Practical Statistics for Data Scientists 2nd Edition

With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised ...

Practical Statistics for Data Scientists  2nd Edition

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-explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised learning.

Practical Statistics for Data Scientists

With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher-quality dataset, even with big data How the principles of experimental design ...

Practical Statistics for Data Scientists

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 popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher-quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that "learn" from data Unsupervised learning methods for extracting meaning from unlabeled data

Statistics for Data Science

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 for Data Science

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 comfortable with performing various statistical computations for data science programmatically. Style and approach Step by step comprehensive guide with real world examples

Probability and Statistics for Data Science

His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017.

Probability and Statistics for Data Science

Probability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture." * Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner. Prerequisites are calculus, some matrix algebra, and some experience in programming. Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.

Statistics for Data Science and Policy Analysis

This book brings together the best contributions of the Applied Statistics and Policy Analysis Conference 2019. Written by leading international experts in the field of statistics, data science and policy evaluation.

Statistics for Data Science and Policy Analysis

This book brings together the best contributions of the Applied Statistics and Policy Analysis Conference 2019. Written by leading international experts in the field of statistics, data science and policy evaluation. This book explores the theme of effective policy methods through the use of big data, accurate estimates and modern computing tools and statistical modelling.

Practical Statistics for Data Scientists

With this book, you'll learn: Why exploratory data analysis is a key preliminary step in data science ; How random sampling can reduce bias and yield a higher quality dataset, even with big data ; How the principles of experimental design ...

Practical Statistics for Data Scientists

"Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you'll learn: Why exploratory data analysis is a key preliminary step in data science ; How random sampling can reduce bias and yield a higher quality dataset, even with big data ; How the principles of experimental design yield definitive answers to questions ; How to use regression to estimate outcomes and detect anomalies ; Key classification techniques for predicting which categories a record belongs to ; Statistical machine learning methods that 'learn' from data ; Unsupervised learning methods for extracting meaning from unlabeled data"--Provided by publisher.

Statistical Data Science

Exploring the relationship of data science with statistics, a well-established and principled data-analytic discipline, this book provides insights about commonalities in approach, and differences in emphasis.Featuring chapters from ...

Statistical Data Science

As an emerging discipline, data science broadly means different things across different areas. Exploring the relationship of data science with statistics, a well-established and principled data-analytic discipline, this book provides insights about commonalities in approach, and differences in emphasis. Featuring chapters from established authors in both disciplines, the book also presents a number of applications and accompanying papers. remove

Statistical Learning and Data Science

Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream.

Statistical Learning and Data Science

Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data wor

Statistics and Data Science

The 11 papers presented in this book were carefully reviewed and selected from 23 submissions. The volume also contains 7 invited talks.

Statistics and Data Science


Statistics for Data Scientists

This book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science students.

Statistics for Data Scientists

This book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science students. It uniquely combines a hands-on approach to data analysis – supported by numerous real data examples and reusable [R] code – with a rigorous treatment of probability and statistical principles. Where contemporary undergraduate textbooks in probability theory or statistics often miss applications and an introductory treatment of modern methods (bootstrapping, Bayes, etc.), and where applied data analysis books often miss a rigorous theoretical treatment, this book provides an accessible but thorough introduction into data analysis, using statistical methods combining the two viewpoints. The book further focuses on methods for dealing with large data-sets and streaming-data and hence provides a single-course introduction of statistical methods for data science.

Statistics for Data Science

But statistics with coding is the true data science that opens a door to a wide range of possibilities with lines of code and a dream. This books is great for a tech enthusiast, a thrill seeker and every other person.

Statistics for Data Science

There's a growing need for data, the world is beginning to depend on it more than ever and as demand grows so does the market of individuals that wish to learn how it works and hone or better still, acquire and improve on skills in this fast paced industry. Data science obviously is no small business and it's definitely not as easy as it seems--like some science class in high school-- this is because there is an innumerable amount of texts that have surfaced online to provide quick means to understanding how data science works without effectively doing what they say. This book combines the knowledge of coding and mathematics to make any dummy a pro in little time with the steps duly followed thus: Firstly a knowledge in coding is key to an future in data science and python is the mother language for most programs and features of data science from the easy charts to the complex algorithms. After python, the book can now be used in focus for full effective learning. Statistics is basically maths of grouping and therefore is important because data of course comes in groups or packets. Sometimes in bits and others in multitudes, so this book gives insight on how to handle simple statistics. Yes, even if you're not good at math. The Introduction to Machine learning with python is a juicy bite Into the various possibilities that python provides which of course machine learning is a key focus in the book although there are components like Artificial Intelligence but it stems from this primarily--teaching machines how to think and act like humans. Humans normally can only handle so much data but machines are more effective than we are because they are faster and can work on hundreds and thousands of things at once.Python for data analysis is an extensive application how to use python in executing programs that collect, process, arrange and analyse the data acquired from various sources. It's a straightforward approach but has a step by step breakdown for even eleven year olds to learn.Python and Data science are mother and daughter. One can't exist without either because python circulated around data collection and execution. That's how Artificial intelligence systems and Internet of Things came to be; wireless technology , cloud technology, and a host of others are products of data science, studying how machines work, interact and to teach them even more human functions.In retrospect, statistics is a part of data science that exist with just the math and no coding so it's boring. But statistics with coding is the true data science that opens a door to a wide range of possibilities with lines of code and a dream. This books is great for a tech enthusiast, a thrill seeker and every other person. Place an order and come worlds with your dreams and some code!

Principles of Managerial Statistics and Data Science

Introduces readers to the principles of managerial statistics and data science, with an emphasis on statistical literacy of business students Through a statistical perspective, this book introduces readers to the topic of data science, ...

Principles of Managerial Statistics and Data Science

Introduces readers to the principles of managerial statistics and data science, with an emphasis on statistical literacy of business students Through a statistical perspective, this book introduces readers to the topic of data science, including Big Data, data analytics, and data wrangling. Chapters include multiple examples showing the application of the theoretical aspects presented. It features practice problems designed to ensure that readers understand the concepts and can apply them using real data. Over 100 open data sets used for examples and problems come from regions throughout the world, allowing the instructor to adapt the application to local data with which students can identify. Applications with these data sets include: Assessing if searches during a police stop in San Diego are dependent on driver’s race Visualizing the association between fat percentage and moisture percentage in Canadian cheese Modeling taxi fares in Chicago using data from millions of rides Analyzing mean sales per unit of legal marijuana products in Washington state Topics covered in Principles of Managerial Statistics and Data Science include:data visualization; descriptive measures; probability; probability distributions; mathematical expectation; confidence intervals; and hypothesis testing. Analysis of variance; simple linear regression; and multiple linear regression are also included. In addition, the book offers contingency tables, Chi-square tests, non-parametric methods, and time series methods. The textbook: Includes academic material usually covered in introductory Statistics courses, but with a data science twist, and less emphasis in the theory Relies on Minitab to present how to perform tasks with a computer Presents and motivates use of data that comes from open portals Focuses on developing an intuition on how the procedures work Exposes readers to the potential in Big Data and current failures of its use Supplementary material includes: a companion website that houses PowerPoint slides; an Instructor's Manual with tips, a syllabus model, and project ideas; R code to reproduce examples and case studies; and information about the open portal data Features an appendix with solutions to some practice problems Principles of Managerial Statistics and Data Science is a textbook for undergraduate and graduate students taking managerial Statistics courses, and a reference book for working business professionals.

Statistical Inference Via Data Science a ModernDive Into R and the Tidyverse

After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple ...

Statistical Inference Via Data Science  a ModernDive Into R and the Tidyverse

"Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on visualization throughout"--

Statistics for Data Science and Business Analysis

Statistics you need in the office: Descriptive and inferential statistics, hypothesis testing, and regression analysis About This Video Learn and understand the fundamentals of statistics for Data Science and Business Analysis.

Statistics for Data Science and Business Analysis

Statistics you need in the office: Descriptive and inferential statistics, hypothesis testing, and regression analysis About This Video Learn and understand the fundamentals of statistics for Data Science and Business Analysis. A practical tutorial with case studies for people interested in Data Science and Business Analysis. In Detail This course will teach you fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations. Modern software packages and programming languages are now automating most of these activities, but this course gives you something more valuable - critical thinking abilities. This course will help you understand the fundamentals of statistics, learn how to work with different types of data, calculate correlation and covariance, and more. Careers in the field of data science are some of the most popular in the corporate world today. And, given that most businesses are starting to realize the advantages of working with the data at their disposal, this trend will only continue to grow...

Probability and Statistics for Data Science

As the title says, this book covers all the topics for probability & statistics in context of data science.

Probability and Statistics for Data Science

As the title says, this book covers all the topics for probability & statistics in context of data science. While working on data science projects, I tried to look for a reference book which can give reader holistic view of probability & statistics useful for data science, but I could not find everything at one place. So every time, I used to look for the term or topic at various places and then used to relate it in context of data science. At the end, I started writing about these topics in my blog (https://medium.com/@rathi.ankit) as my notes on probability & statistics which were well received by data science community.This book is for people who are working in data science field and want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines.The approach I have taken here is not to reinvent the wheel, so I try to give an intuitive understanding of each topic and if the user wants to dig further on that topic, he can refer to the companion GitHub notebook of this book, scan the QR code given in the book to get the link.

Modern Data Science with R

This book will help readers with some background in statistics and modest prior experience with coding develop and practice the appropriate skills to tackle complex data science projects.

Modern Data Science with R

Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world problems with data. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling statistical questions. Contemporary data science requires a tight integration of knowledge from statistics, computer science, mathematics, and a domain of application. This book will help readers with some background in statistics and modest prior experience with coding develop and practice the appropriate skills to tackle complex data science projects. The book features a number of exercises and has a flexible organization conducive to teaching a variety of semester courses.

Advanced Statistics and Data Mining for Data Science

"Data Science is an ever-evolving field.

Advanced Statistics and Data Mining for Data Science

"Data Science is an ever-evolving field. Data Science includes techniques and theories extracted from statistics, computer science, and machine learning. This video course will be your companion and ensure that you master various data mining and statistical techniques. The course starts by comparing and contrasting statistics and data mining and then provides an overview of the various types of projects data scientists usually encounter. You will then learn predictive/classification modeling, which is the most common type of data analysis project. As you move forward on this journey, you will be introduced to the three methods (statistical, decision tree, and machine learning) with which you can perform predictive modeling. Finally, you will explore segmentation modeling to learn the art of cluster analysis. Towards the end of the course, you will work with association modeling, which will allow you to perform market basket analysis."--Resource description page.

New Advances in Statistics and Data Science

Craig, P. S., Goldstein, M., Rougier, J. C., & Seheult, A. H. (2001). Bayesian
forecasting for complex systems using computer simulators. Journal of the
American Statistical Association, 96, 717–729. Cressie, N. (1993). Statistics for
spatial data, ...

New Advances in Statistics and Data Science

This book is comprised of the presentations delivered at the 25th ICSA Applied Statistics Symposium held at the Hyatt Regency Atlanta, on June 12-15, 2016. This symposium attracted more than 700 statisticians and data scientists working in academia, government, and industry from all over the world. The theme of this conference was the “Challenge of Big Data and Applications of Statistics,” in recognition of the advent of big data era, and the symposium offered opportunities for learning, receiving inspirations from old research ideas and for developing new ones, and for promoting further research collaborations in the data sciences. The invited contributions addressed rich topics closely related to big data analysis in the data sciences, reflecting recent advances and major challenges in statistics, business statistics, and biostatistics. Subsequently, the six editors selected 19 high-quality presentations and invited the speakers to prepare full chapters for this book, which showcases new methods in statistics and data sciences, emerging theories, and case applications from statistics, data science and interdisciplinary fields. The topics covered in the book are timely and have great impact on data sciences, identifying important directions for future research, promoting advanced statistical methods in big data science, and facilitating future collaborations across disciplines and between theory and practice.

Statistical Inference for Engineers and Data Scientists

A mathematically accessible textbook introducing all the tools needed to address modern inference problems in engineering and data science.

Statistical Inference for Engineers and Data Scientists

A mathematically accessible textbook introducing all the tools needed to address modern inference problems in engineering and data science.