Data science has become extremely important for every business in a data-driven ecosystem. Besides being one of the highest-paid and infamous fields in today’s market, data science will continue to grow beyond all future challenges. To remain relevant to the growing market data scientists need to educate themselves, and data science books provide one of the most holistic views to hold on to their data skills.
Here are the top 20 books for data scientists to read.
An Introduction to Statistical Learning: with Applications in R
Author: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
An Introduction to Statistical Learning offers an accessible summary of the field of statistical learning, an important toolkit for understanding the large and diverse data sets that have appeared in fields ranging from biology to finance to marketing to astrophysics over the past 20 years. The book discusses some of the most important techniques of modeling , analysis and related applications. Topics include linear regression, classification, resampling methods, tree-based approaches, supporting vector machines, clustering, and more. Color graphics and real-world examples illustrate presented methods. Since this textbook ‘s goal is to facilitate practitioners’ use of these statistical learning techniques in science , industry, and other fields, each chapter contains a tutorial on implementing the analyzes and methods presented in R, an extremely popular open source statistical software platform.
Data Science from Scratch: First Principles with Python
Author: Joel Grus
Data science libraries, frameworks, modules, and toolkits are great to do data science, but they are also a good way to dive into the discipline without understanding data science in fact. You will learn how many of the most basic data science tools and algorithms work by implementing them from scratch.
If you have an aptitude for mathematics and some programming skills, author Joel Grus can help you get familiar with data science math and statistics and with hacking skills you need to start as a data scientist. Today ‘s chaotic abundance of data holds answers to questions no one even thinks to ask. This book offers the know-how to find those responses.
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
Author: Hadley Wickham, Garrett Grolemund
Learn how to use R to render raw data analysis, information, and understanding. This book introduces you to R, RStudio, and the tidyverse, a series of R packages to work together to make data science quick, smooth, and enjoyable. For readers without prior programming experience, R for Data Science is designed to get you to do data science as quickly as possible.
Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, modeling and communicating your data. You will get a complete, comprehensive picture understanding of the data science cycle along with basic tools to manage the details. Each book section is paired with exercises to help you practice what you’ve learned along the way.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Author: Robert Tibshirani, Jerome Friedman
The last decade has seen an explosion in computing and information technology. For it came huge quantities of data in a number of fields including medicine, genetics, economics, and marketing. The challenge of understanding these data led to the development of new computational methods, spawning new fields such as data mining , machine learning, and bioinformatics. Many of these tools have common underpinnings, but often with different terminology. This book presents essential ideas in a specific conceptual context. While the approach is statistical, it emphasizes concept rather than mathematics. Many examples are given, using color graphics liberally. A valuable resource for statisticians and anyone interested in science or industry data mining. Coverage from supervised learning (prediction) to unsupervised learning is broad. The other topics include neural networks, supporting vector machines, ranking trees, and boosting — the first systematic treatment of this subject in any book.
This major new edition features many non-original topics, including graphic models, random forests, ensemble methods, lasso less angle regression & path algorithms, non-negative matrix factorization, and spectral clustering. There’s also a chapter on “big” data methods (p larger than n), including multiple tests and fake discovery levels.
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
Author: Provost, Tom Fawcett
Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the basic principles of data science and takes you through the “data-analytic thinking” needed to extract useful knowledge and business value from the data you collect. This guide also helps you appreciate today’s many data-mining techniques.
Data Science for Business, based on an MBA course Provost has taught at New York University over the past ten years, provides examples of real-world business issues to illustrate these principles. Not only can you learn how to enhance collaboration between business stakeholders and data scientists, but also how to participate smartly in data science projects in your organization. You can also find out how to approach data-analytically and how data analysis approaches can help business decision-making.
Doing Data Science: Straight Talk from the Frontline
Author: Cathy O’Neil, Rachel Schutt
Now that people are conscious that in an election or business model , data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary, hype-clouded field? This informative novel, based on Introduction to Data Science class at Columbia University, tells you what to learn.
Data scientists from companies including Google, Microsoft, and eBay share new algorithms, techniques, and models in each of these chapter-long lectures by sharing case studies and their code. If you know linear algebra, probability, and statistics and have programming experience, this book is an excellent introduction to data science.
Storytelling with Data: A Data Visualization Guide for Business Professionals
Storytelling with data teaches you data visualization fundamentals and how to communicate effectively with data. You will discover storytelling power and the way to make data a pivotal point in your story. The lessons in this illuminative text are theoretically oriented, but made accessible by multiple real-world examples — ready for immediate application to the next graph or presentation.
Storytelling is not an inherent ability, especially when it comes to data visualization, and the tools at our disposal make it easier. This book shows how to go beyond traditional methods to get your data root and how to use your data to create an entertaining, insightful, convincing tale.
Together, this book’s lessons will help turn your data into high-impact visual stories that stick with your audience. Rid the world of useless maps, one pie chart bursting at a time. Your data contains a story — Storytelling with Data gives you the skills and power to tell!
Python Data Science Handbook: Essential Tools for Working with Data
For many researchers, Python is a first-class tool mainly due to its data storage , manipulation and insight libraries. Several resources exist for individual parts of this data science stack, but all you get is the Python Data Science Handbook — IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other similar tools.
Working scientists and data crunchers familiar with reading and writing Python code may find this thorough desk guide perfect for dealing with everyday issues: processing, transforming and cleaning data; visualizing different data types; and using data to create statistical or machine learning models. Quite simply, Python’s must-have reference for scientific computing.
The Signal and the Noise: The Art and Science of Prediction
Author: Silver Nate
Every time we choose a route to work, decide to go on a second date, or set aside money for a rainy day, we predict the future. And from the financial crisis to natural disasters, we regularly struggle to predict significant events, even at considerable risk to society. The rise of ‘big data’ can help us predict the future, yet much is misleading, useless or distracting.
The New York Times political forecaster Nate Silver, who correctly predicted the outcome of each state in the 2012 US election, explains how we can all build greater foresight in an unpredictable world. From stock market to poker table, from earthquakes to economy, he takes us on a exciting insider ‘s tour of the high-stakes forecasting universe, demonstrating how we can all learn to identify the true signals in the midst of data noise.
Author: Allen Downey
If you know how to program, use the resources of probability and statistics to turn data into information. This succinct introduction shows you how to do statistical analysis with Python-written programs computationally rather than mathematically.
Throughout the book, you can work with a case study to help you understand the entire data analysis process — from data collection, statistics creation, pattern recognition, and testing hypotheses. Along the way, you’ll become familiar with distributions, probability laws, visualization, and many other methods and concepts.
Author: Ian Goodfellow, YoshuaBengio, Aaron Courville
Deep learning is a type of machine learning , allowing computers to learn from experience and understand the world in terms of concept hierarchy. Because the computer gathers knowledge from experience, a human computer operator does not need to formally specify all the knowledge the computer needs. Concept hierarchy allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a wide range of deep-learning topics.
The text provides mathematical and conceptual history in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by industry practitioners, including deep feedforward networks, regularization, optimization algorithms, convolutionary networks, sequence modeling, and practical methodology; and examines applications such as natural language processing , speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book presents research insights such as linear factor models, autoencoders, representation learning, organized probabilistic models, Monte Carlo methods, partition function, indirect inference, and deep generative models.
Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit
Author: Steven Bird, Ewan Klein, Edward Loper
This book offers a highly accessible introduction to natural language processing, the field supporting a variety of language technologies, from predictive text filtering and email filtering to automatic summary and translation. You’ll learn how to write Python programs with large collections of unstructured text. Using a wide selection of linguistic data structures, you can access richly annotated datasets, and understand the key algorithms for analyzing written communication content and structure.
Using the Python programming language and the open source library, this book will help you gain practical skills in natural language processing. If you’re interested in creating web apps, reviewing multilingual news sources, or recording endangered languages — or if you’re just curious to get a programmer ‘s perspective on how human language works — you’ll consider Python’s Natural Language Processing both interesting and incredibly useful.
Data Smart: Using Data Science to Transform Information into Insight
Author: John W. Foreman
Data Science is tossed like sorcery in the papers. Major retailers predict everything from pregnant customers to new pairs of Chuck Taylors. It’s a brave new world that can transform seemingly meaningless data into valuable insight to drive smart business decisions.
But how exactly do data science? Must you hire one of these dark arts priests, the “data scientist,” to extract this gold from your results? Nope. Nope.
Data science is nothing more than basic measures to turn raw data into actionable knowledge. And in Data Smart, author and data scientist John Foreman will show you how it’s done in the familiar spreadsheet world.
Why a leaflet? It’s amazing! You get to look at data every step of the way, building confidence as you learn trade tricks. Plus, spreadsheets are a vendor-neutral place to learn without the hype.
But don’t be fooled by Excel papers. This is a book behind big data for those serious about learning analytical techniques, math and magic.
You get your hands dirty as each strategy works with John. But never fear, the topics are readily applicable, and the author laces humor. You’ll even learn what a dead squirrel has to do with modeling optimization, which you’re dying to know.
R for Everyone: Advanced Analytics and Graphics
Author: Jared P. Lander
Using the language open source R, you can create powerful statistical models to address many of your challenging questions. Traditionally, R has been difficult for non-statisticians to learn, and most R books assume too much knowledge to help. Everyone’s the answer.
Based on his unrivaled experience teaching new users, professional data scientist Jared P. Lander wrote the perfect tutorial for anyone new to statistical programming and modeling. Designed to make learning simple and intuitive, this guide focuses on the 20% of R features you need to perform 80% of modern data tasks.
Lander’s self-contained chapters begin with absolute basics, providing comprehensive practice and sample code. You can download and install R; access and use the R environment; master basic program management , data import and manipulation; and go through several key tests. Building on this basis, you can then create many full models, linear and nonlinear, using some data mining techniques.
The Hundred-Page Machine Learning Book
As the title says, the hundred-page machine learning book. It was written by a machine learning expert holding a Ph.D. in Artificial Intelligence with nearly two decades of computer science and hands-on machine learning experience.
This book is unique in many ways. It’s the first successful attempt to write an easy-to – read machine learning book that doesn’t fear using math. It’s also the first attempt to systematically squeeze a wide variety of machine learning topics without loss of efficiency.
The book contains only those parts of the vast body of material on machine learning developed since the 1960s that have demonstrated significant practical value. A computer learner should consider enough information in this book to get a comfortable level of field understanding and start asking the right questions. Experienced professionals should use this book as a set of recommendations for more self-improvement.
Data Science For Dummies
Author: Lillian Pierson
Computer science positions exist, but few people have the data science expertise to fill these increasingly critical roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick introduction to all areas of expansive data science. Focusing on business cases, the book explores topics in big data , data science, and data engineering, and how these three areas are combined to yield tremendous value. To pick up the skills you need to start a new career or start a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods to focus on.
Although this book is a wonderfully fantastic guide through the large, often daunting area of big data and data science, it’s not a hands-on implementation instruction manual.
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
Author: Pedro Domingos
Society is changing — one learning algorithm at a time — from search engines to online dating, personalized health to stock market analysis. Yet learning algorithms are not only about big data – they take raw data and make it useful by building more algorithms. Something new under the sun: a technology that builds itself. In The Master Algorithm, Pedro Domingos reveals how machine learning transforms business, politics , science and war. And he takes us on an amazing quest to find ‘The Master Algorithm,’ a universal learner able to derive all knowledge from data.
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
Author: Peter Bruce, Andrew Bruce, Peter Gedeck
Statistical methods are key components of data science, but few data scientists have formal statistical training. Basic statistics courses and books seldom address the subject from a data-science perspective. The second edition of this popular guide introduces detailed examples in Python, offers practical guidance on applying statistical methods to data science, tells you how to prevent misuse, and provides advice on what’s important and what’s not important.
Many data science tools include statistical approaches, but lack a deeper statistical view. If you know the R or Python programming languages and are exposed to statistics, this fast guide bridges the gap in an open, readable format.
Naked Statistics: Stripping the Dread from the Data
Author: Charles Wheelan
Once considered tedious, the field of statistics is rapidly evolving into a discipline called “sexy” by Google’s chief economist Hal Varian. From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. How can we catch schools cheating standardized tests? How does Netflix know you’ll like movies? What causes autism ‘s rising incidence? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and some selected statistical tools can help us answer these and more questions.
For those who slept Stats 101, this book is a lifesaver. Wheelan strips arcane and technical details and focuses on the underlying intuition driving statistical analysis. He clarifies key concepts such as inference, correlation , and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows how brilliant and creative researchers use valuable data from natural experiments to address thorny issues.
The Data Science Handbook
Author: Field Cady
Finding a good data scientist was like hunting for a unicorn: the combination of technical skills required is simply very hard to find in one person. Furthermore, good data science is not just rotary application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand their connections. This book offers a data science crash course, incorporating the required skills into a single discipline.
Unlike many analytics books, computer science and software engineering have extensive coverage, as they play such a central role in a data scientist’s daily work. The author also describes classic machine learning algorithms, from mathematical to real-world applications. Visualization tools are reviewed, highlighting their central importance in data science. Classical statistics are presented to help readers think critically about data interpretation and raising pitfalls. Clear communication of technical findings, which may be the most under-trained data science skills, is given its own chapter, and all topics are discussed in the sense of solving real-world data issues.
Practical Data Science with R
Author: Nina Zumel, John Mount
This indispensable addition to the library of any data scientist teaches you how to apply R programming language and practical statistical methods to daily business scenarios and how to easily deliver findings to audiences at all ages. To meet growing demand for machine learning and analysis, this new edition features additional R tools, modeling techniques, and more.
In the ever-expanding area of data science, Practical Data Science with R, Second Edition takes a practice-oriented approach to explaining basic concepts. You’ll leap straight to real-world use cases as you apply R programming language and statistical analysis techniques to carefully illustrate marketing-based examples, business intelligence, and decision support.
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