20 Best Books For Data Science (2023 Update)

Learn how to use r to turn raw data into insight, knowledge, and understanding. this book introduces you to r, rstudio, and the tidyverse, a collection of r packages designed to work together to make data science fast, fluent, and fun. suitable for readers with no previous programming experience, r for data science is designed to get you doing data science as quickly as possible. – authors hadley wickham and garrett grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. you'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. each section of the book is paired with exercises to help you practice what you've learned along the way. – you'll learn how to: wrangle–transform your datasets into a form convenient for analysis – program–learn powerful r tools for solving data problems with greater clarity and ease – explore–examine your data, generate hypotheses, and quickly test them – model–provide a low-dimensional summary that captures true "signals" in your dataset – communicate–learn r markdown for integrating prose, code, and results

Summary practical data science with r, second edition takes a practice-oriented approach to explaining basic principles in the ever expanding field of data science. you’ll jump right to real-world use cases as you apply the r programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. purchase of the print book includes a free ebook in pdf, kindle, and epub formats from manning publications. about the technology evidence-based decisions are crucial to success. applying the right data analysis techniques to your carefully curated business data helps you make accurate predictions, identify trends, and spot trouble in advance. the r data analysis platform provides the tools you need to tackle day-to-day data analysis and machine learning tasks efficiently and effectively. about the book practical data science with r, second edition is a task-based tutorial that leads readers through dozens of useful, data analysis practices using the r language. by concentrating on the most important tasks you’ll face on the job, this friendly guide is comfortable both for business analysts and data scientists. because data is only useful if it can be understood, you’ll also find fantastic tips for organizing and presenting data in tables, as well as snappy visualizations. what's inside statistical analysis for business pros effective data presentation the most useful r tools interpreting complicated predictive models about the reader you’ll need to be comfortable with basic statistics and have an introductory knowledge of r or another high-level programming language. about the author nina zumel and john mount founded a san francisco–based data science consulting firm. both hold phds from carnegie mellon university and blog on statistics, probability, and computer science.

Introduction to data science: data analysis and prediction algorithms with r introduces concepts and skills that can help you tackle real-world data analysis challenges. it covers concepts from probability, statistical inference, linear regression, and machine learning. it also helps you develop skills such as r programming, data wrangling, data visualization, predictive algorithm building, file organization with unix/linux shell, version control with git and github, and reproducible document preparation. this book is a textbook for a first course in data science. no previous knowledge of r is necessary, although some experience with programming may be helpful. the book is divided into six parts: r, data visualization, statistics with r, data wrangling, machine learning, and productivity tools. each part has several chapters meant to be presented as one lecture.the author uses motivating case studies that realistically mimic a data scientist's experience. he starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. examples of the case studies included are: us murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. the statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. if you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.a complete solutions manual is available to registered instructors who require the text for a course.

For many researchers, python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. several resources exist for individual pieces of this data science stack, but only with the python data science handbook do you get them all–ipython, num – py, pandas, matplotlib, scikit-learn, and other related tools. – working scientists and data crunchers familiar with reading and writing python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. quite simply, this is the must-have reference for scientific computing in python. – with this handbook, you'll learn how to use: ipython and jupyter: provide computational environments for data scientists using pythonnumpy: includes the ndarray for efficient storage and manipulation of dense data arrays in python – pandas: features the data – frame for efficient storage and manipulation of labeled/columnar data in python – matplotlib: includes capabilities for a flexible range of data visualizations in python – scikit-learn: for efficient and clean python implementations of the most important and established machine learning algorithms

A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.the goal of data science is to improve decision making through the analysis of data. this volume in the mit press essential knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges.it has never been easier for organizations to gather, store, and process data. use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. it is closely related to the fields of data mining and machine learning, but broader in scope. this book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. it considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. the book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. finally, it considers the future impact of data science and offers principles for success in data science projects.

Written by renowned data science experts foster provost and tom fawcett, data science for business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. this guide also helps you understand the many data-mining techniques in use today. – based on an mba course provost has taught at new york university over the past ten years, data science for business provides examples of real-world business problems to illustrate these principles. you'll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company's data science projects. you'll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. – understand how data science fits in your organization—and how you can use it for competitive advantage – treat data as a business asset that requires careful investment if you're to gain real value – approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way – learn general concepts for actually extracting knowledge from data – apply data science principles when interviewing data science job candidates

Written by renowned data science experts foster provost and tom fawcett, data science for business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. this guide also helps you understand the many data-mining techniques in use today. – based on an mba course provost has taught at new york university over the past ten years, data science for business provides examples of real-world business problems to illustrate these principles. you'll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company's data science projects. you'll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. – understand how data science fits in your organization—and how you can use it for competitive advantage – treat data as a business asset that requires careful investment if you're to gain real value – approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way – learn general concepts for actually extracting knowledge from data – apply data science principles when interviewing data science job candidates

This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. it focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. the data science design manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. the book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. this easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “introduction to data science” course. it reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. practitioners in these and related fields will find this book perfect for self-study as well. additional learning tools: contains “war stories,” offering perspectives on how data science applies in the real world – includes “homework problems,” providing a wide range of exercises and projects for self-study – provides “take-home lessons,” emphasizing the big-picture concepts to learn from each chapter – recommends exciting “kaggle challenges” from the online platform kaggle – highlights “false starts,” revealing the subtle reasons why certain approaches fail

For courses in introductory statistics. the art and science of learning from data statistics: the art and science of learning from data, fourth edition, takes a conceptual approach, helping students understand what statistics is about and learning the right questions to ask when analyzing data, rather than just memorizing procedures. this book takes the ideas that have turned statistics into a central science in modern life and makes them accessible, without compromising the necessary rigor. students will enjoy reading this book, and will stay engaged with its wide variety of real-world data in the examples and exercises. the authors believe that it's important for students to learn and analyze both quantitative and categorical data. as a result, the text pays greater attention to the analysis of proportions than many other introductory statistics texts. concepts are introduced first with categorical data, and then with quantitative data. also available with mystatlab mystatlab is an online homework, tutorial, and assessment program designed to work with this text to engage students and improve results. within its structured environment, students practice what they learn, test their understanding, and pursue a personalized study plan that helps them absorb course material and understand difficult concepts. for this edition, new web apps with complementary exercises, a tightly integrated video program, and strong exercise coverage enhance student learning. note: you are purchasing a standalone product; mylab & mastering does not come packaged with this content. students, if interested in purchasing this title with mylab & mastering, ask your instructor for the correct package isbn and course id.

Used in stanford's cs102 big data (spring 2017) course. want to get started on data science? this book has been written in layman's terms as a gentle introduction to data science and its algorithms. each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application.

Simplify data science infrastructure to give data scientists an efficient path from prototype to production.in effective data science infrastructure you will learn how to: design data science infrastructure that boosts productivity handle compute and orchestration in the cloud deploy machine learning to production monitor and manage performance and results combine cloud-based tools into a cohesive data science environment develop reproducible data science projects using metaflow, conda, and docker architect complex applications for multiple teams and large datasets customize and grow data science infrastructure effective data science infrastructure: how to make data scientists more productive is a hands-on guide to assembling infrastructure for data science and machine learning applications. it reveals the processes used at netflix and other data-driven companies to manage their cutting edge data infrastructure. in it, you’ll master scalable techniques for data storage, computation, experiment tracking, and orchestration that are relevant to companies of all shapes and sizes. you’ll learn how you can make data scientists more productive with your existing cloud infrastructure, a stack of open source software, and idiomatic python. the author is donating proceeds from this book to charities that support women and underrepresented groups in data science. purchase of the print book includes a free ebook in pdf, kindle, and epub formats from manning publications. about the technology growing data science projects from prototype to production requires reliable infrastructure. using the powerful new techniques and tooling in this book, you can stand up an infrastructure stack that will scale with any organization, from startups to the largest enterprises. about the book effective data science infrastructure teaches you to build data pipelines and project workflows that will supercharge data scientists and their projects. based on state-of-the-art tools and concepts that power data operations of netflix, this book introduces a customizable cloud-based approach to model development and mlops that you can easily adapt to your company’s specific needs. as you roll out these practical processes, your teams will produce better and faster results when applying data science and machine learning to a wide array of business problems. what's inside handle compute and orchestration in the cloud combine cloud-based tools into a cohesive data science environment develop reproducible data science projects using metaflow, aws, and the python data ecosystem architect complex applications that require large datasets and models, and a team of data scientists about the reader for infrastructure engineers and engineering-minded data scientists who are familiar with python. about the author at netflix, ville tuulos designed and built metaflow, a full-stack framework for data science. currently, he is the ceo of a startup focusing on data science infrastructure. table of contents 1 introducing data science infrastructure 2 the toolchain of data science 3 introducing metaflow 4 scaling with the compute layer 5 practicing scalability and performance 6 going to production 7 processing data 8 using and operating models 9 machine learning with the full stack

"turn yourself into a data head. you'll become a more valuable employee and make your organization more successful." thomas h. davenport, research fellow, author of competing on analytics, big data @ work, and the ai advantage you've heard the hype around data—now get the facts. in becoming a data head: how to think, speak, and understand data science, statistics, and machine learning, award-winning data scientists alex gutman and jordan goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it. you'll learn how to: think statistically and understand the role variation plays in your life and decision making speak intelligently and ask the right questions about the statistics and results you encounter in the workplace understand what's really going on with machine learning, text analytics, deep learning, and artificial intelligence avoid common pitfalls when working with and interpreting data becoming a data head is a complete guide for data science in the workplace: covering everything from the personalities you’ll work with to the math behind the algorithms. the authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. anyone can become a data head—an active participant in data science, statistics, and machine learning. whether you're a business professional, engineer, executive, or aspiring data scientist, this book is for you.

This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. the foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. using popular data science tools such as python and r, the book offers many examples of real-life applications, with practice ranging from small to big data. a suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. this entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. but how can you get started working in a wide-ranging, interdisciplinary field that's so clouded in hype? this insightful book, based on columbia university's introduction to data science class, tells you what you need to know. if you're familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. topics include: statistical inference, exploratory data analysis, and the data science process algorithms spam filters, naive bayes, and data wrangling logistic regression financial modeling recommendation engines and causality data visualization social networks and data journalism data engineering, mapreduce, pregel, and hadoop doing data science is collaboration between course instructor rachel schutt, senior vp of data science at news corp, and data science consultant cathy o'neil, a senior data scientist at johnson research labs, who attended and blogged about the course.

The fast and easy way to learn python programming and statistics python is a general-purpose programming language created in the late 1980s—and named after monty python—that's used by thousands of people to do things from testing microchips at intel, to powering instagram, to building video games with the pygame library. python for data science for dummies is written for people who are new to data analysis, and discusses the basics of python data analysis programming and statistics. the book also discusses google colab, which makes it possible to write python code in the cloud. get started with data science and python visualize information wrangle data learn from data the book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.

Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets.human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. this book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. it offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. the authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by making the process more transparent, asking questions, and considering the social context of the data. they describe how tools from social science might be incorporated into data science practices, discuss different types of collaboration, and consider data storytelling through visualization. the book shows that data science practitioners can build rigorous and ethical algorithms and design projects that use cutting-edge computational tools and address social concerns.

Summary – practical data science with r lives up to its name. it explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. you'll apply the r programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. – purchase of the print book includes a free ebook in pdf, kindle, and e – pub formats from manning publications. – about the book – business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. the r language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics. – practical data science with r shows you how to apply the r programming language and useful statistical techniques to everyday business situations. using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as a/b tests), build predictive models, and present results to audiences of all levels. – this book is accessible to readers without a background in data science. some familiarity with basic statistics, r, or another scripting language is assumed. – what's inside – data science for the business professional – statistical analysis using the r language – numerous instantly familiar use cases – keys to effective data presentations – about the authors – nina zumel and john mount are cofounders of a san francisco-based data science consulting firm. both hold ph – table of contents – part 1 introduction to data sciencethe data science process – loading data into rexploring data – managing data – part 2 modeling methodschoosing and evaluating models – memorization methods – linear and logistic regression – unsupervised methods – exploring advanced methods – part 3 delivering resultsdocumentation and deployment – producing effective presentations

Summary – practical data science with r lives up to its name. it explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. you'll apply the r programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. – purchase of the print book includes a free ebook in pdf, kindle, and e – pub formats from manning publications. – about the book – business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. the r language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics. – practical data science with r shows you how to apply the r programming language and useful statistical techniques to everyday business situations. using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as a/b tests), build predictive models, and present results to audiences of all levels. – this book is accessible to readers without a background in data science. some familiarity with basic statistics, r, or another scripting language is assumed. – what's inside – data science for the business professional – statistical analysis using the r language – numerous instantly familiar use cases – keys to effective data presentations – about the authors – nina zumel and john mount are cofounders of a san francisco-based data science consulting firm. both hold ph – table of contents – part 1 introduction to data sciencethe data science process – loading data into rexploring data – managing data – part 2 modeling methodschoosing and evaluating models – memorization methods – linear and logistic regression – unsupervised methods – exploring advanced methods – part 3 delivering resultsdocumentation and deployment – producing effective presentations

Summary – think like a data scientist presents a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems. – purchase of the print book includes a free ebook in pdf, kindle, and e – pub formats from manning publications. – about the technology – t sensors, and so on is valuable only if you understand it. data scientists revel in the interesting and rewarding challenge of observing, exploring, analyzing, and interpreting this data. getting started with data science means more than mastering analytic tools and techniques, however; the real magic happens when you begin to think like a data scientist. this book will get you there. – about the book – think like a data scientist teaches you a step-by-step approach to solving real-world data-centric problems. by breaking down carefully crafted examples, you'll learn to combine analytic, programming, and business perspectives into a repeatable process for extracting real knowledge from data. as you read, you'll discover (or remember) valuable statistical techniques and explore powerful data science software. more importantly, you'll put this knowledge together using a structured process for data science. when you've finished, you'll have a strong foundation for a lifetime of data science learning and practice. – what's inside – how to anticipate problems – dealing with uncertainty – best practices in software and scientific thinking – about the reader – readers need beginner programming skills and knowledge of basic statistics. – about the author – brian godsey has worked in software, academia, finance, and defense and has launched several data-centric start-ups. – table of contents – part 1 – preparing and gathering data and knowledgephilosophies of data science – setting goals by asking good questions – data all around us: the virtual wilderness – data wrangling: from capture to domestication – data assessment: poking and prodding – part 2 – building a product with software and statisticsdeveloping a plan – statistics and modeling: concepts and foundations – software: statistics in action – supplementary software: bigger, faster, more efficient – plan execution: putting it all together – part 3 – finishing off the product and wrapping updelivering a product – wrapping up: putting the project away

Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. data science for dummies is the perfect starting point for it professionals and students who want a quick primer on all areas of the expansive data science space. with a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. if you want to pick-up the skills you need to begin a new career or initiate a new project, listening to this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. while this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. here's what to expect: ● provides a background in big data and data engineering before moving on to data science and how it's applied to generate value ● includes coverage of big data frameworks like hadoop, mapreduce, spark, mpp platforms, and nosql ● and more it's a big, big data world out there-let data science for dummies help you harness its power and gain a competitive edge for your organization.