Data Science Books

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

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 to 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
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

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
Data Science from Scratch: First Principles with Python
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental 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 will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.

Get a crash course in Python
Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science
Collect, explore, clean, munge, and manipulate data
Dive into the fundamentals of machine learning
Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
Data Smart: Using Data Science to Transform Information into Insight

Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.

But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.

Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet.
Practical Statistics for Data Scientists: 50 Essential Concepts

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
Naked Statistics: Stripping the Dread from the Data

“Brilliant, funny . . . the best math teacher you never had.”―San Francisco Chronicle

Once considered tedious, the field of statistics is rapidly evolving into a discipline Hal Varian, chief economist at Google, has actually called “sexy.” 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 that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.
For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives 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 us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.

And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal―and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.
Numsense! Data Science for the Layman: No Math Added

Used in Stanford's CS102 Big Data (Spring 2017) course.

Want to get started on data science?
Our promise: no math added.

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. To help you grasp key concepts, we stick to intuitive explanations, as well as lots of visuals, all of which are colorblind-friendly.

Popular concepts covered include:

A/B Testing
Anomaly Detection
Association Rules
Decision Trees and Random Forests
Regression Analysis
Social Network Analysis
Neural Networks

Intuitive explanations and visuals
Real-world applications to illustrate each algorithm
Point summaries at the end of each chapter
Reference sheets comparing the pros and cons of algorithms
Glossary list of commonly-used terms
With this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions.
What Is Data Science?

We've all heard it: according to Hal Varian, statistics is the next sexy job. Five years ago, in What is Web 2.0, Tim O'Reilly said that "data is the next Intel Inside." But what does that statement mean? Why do we suddenly care about statistics and about data? This report examines the many sides of data science -- the technologies, the companies and the unique skill sets.The web is full of "data-driven apps." Almost any e-commerce application is a data-driven application. There's a database behind a web front end, and middleware that talks to a number of other databases and data services (credit card processing companies, banks, and so on). But merely using data isn't really what we mean by "data science." A data application acquires its value from the data itself, and creates more data as a result. It's not just an application with data; it's a data product. Data science enables the creation of data products.
Storytelling with Data: A Data Visualization Guide for Business Professionals

Don't simply show your data—tell a story with it!
Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation.

Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to:

Understand the importance of context and audience
Determine the appropriate type of graph for your situation
Recognize and eliminate the clutter clouding your information
Direct your audience's attention to the most important parts of your data
Think like a designer and utilize concepts of design in data visualization
Leverage the power of storytelling to help your message resonate with your audience
Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it!
Python Data Science Handbook: Essential Tools for Working with Data

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, NumPy, 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 Python
NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python
Pandas: features the DataFrame 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
Machine Intelligence: The Death of Artificial Intelligence

We sit at the threshold of the next generation of artificial intelligence—the development of true machine intelligence. Today, the best of A.I. has given us virtual assistants like Apple’s Siri and big data question/answering systems like IBM Watson. These statistical systems—based on Natural Language Processing—have accomplished a great deal. But, these assistants don’t really understand and do what we ask of them. They understand simple questions but cannot respond to complex or even slightly ambiguous ideas. Imagine you say, “I dropped my book and walked out of the kitchen to the bedroom. Where's the book?" A three-year old can grasp the meaning but your assistant can only scratch their virtual head.

Brains aren’t what you think they are. They aren’t computers and they don’t process data. Cognitive science tells us that the brain is more of a pattern-matching machine than a processing machine. Understanding meaning—Natural Language Understanding—can’t be achieved through statistical processing. NLU relies on a richer environment that looks at patterns in linguistics, as well as sensory perceptions. Machine Intelligence, first published in 1998, takes the reader through the research that lead to Patom Theory, a brain-based theory based solely on a brain that stores, matches, and uses patterns.

Ball, a cognitive scientist, began exploring the gap between how our brains interpret information and how computers work in 1983. Research, development collaborations and idea exchanges with the likes of A.I. co-founder and Turing Award winner Marvin Minsky became the foundation of Patom Theory. The theory has laid the groundwork work for NLU software developments that may lead to truly intelligent machines.
Sentiment Analysis: Mining Opinions, Sentiments, and Emotions

Sentiment analysis is the computational study of people's opinions, sentiments, emotions, and attitudes. This fascinating problem is increasingly important in business and society. It offers numerous research challenges but promises insight useful to anyone interested in opinion analysis and social media analysis. This book gives a comprehensive introduction to the topic from a primarily natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs that are commonly used to express opinions and sentiments. It covers all core areas of sentiment analysis, includes many emerging themes, such as debate analysis, intention mining, and fake-opinion detection, and presents computational methods to analyze and summarize opinions. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences.
Statistics: Learning from Data

STATISTICS: LEARNING FROM DATA, by respected and successful author Roxy Peck, resolves common problems faced by learners of elementary statistics with an innovative approach. Peck tackles the areas learners struggle with most--probability, hypothesis testing, and selecting an appropriate method of analysis--unlike any book on the market. Probability coverage is based on current research that shows how users best learn the subject. Two unique chapters, one on statistical inference and another on learning from experiment data, address two common areas of confusion: choosing a particular inference method and using inference methods with experimental data. Supported by learning objectives, real-data examples and exercises, and technology notes, this brand new book guides readers in gaining conceptual understanding, mechanical proficiency, and the ability to put knowledge into practice.
Data Analytics Made Accessible: 2017 edition

This book fills the need for a concise and conversational book on the growing field of Data Science. Easy to read and informative, this lucid book covers everything important, with concrete examples, and invites the reader to join this field. The chapters in the book are organized for a typical one-semester course. The book contains case-lets from real-world stories at the beginning of every chapter. There is also a running case study across the chapters as exercises. This book is designed to provide a student with the intuition behind this evolving area, along with a solid toolset of the major data mining techniques and platforms. Finally, it includes a tutorial for R platform.
The book has proved very popular throughout the world. Many universities in the US, and around the world, have adopted it as a textbook for their courses. This 2017 edition has added four new chapters in response to the thoughts and suggestions expressed by many reviewers.
Students across a variety of academic disciplines, including business, computer science, statistics, engineering, and others attracted to the idea of discovering new insights and ideas from data can use this as a textbook. Professionals in various domains, including executives, managers, analysts, professors, doctors, accountants, and others can use this book to learn in a few hours how to make sense of and develop actionable insights from the enormous data coming their way. This is a flowing book that one can finish in one sitting, or one can return to it again and again for insights and techniques.
The Data Science Handbook

A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline

Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline.

Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features:

• Extensive sample code and tutorials using Python™ along with its technical libraries

• Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems

• Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity

• A wide variety of case studies from industry

• Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed

The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set.

FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon.
Data Analytics: Practical Guide to Leveraging the Power of Algorithms, Data Science, Data Mining, Statistics, Big Data, and Predictive Analysis to Improve Business, Work, and Life

The Ultimate Guide to Data Science and Analytics
This practical guide is accessible for the reader who is relatively new to the field of data analytics, while still remaining robust and detailed enough to function as a helpful guide to those already experienced in the field. Data science is expanding in breadth and growing rapidly in importance as technology rapidly integrates ever deeper into business and our daily lives. The need for a succinct and informal guide to this important field has never been greater.
RIGHT NOW you can get ahead of the pack!
This coherent guide covers everything you need to know on the subject of data science, with numerous concrete examples, and invites the reader to dive further into this exciting field. Students from a variety of academic backgrounds, including computer science, business, engineering, statistics, anyone interested in discovering new ideas and insights derived from data can use this as a textbook. At the same time, professionals such as managers, executives, professors, analysts, doctors, developers, computer scientists, accountants, and others can use this book to make a quantum leap in their knowledge of big data in a matter of only a few hours. Learn how to understand this field and uncover actionable insights from data through analytics.
Data Driven

Succeeding with data isn’t just a matter of putting Hadoop in your machine room, or hiring some physicists with crazy math skills. It requires you to develop a data culture that involves people throughout the organization. In this O’Reilly report, DJ Patil and Hilary Mason outline the steps you need to take if your company is to be truly data-driven—including the questions you should ask and the methods you should adopt.

You’ll not only learn examples of how Google, LinkedIn, and Facebook use their data, but also how Walmart, UPS, and other organizations took advantage of this resource long before the advent of Big Data. No matter how you approach it, building a data culture is the key to success in the 21st century.

You’ll explore:

Data scientist skills—and why every company needs a Spock
How the benefits of giving company-wide access to data outweigh the costs
Why data-driven organizations use the scientific method to explore and solve data problems
Key questions to help you develop a research-specific process for tackling important issues
What to consider when assembling your data team
Developing processes to keep your data team (and company) engaged
Choosing technologies that are powerful, support teamwork, and easy to use and learn.
Doing Data Science: Straight Talk from the Frontline

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.

In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. 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
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 Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists

The Data Science Handbook contains interviews with 25 of the world s best data scientists. We sat down with them, had in-depth conversations about their careers, personal stories, perspectives on data science and life advice. In The Data Science Handbook, you will find war stories from DJ Patil, US Chief Data Officer and one of the founders of the field. You ll learn industry veterans such as Kevin Novak and Riley Newman, who head the data science teams at Uber and Airbnb respectively. You ll also read about rising data scientists such as Clare Corthell, who crafted her own open source data science masters program. This book is perfect for aspiring or current data scientists to learn from the best. It s a reference book packed full of strategies, suggestions and recipes to launch and grow your own data science career.
Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data

Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software.
This book will help you:

Become a contributor on a data science team
Deploy a structured lifecycle approach to data analytics problems
Apply appropriate analytic techniques and tools to analyzing big data
Learn how to tell a compelling story with data to drive business action
Prepare for EMC Proven Professional Data Science Certification
Corresponding data sets are available at

Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!
Data Analytics: The Insider's Guide To Master Data Analytics (Business Intelligence, Data Science - Leverage and Integrate Data Analytics into your Business)

Analytics is a vital part of the business world we live in today. Without a detailed analysis of market conditions and other factors it would be impossible to tell if any new venture, whether it be a new business or the revamp of an old one, would be profitable.

Data Analytics: Insider’s Guide to Master Data Analytics will help you to better understand the complexities of data analytics. It will show you the benefits it can have for your business and how to make the best decisions.

The chapters include detailed information on;

The basics of analytics
Techniques for data analysis
Genetic algorithms
Regression analysis
Social network analysis
And much more…
The benefits of understanding data analysis will help your business to prosper and expand in the right directions, cutting down on risk and creating greater profitability.

The Insider’s Guide to Master Data Analytics is a book which is thorough and complete, delivering all the information you’ll ever need, in one handy book and providing you with real life examples of those businesses that got it right.

Get you copy today and see your business thrive for tomorrow.
Data Analytics: What Every Business Must Know About Big Data And Data Science

Are You Actively Analyzing the Data Surrounding Your Business? Keep Reading to Learn Why You Should Be..

You may be the owner of a business, or someone who actively participates in the day to day operations of a business. We will go ahead and assume that your business is operating at a profit and you are happy with the direction it is going. As someone in this situation you might ask yourself, "Why do I need Data Analysis anyways?". I'll tell you why, one simple reason. You are leaving money on the table. Let's put it this way.. you are doing good, but wouldn't you rather be doing great? Wouldn't you rather have the ability to predict how the consumers in your target market are going to be behaving a year from now? Five years from now? This is where Data Analysis comes in.

Many people realize the need to pay attention to data in their business, but have no clue where to start. With the help of this book you will be better able to understand the importance of the data surrounding your business and exactly what to do with it.

A Preview of What You Will Learn
The Importance of Data in Business
Exactly How to Handle and Manage Big Data
Real World Examples of Data Science Benefiting Businesses
Ways Data Can Be Used to Mitigate Risks
The Entire Process of Data Analytics
Much, much more!
Data Science with Java: Practical Methods for Scientists and Engineers

Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today’s data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java.

You’ll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you’ll find code examples you can use in your applications.

Examine methods for obtaining, cleaning, and arranging data into its purest form
Understand the matrix structure that your data should take
Learn basic concepts for testing the origin and validity of data
Transform your data into stable and usable numerical values
Understand supervised and unsupervised learning algorithms, and methods for evaluating their success
Get up and running with MapReduce, using customized components suitable for data science algorithms.
How To Start a Career in Data Science

Data Science is the job of the decade. Yet there are only a few colleges which have a course on data science. This book is all about how to start a career in data science. The book covers all the detail of the topics to cover, tools and technologies to learn, important concepts, interview questions, companies to apply. This is a complete guide which can help you start a career as the sexiest job 21st Century
Data Science at the Command Line: Facing the Future with Time-Tested Tools

This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data.

To get you started—whether you’re on Windows, OS X, or Linux—author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools.

Discover why the command line is an agile, scalable, and extensible technology. Even if you’re already comfortable processing data with, say, Python or R, you’ll greatly improve your data science workflow by also leveraging the power of the command line.

Obtain data from websites, APIs, databases, and spreadsheets
Perform scrub operations on plain text, CSV, HTML/XML, and JSON
Explore data, compute descriptive statistics, and create visualizations
Manage your data science workflow using Drake
Create reusable tools from one-liners and existing Python or R code
Parallelize and distribute data-intensive pipelines using GNU Parallel
Model data with dimensionality reduction, clustering, regression, and classification algorithms.
Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark

Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools.

Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization.

Build value from your data in a series of agile sprints, using the data-value pyramid
Extract features for statistical models from a single dataset
Visualize data with charts, and expose different aspects through interactive reports
Use historical data to predict the future via classification and regression
Translate predictions into actions
Get feedback from users after each sprint to keep your project on track
Markov Models: Understanding Data Science, Markov Models And Unsupervised Machine Learning In Python

Do you want to MASTER data science?

Learn how MACHINE LEARNING systems can carry out multifaceted processes by learning from data?

Understand MARKOV MODELS and how they can help your correctly forecast future events?

Want to explore practical implementations of Markov models in PYTHON PROGRAMMING environment?

Then you should DOWNLOAD your copy today

The aim of machine learning is to train the computers or machine to learn on its own and make informed decisions in a relatively shorter time than what human beings can do.

The primary objective of this book is to provide you with all the ins and outs of Markov models and unsupervised machine learning over a range of multi-faceted applications. Specifically, the book will explore practical implementations of Markov models in Python programming environment.

You'll discover: - Types of machine learning algorithms - The mathematics behind markov algorithms - Application of markov models in python programming - Application of markov models in - gaming - Speech recognition - Weather reporting and much much more!
Data Science Interviews Exposed

Data Science Interviews Exposed offers data science career advice and REAL interview questions to help you get the six-figures salary jobs! A data science job is extremely rewarding. It empowers to you make real impact in the world! And besides, it offers competitive salaries, and it develops your creative as well as quantitative skills. No wonder the data science job is rated as one of the sexist jobs in 21st century. So what you are waiting for ?
Are you still wondering how to join data science work force ?
Are you lost in the tremendous amount of online data science courses and resources ?
Are you endlessly searching online to find data science interview questions and answers?
If you answer yes for any of the questions, Data Science Interviews Exposed is a book you absolutely want to read. Why?
This book is written by data science professionals from Facebook, LinkedIn, Amazon, Google and Microsoft, with years of first hand working and interviewing experience.
This is the first book in the industry that systematically covers everything for preparing for a data science career and interviews, and with real interview questions and detailed answers.
This book provides both career guidance for entry level candidates as well as interview questions practice for intermediate candidates.

Here is a full list of topics:
This chapter presents an overview to the data science job market and the book organization.

Find the Right Job Roles
Get confused about the various data science job titles? This chapter provides a detailed description for each of them, the differences among them, as well as the guidance for choosing the one that suits you the most.

Find the Right Experience
Don't know how to prepare yourself with the right experience to meet the job requirements and your career goals? This chapter helps you to identify the experience you need to land your dream position. It also provides suggestions for new graduates as well as candidates from a different industry who want to transfer to data science field.

Get Ready for the Interviews
Think you have a clear goal and have possessed all the required skill sets, but just don't know how to get job interviews? This chapter walks you through how to build good resumes and professional profiles that would bring you the right exposure to the right person -- recruiters and hiring managers.

Polish Your Soft Skills
Heard of your competent peers failing job interviews and want to know why? This chapter reveals the secrets that most companies don t talk about publicly -- the soft skills. What are behavior questions, why are they important, how do you prepare for them? You will find the answer here.

Technical Interview Questions
An interview is not a pop quiz. You should take the time to practice on real interview problems and learn their patterns. This chapter lists eight major topics that are frequently covered by data science job interviews, associated with example interview questions for each of them. All of them are either real interview questions or adapted from real interview questions:
Probability Theory
Statistical Inference
Dataset Manipulation
Product, Metrics and Analytics
Experiment Design
Machine Learning
Brain Teasers
Solutions to Technical Interview Questions
This chapter attaches the solutions and thought process for each question in the previous chapter. We hope the readers can grasp the key points behind each of them, hence be able to apply the approaches to other similar questions in the real interviews.
Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs)

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Graphics in this book are printed in black and white.

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

Explore the machine learning landscape, particularly neural nets
Use scikit-learn to track an example machine-learning project end-to-end
Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
Use the TensorFlow library to build and train neural nets
Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
Learn techniques for training and scaling deep neural nets
Apply practical code examples without acquiring excessive machine learning theory or algorithm details
Data Science in Python. Volume 1: Get and Install Scientific Python3: WinPython, Anaconda

Python is the most popular programming language in scientific computing today. This series is for people who want to start using Python 3 and its popular extension libraries quickly. I assume you are familiar with Python. This short introductory volume 1 is intended to get you started with scientific Python distribution necessary to run examples from other volumes. It covers how to:
Obtain and install Winpython or Anaconda Python distribution.

Start a Jupyter (formerly IPython) notebook

Use IDLE and Spyder integrated development environments

Gives an overview of the topics covered in the following volumes

Volume 2 of this series, that describes how to read tabular data, save it as text or Microsoft Excel file, explore data interactively with Ipython notebook, create GUI application with TkInter, package your program for deployment on other computers, do efficient computation with Numpy, run Python at the speed of compiled program on all cores of your processor.

Volume 3 describes plotting library Matplotlib and using Python together with SQLite database.
Introduction to Machine Learning with Python: A Guide for Data Scientists

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.

You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.

With this book, you’ll learn:

Fundamental concepts and applications of machine learning
Advantages and shortcomings of widely used machine learning algorithms
How to represent data processed by machine learning, including which data aspects to focus on
Advanced methods for model evaluation and parameter tuning
The concept of pipelines for chaining models and encapsulating your workflow
Methods for working with text data, including text-specific processing techniques
Suggestions for improving your machine learning and data science skills
Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications (Undergraduate Topics in Computer Science)

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.

Practical Data Science with R


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 ePub 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
Project lifecycle, from planning to delivery
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 PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at

Table of Contents

The data science process
Loading data into R
Exploring data
Managing data
Choosing and evaluating models
Memorization methods
Linear and logistic regression
Unsupervised methods
Exploring advanced methods
Documentation and deployment
Producing effective presentations
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.

Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It’s ideal for analysts new to Python and for Python programmers new to scientific computing.

Use the IPython interactive shell as your primary development environment
Learn basic and advanced NumPy (Numerical Python) features
Get started with data analysis tools in the pandas library
Use high-performance tools to load, clean, transform, merge, and reshape data
Create scatter plots and static or interactive visualizations with matplotlib
Apply the pandas groupby facility to slice, dice, and summarize datasets
Measure data by points in time, whether it’s specific instances, fixed periods, or intervals
Learn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples
Machine Learning with R - Second Edition

Key Features
Harness the power of R for statistical computing and data science
Explore, forecast, and classify data with R
Use R to apply common machine learning algorithms to real-world scenarios
Book Description
Machine learning, at its core, is concerned with transforming data into actionable knowledge. This makes machine learning well suited to the present-day era of big data. Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning to your data. Whether you are new to data analytics or a veteran, machine learning with R offers a powerful set of methods to quickly and easily gain insights from your data.

Want to turn your data into actionable knowledge, predict outcomes that make real impact, and have constantly developing insights? R gives you access to the cutting-edge power you need to master exceptional machine learning techniques.

Updated and upgraded to the latest libraries and most modern thinking, the second edition of Machine Learning with R provides you with a rigorous introduction to this essential skill of professional data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience.

With this book you’ll discover all the analytical tools you need to gain insights from complex data and learn how to to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you’ll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering. Transform the way you think about data; discover machine learning with R.

What you will learn
Harness the power of R to build common machine learning algorithms with real-world data science applications
Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results
Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems
Classify your data with Bayesian and nearest neighbour methods
Predict values by using R to build decision trees, rules, and support vector machines
Forecast numeric values with linear regression, and model your data with neural networks
Evaluate and improve the performance of machine learning models
Learn specialized machine learning techniques for text mining, social network data, big data, and more
About the Author
Brett Lantz has used innovative data methods to understand human behavior for more than 10 years. A sociologist by training, he was first enchanted by machine learning while studying a large database of teenagers' social networking website profiles. Since then, he has worked on the interdisciplinary studies of cellular telephone calls, medical billing data, and philanthropic activity, among others.

Table of Contents
Introducing Machine Learning
Managing and Understanding Data
Lazy Learning – Classification Using Nearest Neighbors
Probabilistic Learning – Classification Using Naive Bayes
Divide and Conquer – Classification Using Decision Trees and Rules
Forecasting Numeric Data – Regression Methods
Black Box Methods – Neural Networks and Support Vector Machines
Finding Patterns – Market Basket Analysis Using Association Rules
Finding Groups of Data – Clustering with K-means
Evaluating Model Performance
Improving Model Performance
Specialized Machine Learning Topics
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools


Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

Many companies need developers with data science skills to work on projects ranging from social media marketing to machine learning. Discovering what you need to learn to begin a career as a data scientist can seem bewildering. This book is designed to help you get started.

About the Book

Introducing Data ScienceIntroducing Data Science explains vital data science concepts and teaches you how to accomplish the fundamental tasks that occupy data scientists. You’ll explore data visualization, graph databases, the use of NoSQL, and the data science process. You’ll use the Python language and common Python libraries as you experience firsthand the challenges of dealing with data at scale. Discover how Python allows you to gain insights from data sets so big that they need to be stored on multiple machines, or from data moving so quickly that no single machine can handle it. This book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels. After reading this book, you’ll have the solid foundation you need to start a career in data science.

What’s Inside

Handling large data
Introduction to machine learning
Using Python to work with data
Writing data science algorithms
About the Reader

This book assumes you're comfortable reading code in Python or a similar language, such as C, Ruby, or JavaScript. No prior experience with data science is required.

About the Authors

Davy Cielen, Arno D. B. Meysman, and Mohamed Ali are the founders and managing partners of Optimately and Maiton, where they focus on developing data science projects and solutions in various sectors.

Table of Contents

Data science in a big data world
The data science process
Machine learning
Handling large data on a single computer
First steps in big data
Join the NoSQL movement
The rise of graph databases
Text mining and text analytics
Data visualization to the end user
Mastering Python for Data Science

Explore the world of data science through Python and learn how to make sense of data

About This Book
Master data science methods using Python and its libraries
Create data visualizations and mine for patterns
Advanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learning
Who This Book Is For
If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed.

What You Will Learn
Manage data and perform linear algebra in Python
Derive inferences from the analysis by performing inferential statistics
Solve data science problems in Python
Create high-end visualizations using Python
Evaluate and apply the linear regression technique to estimate the relationships among variables.
Build recommendation engines with the various collaborative filtering algorithms
Apply the ensemble methods to improve your predictions
Work with big data technologies to handle data at scale
In Detail
Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving.

This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science.

Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods.

Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics.

Style and approach
This book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios.
R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics (O'Reilly Cookbooks)

With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.

Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an experienced data programmer, it will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process.

Create vectors, handle variables, and perform other basic functions
Input and output data
Tackle data structures such as matrices, lists, factors, and data frames
Work with probability, probability distributions, and random variables
Calculate statistics and confidence intervals, and perform statistical tests
Create a variety of graphic displays
Build statistical models with linear regressions and analysis of variance (ANOVA)
Explore advanced statistical techniques, such as finding clusters in your data
"Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language—one practical example at a time."—Jeffrey Ryan, software consultant and R package author
Think Like a Data Scientist: Tackle the data science process step-by-step


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 ePub formats from Manning Publications.

About the Technology

Data collected from customers, scientific measurements, IoT 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

The data science process, step-by-step
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

Philosophies 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
Developing a plan
Statistics and modeling: concepts and foundations
Software: statistics in action
Supplementary software: bigger, faster, more efficient
Plan execution: putting it all together
Delivering a product
After product delivery: problems and revisions
Wrapping up: putting the project away
R Programming for Data Science

Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.
Machine Learning With R Cookbook - 110 Recipes for Building Powerful Predictive Models with R

Key Features
Apply R to simplify predictive modeling with short and simple code
Use machine learning to solve problems ranging from small to big data
Build a training and testing dataset from the churn dataset, applying different classification methods
Book Description
The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics.

This book covers the basics of R by setting up a user-friendly programming environment and performing data ETL in R. Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationships. You will then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimension reduction.

What you will learn
Create and inspect the transaction dataset, performing association analysis with the Apriori algorithm
Visualize patterns and associations using a range of graphs and find frequent itemsets using the Eclat algorithm
Compare differences between each regression method to discover how they solve problems
Predict possible churn users with the classification approach
Implement the clustering method to segment customer data
Compress images with the dimension reduction method
Incorporate R and Hadoop to solve machine learning problems on Big Data
About the Author
Yu-Wei, Chiu (David Chiu) is the founder of Largit Data. He has previously worked for Trend Micro as a software engineer, with the responsibility of building big data platforms for business intelligence and customer relationship management systems. In addition to being a start-up entrepreneur and data scientist, he specializes in using Spark and Hadoop to process big data and apply data mining techniques for data analysis.

Table of Contents
Practical Machine Learning with R
Data Exploration with RMS Titanic
R and Statistics
Understanding Regression Analysis
Classification (I) Tree, Lazy, and Probabilistic
Classification (II) Neural Network and SVM
Model Evaluation
Ensemble Learning
Association Analysis and Sequence Minin
Dimension Reduction
Big Data Analysis (R and Hadoop)