statistical techniques using data relating to relevant geographical, geospatial, earth and environmental science examples, employing graphics as well as mathematical notation for maximum clarity.

Advice is given on asking the appropriate preliminary research questions to ensure that the correct data is collected for the chosen statistical analysis method. The book offers a practical guide to making the transition from understanding principles of spatial and non-spatial statistical techniques to planning a series analyses and generating results using statistical and spreadsheet computer software. Learning outcomes included in each chapter International focus Explains the underlying mathematical basis of spatial and non-spatial statistics Provides an geographical, geospatial, earth and environmental science context for the use of statistical methods Written in an accessible, user-friendly style Datasets available on accompanying website at www.

Publisher: CRC Press ISBN: Category: Page: View: Designed as a textbook for a one or two-term introduction to mathematical statistics for students training to become data scientists, Foundations of Statistics for Data Scientists: With R and Python is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modelling.

The book assumes knowledge of basic calculus, so the presentation can focus on 'why it works' as well as 'how to do it. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python.

The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists, such as Bayesian inference, generalized linear models for non-normal responses e. The nearly exercises are grouped into "Data Analysis and Applications" and "Methods and Concepts. The book's website has expanded R, Python, and Matlab appendices and all data sets from the examples and exercises. She has a long-term experience in teaching statistics courses to students of Data Science, Mathematics, Statistics, Computer Science, and Business Administration and Engineering.

The authors depart from the typical approaches taken by many conventional mathematical statistics textbooks by placing more emphasis on providing the students with intuitive and practical interpretations of those methods with the aid of R programming codes I find its particular strength to be its intuitive presentation of statistical theory and methods without getting bogged down in mathematical details that are perhaps less useful to the practitioners" Mintaek Lee, Boise State University "The aspects of this manuscript that I find appealing: 1.

Univariate, Bivariate, and Multivariate Statistics Using R offers a practical and very user-friendly introduction to the. This book focuses on the implementation of statistics and data analysis through R. It deals first with the Exploratory D. Gain a thorough understanding of supervised learning algorithms by developing use cases with Python. You will study supe. Why Not C, D,…? 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, 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. 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. 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. 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 general, 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. An attribution usually includes the title, author, publisher, and ISBN. 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 we 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 and Deborah Donnell, whose patience and support made this endeavor possible.

In , John W. Tukey forged links to the engineering and computer science communities he coined the terms bit, short for binary digit, and software , and his original tenets are surprisingly 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, 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. 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.

In Python, scikit-learn supports ordinal data with the sklearn. 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. The 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.

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 is 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. Synonyms case, example, instance, observation, pattern, sample Table In Python, with the pandas library, the basic rectangular data structure is a DataFrame object. By default, an automatic integer index is created for a DataFrame based on the order of the rows.

In R, the basic rectangular data structure is a data. A data. The native R data. To overcome this deficiency, two new packages are gaining widespread use: data. Both support multilevel indexes and offer significant speedups in working with a data. Terminology Differences Terminology for rectangular data can be confusing.

Statisticians and data scientists use different terms for the same thing. For a data scientist, features are used to predict a target. Nonrectangular Data Structures There are other data structures besides rectangular data. Time series data records successive measurements of the same variable.

It is the raw material for statistical forecasting methods, and it is also a key component of the data produced by devices—the Internet of Things. Spatial data structures, which are used in mapping and location analytics, are more complex and varied than rectangular data structures.

In the object representation, the focus of the data is an object e. The field view, by contrast, focuses on small units of space and the value of a relevant metric pixel brightness, for example.

For example, a graph of a social network, such as Facebook or LinkedIn, may represent connections between people on the network. Distribution hubs connected by roads are an example of a physical network. Graph structures are useful for certain types of problems, such as network optimization and recommender systems.

Each of these data types has its specialized methodology in data science. The focus of this book is on rectangular data, the fundamental building block of predictive modeling. Graphs in Statistics In computer science and information technology, the term graph typically refers to a depiction of the connections among entities, and to the underlying data structure.

In statistics, graph is used to refer to a variety of plots and visualizations, not just of connections among entities, and the term applies only to the visualization, not to the data structure.

Synonym average Weighted mean The sum of all values times a weight divided by the sum of the weights. Synonym weighted average Median The value such that one-half of the data lies above and below. Synonym 50th percentile Percentile The value such that P percent of the data lies below. Synonym quantile Weighted median The value such that one-half of the sum of the weights lies above and below the sorted data. Trimmed mean The average of all values after dropping a fixed number of extreme values.

Synonym truncated mean Robust Not sensitive to extreme values. Synonym resistant Outlier A data value that is very different from most of the data. Synonym extreme value 8 Chapter 1: Exploratory Data Analysis At first glance, summarizing data might seem fairly trivial: just take the mean of the data.

In fact, while the mean is easy to compute and expedient to use, it may not always be the best measure for a central value. For this reason, statisticians have developed and promoted several alternative estimates to the mean.

Metrics and Estimates Statisticians often use the term estimate for a value calculated from the data at hand, to draw a distinction between what we see from the data and the theoretical true or exact state of affairs.

Hence, statisticians estimate, and data scientists measure. Mean The most basic estimate of location is the mean, or average value. The mean is the sum of all values divided by the number of values. The formula to compute the mean for a set of n values x1, x2, In data science, that distinction is not vital, so you may see it both ways. Representing the sorted values by x 1 , x 2 , Another type of mean is a weighted mean, which you calculate by multiplying each data value xi by a user-specified weight wi and dividing their sum by the sum of the weights.

For example, if we are taking the average from multiple sensors and one of the sensors is less accurate, then we might downweight the data from that sensor. For example, because of the way an online experiment was conducted, we may not have a set of data that accurately reflects all groups in the user base.

To correct that, we can give a higher weight to the values from the groups that were underrepresented. The following typographical conventions are used in this book: Italic Indicates new terms, URLs, email addresses, filenames, and file extensions. 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.

Many data science resources incorporate statistical methods but lack a deeper statistical perspective. Do you like this book?