19 Best Books On Data Analysis (2023 Update)

Today, interpreting data is a critical decision-making factor for businesses and organizations. if your job requires you to manage and analyze all kinds of data, turn to head first data analysis, where you'll quickly learn how to collect and organize data, sort the distractions from the truth, find meaningful patterns, draw conclusions, predict the future, and present your findings to others. whether you're a product developer researching the market viability of a new product or service, a marketing manager gauging or predicting the effectiveness of a campaign, a salesperson who needs data to support product presentations, or a lone entrepreneur responsible for all of these data-intensive functions and more, the unique approach in head first data analysis is by far the most efficient way to learn what you need to know to convert raw data into a vital business tool. you'll learn how to: determine which data sources to use for collecting information assess data quality and distinguish signal from noise build basic data models to illuminate patterns, and assimilate new information into the models cope with ambiguous information design experiments to test hypotheses and draw conclusions use segmentation to organize your data within discrete market groups visualize data distributions to reveal new relationships and persuade others predict the future with sampling and probability models clean your data to make it useful communicate the results of your analysis to your audience using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, head first data analysis uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep.

The r language is widely used by statisticians for data analysis, and the popularity of r programming has therefore increased substantially in recent years. the emerging internet of things (iot) gathers increasing amounts of data that can be analyzed to gain useful insights into trends. r for data analysis in easy steps has an easy-to-follow style that will appeal to anyone who wants to produce graphic visualizations to gain insights from gathered data. r for data analysis in easy steps begins by explaining core programming principles of the r programming language, which stores data in "vectors" from which simple graphs can be plotted. next, the book describes how to create "matrices" to store and manipulate data from which graphs can be plotted to provide better insights. this book then demonstrates how to create "data frames" from imported data sets, and how to employ the "grammar of graphics" to produce advanced visualizations that can best illustrate useful insights from your data. r for data analysis in easy steps contains separate chapters on the major features of the r programming language. there are complete example programs that demonstrate how to create line graphs, bar charts, histograms, scatter graphs, box plots, and more. the code for each r script is listed, together with screenshots that illustrate the actual output when that script has been executed. the free, downloadable example r code is provided for clearer understanding. by the end of this book you will have gained a sound understanding of r programming, and be able to write your own scripts that can be executed to produce graphic visualizations for data analysis. you need have no previous knowledge of any programming language, so it's ideal for the newcomer to computer programming.

This is the first text in a generation to re-examine the purpose of the mathematical statistics course. the book’s approach interweaves traditional topics with data analysis and reflects the use of the computer with close ties to the practice of statistics. the author stresses analysis of data, examines real problems with real data, and motivates the theory. the book’s descriptive statistics, graphical displays, and realistic applications stand in strong contrast to traditional texts that are set in abstract settings.important notice: media content referenced within the product description or the product text may not be available in the ebook version.

Learn, by example, the fundamentals of data analysis as well as several intermediate to advanced methods and techniques ranging from classification and regression to bayesian methods and mcmc, which can be put to immediate use. – learn how to implement applied statistics using practical use-cases – use popular r packages to work with unstructured and structured data – book description: frequently the tool of choice for academics, r has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. the power and domain-specificity of r allows the user to express complex analytics easily, quickly, and succinctly. – starting with the basics of r and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. – packed with engaging problems and exercises, this book begins with a review of r and its syntax with packages like rcpp, ggplot2, and dplyr. from there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. – this book is engineered to be an invaluable resource through many stages of anyone's career as a data analyst. – what you will learn: gain a thorough understanding of statistical reasoning and sampling theory – employ hypothesis testing to draw inferences from your data – learn bayesian methods for estimating parameters – train regression, classification, and time series models – handle missing data gracefully using multiple imputation – identify and manage problematic data points – learn how to scale your analyses to larger data with rcpp, data.table, dplyr, and parallelization – put best practices into effect to make your job easier and facilitate reproducibility – who this book is for: budding data scientists and data analysts who are new to the concept of data analysis, or who want to build efficient analytical models in r will find this book to be useful. no prior exposure to data analysis is needed, although a fundamental understanding of the r programming language is required to get the best out of this book.

This practical guide covers the essential tasks in statistical data analysis encountered in high energy physics and provides comprehensive advice for typical questions and problems. the basic methods for inferring results from data are presented as well as tools for advanced tasks such as improving the signal-to-background ratio, correcting detector effects, determining systematics and many others. concrete applications are discussed in analysis walkthroughs. each chapter is supplemented by numerous examples and exercises and by a list of literature and relevant links. the book targets a broad readership at all career levels – from students to senior researchers. from the contents: _ fundamental concepts _ parameter estimation _ hypothesis testing _ interval estimation _ classification _ unfolding _ constrained fits _ how to deal with systematic uncertainties _ theory uncertainties _ statistical methods commonly used in high energy physics _ analysis walk-throughs _ applications in astronomy

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. as the most effective way to communicate data analysis, running case studies play a central role in this textbook. each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. learning is then consolidated by 360 practice questions and 120 data exercises.

Think big about your data! pyspark brings the powerful spark big data processing engine to the python ecosystem, letting you seamlessly scale up your data tasks and create lightning-fast pipelines. – in data analysis with python and pyspark you will learn how to: manage your data as it scales across multiple machines scale up your data programs with full confidence read and write data to and from a variety of sources and formats deal with messy data with py – spark’s data manipulation functionality discover new data sets and perform exploratory data analysis build automated data pipelines that transform, summarize, and get insights from data troubleshoot common pyspark errors creating reliable long-running jobs data analysis with python and pyspark is your guide to delivering successful python-driven data projects. packed with relevant examples and essential techniques, this practical book teaches you to build pipelines for reporting, machine learning, and other data-centric tasks. quick exercises in every chapter help you practice what you’ve learned, and rapidly start implementing pyspark into your data systems. no previous knowledge of spark is required. purchase of the print book includes a free e – book in pdf, kindle, and e – pub formats from manning publications. about the technology the spark data processing engine is an amazing analytics factory: raw data comes in, insight comes out. pyspark wraps spark’s core engine with a python-based api. it helps simplify spark’s steep learning curve and makes this powerful tool available to anyone working in the python data ecosystem. about the book data analysis with python and pyspark helps you solve the daily challenges of data science with pyspark. you’ll learn how to scale your processing capabilities across multiple machines while ingesting data from any source—whether that’s hadoop clusters, cloud data storage, or local data files. once you’ve covered the fundamentals, you’ll explore the full versatility of pyspark by building machine learning pipelines, and blending python, pandas, and pyspark code. what's inside organizing your pyspark code managing your data, no matter the size scale up your data programs with full confidence troubleshooting common data pipeline problems creating reliable long-running jobs about the reader written for data scientists and data engineers comfortable with python. about the author as a ml director for a data-driven software company, jonathan rioux uses pyspark daily. he teaches the software to data scientists, engineers, and data-savvy business analysts. table of contents 1 introduction part 1 get acquainted: first steps in pyspark 2 your first data program in pyspark 3 submitting and scaling your first pyspark program 4 analyzing tabular data with pyspark.sql 5 data frame gymnastics: joining and grouping part 2 get proficient: translate your ideas into code 6 multidimensional data frames: using pyspark with json data 7 bilingual pyspark: blending python and sql code 8 extending pyspark with python: rdd and udfs 9 big data is just a lot of small data: using pandas udfs 10 your data under a different lens: window functions 11 faster pyspark: understanding spark’s query planning part 3 get confident: using machine learning with pyspark 12 setting the stage: preparing features for machine learning 13 robust machine learning with ml pipelines 14 building custom ml transformers and estimators

This book is a comprehensive introduction to the methods and algorithms of modern data analytics. it provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. this book has been used for more than ten years in the data mining course at the technical university of munich. much of the content is based on the results of industrial research and development projects at siemens. – content – data analytics – data and relations – data preprocessing – data visualization – correlation – regression – forecasting – classification – clustering – target groups – students of computer science, mathematics and engineering – data analytics practitioners – the author runkler is principal research scientist at siemens corporate technology and professor for computer science at the technical university of munich.

Master the concepts and techniques of statistical analysis using jmp practical data analysis with jmp, third edition, highlights the powerful interactive and visual approach of jmp to introduce readers to statistical thinking and data analysis. it helps you choose the best technique for the problem at hand by using real-world cases. it also illustrates best-practice workflow throughout the entire investigative cycle, from asking valuable questions through data acquisition, preparation, analysis, interpretation, and communication of findings. the book can stand on its own as a learning resource for professionals, or it can be used to supplement a college-level textbook for an introductory statistics course. it includes varied examples and problems using real sets of data. each chapter typically starts with an important or interesting research question that an investigator has pursued. reflecting the broad applicability of statistical reasoning, the problems come from a wide variety of disciplines, including engineering, life sciences, business, and economics, as well as international and historical examples. application scenarios at the end of each chapter challenge you to use your knowledge and skills with data sets that go beyond mere repetition of chapter examples. new in the third edition, chapters have been updated to demonstrate the enhanced capabilities of jmp, including projects, graph builder, query builder, and formula depot.

Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. applications presented include scientific imaging, communication, face recognition, 3d vision, and deep networks for classification. with code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. foreword by emmanuel candès.

An engaging introduction to data science that emphasizes critical thinking over statistical techniques – an introduction to data science or statistics shouldn’t involve proving complex theorems or memorizing obscure terms and formulas, but that is exactly what most introductory quantitative textbooks emphasize. in contrast, thinking clearly with data focuses, first and foremost, on critical thinking and conceptual understanding in order to teach students how to be better consumers and analysts of the kinds of quantitative information and arguments that they will encounter throughout their lives. – among much else, the book teaches how to assess whether an observed relationship in data reflects a genuine relationship in the world and, if so, whether it is causal; how to make the most informative comparisons for answering questions; what questions to ask others who are making arguments using quantitative evidence; which statistics are particularly informative or misleading; how quantitative evidence should and shouldn’t influence decision-making; and how to make better decisions by using moral values as well as data. filled with real-world examples, the book shows how its thinking tools apply to problems in a wide variety of subjects, including elections, civil conflict, crime, terrorism, financial crises, health care, sports, music, and space travel. – above all else, thinking clearly with data demonstrates why, despite the many benefits of our data-driven age, data can never be a substitute for thinking. – an ideal textbook for introductory quantitative methods courses in data science, statistics, political science, economics, psychology, sociology, public policy, and other fields – introduces the basic toolkit of data analysis—including sampling, hypothesis testing, bayesian inference, regression, experiments, instrumental variables, differences in differences, and regression discontinuity – uses real-world examples and data from a wide variety of subjects – includes practice questions and data exercises

Do you want to expand your skills from being a basic data scientist to becoming an expert data scientist ready to solve real-world data centric issues? exploring this book could be a step in the right direction… 2 comprehensive manuscripts in 1 book data science: what the best data scientists know about data analytics, data mining, statistics, machine learning, and big data – that you don't data science for business: predictive modeling, data mining, data analytics, data warehousing, data visualization, regression analysis, database querying, and machine learning for beginners part one of this book will cover topics such as: what data science is what it takes to become an expert in data science best data mining techniques to apply in data data visualization logistic regression data engineering machine learning big data analytics and much more part 2 of this book will discuss the following topics: how big data works and why it is so important how to do an explorative data analysis working with data mining how to mine text to get the data some amazing machine learning algorithms to help with data science how to do data modeling data visualization how to use data science to help your business grow tips to help you get started with data science and much, much more so if you want to learn more about data science, click "buy now"

Data analysis using regression and multilevel/hierarchical models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. it introduces and demonstrates a variety of models and instructs the reader in how to fit these models using freely available software packages. data analysis using regression and multilevel/hierarchical models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. the book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. the book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. practical tips regarding building, fitting, and understanding are provided throughout.

These days the business world is full of talk about data science, big data, and how data analysis can transform your business. – and it's absolutely true that collecting the right information, in the right way, analyzing that information, and then using it effectively to manage your business can give your business a competitive edge. – but most businesses don't need to go so far as big data and data science. they just need to understand and implement some basic steps for gathering the right information about their business and using it effectively. – leveraging over twenty years of experience using data for business, m.l. humphrey will walk you through what you need to know to help improve your bottom line today. – so don't wait. get started now. – this title was previously published as data principles for beginners.

The second path analysis book is the improvement of the first edition book with the additional content is how to calculate the indirect effect and to conduct the hypothesis testing. the second edition book includes:1path analysis definitions and basic concepts2assumptions and basic principles3requirements and stages in using path analysis4path analysis models5an application of a multiple regression model6an application of a mediation model7an application of a joint multiple regression and mediation model8an application of a complex model9path analysis using panel data10an application in thesis research11indirect effect calculation

Key benefit: for over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis. hair, et. al provides an applications-oriented introduction to multivariate analysis for the non-statistician. by reducing heavy statistical research into fundamental concepts, the text explains to students how to understand and make use of the results of specific statistical techniques. in this seventh revision, the organization of the chapters has been greatly simplified. new chapters have been added on structural equations modeling, and all sections have been updated to reflect advances in technology, capability, and mathematical techniques. preparing for a mv analysis; dependence techniques; interdependence techniques; moving beyond the basic techniques market: statistics and statistical research can provide managers with invaluable data. this textbook teaches them the different kinds of analysis that can be done and how to apply the techniques in the workplace.

Key benefit: for over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis. hair, et. al provides an applications-oriented introduction to multivariate analysis for the non-statistician. by reducing heavy statistical research into fundamental concepts, the text explains to students how to understand and make use of the results of specific statistical techniques. in this seventh revision, the organization of the chapters has been greatly simplified. new chapters have been added on structural equations modeling, and all sections have been updated to reflect advances in technology, capability, and mathematical techniques. preparing for a mv analysis; dependence techniques; interdependence techniques; moving beyond the basic techniques market: statistics and statistical research can provide managers with invaluable data. this textbook teaches them the different kinds of analysis that can be done and how to apply the techniques in the workplace.

Data analysis with competing risks and intermediate states explains when and how to use models and techniques for the analysis of competing risks and intermediate states. it covers the most recent insights on estimation techniques and discusses in detail how to interpret the obtained results.after introducing example studies from the biomedical and epidemiological fields, the book formally defines the concepts that play a role in analyses with competing risks and intermediate states. it addresses nonparametric estimation of the relevant quantities. the book then shows how to use a stacked data set that offers great flexibility in the modeling of covariable effects on the transition rates between states. it also describes three ways to quantify effects on the cumulative scale.each chapter includes standard exercises that reflect on the concepts presented, a section on software that explains options in sas and stata and the functionality in the r program, and computer practicals that allow readers to practice with the techniques using an existing data set of bone marrow transplant patients. the book's website provides the r code for the computer practicals along with other material.for researchers with some experience in the analysis of standard time-to-event data, this practical and thorough treatment extends their knowledge and skills to the competing risks and multi-state settings. researchers from other fields can also easily translate individuals and diseases to units and phenomena from their own areas.

Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in python. updated for python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. you'll learn the latest versions of pandas, numpy, and jupyter in the process. written by wes mckinney, the creator of the python pandas project, this book is a practical, modern introduction to data science tools in python. it's ideal for analysts new to python and for python programmers new to data science and scientific computing. data files and related material are available on github. use the jupyter notebook and ipython shell for exploratory computing learn basic and advanced features in numpy get started with data analysis tools in the pandas library use flexible tools to load, clean, transform, merge, and reshape data create informative visualizations with matplotlib apply the pandas groupby facility to slice, dice, and summarize datasets analyze and manipulate regular and irregular time series data learn how to solve real-world data analysis problems with thorough, detailed examples