Download Data Science For Business Book PDF

Download full Data Science For Business books PDF, EPUB, Tuebl, Textbook, Mobi or read online Data Science For Business anytime and anywhere on any device. Get free access to the library by create an account, fast download and ads free. We cannot guarantee that every book is in the library.

Data Science for Business

Data Science for Business
  • Author : Foster Provost,Tom Fawcett
  • Publisher :Unknown
  • Release Date :2013-07-27
  • Total pages :414
  • ISBN : 9781449374280
GET BOOK HERE

Summary : 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

Data Science for Business

Data Science for Business
  • Author : Foster Provost,Tom Fawcett
  • Publisher :Unknown
  • Release Date :2013-07-27
  • Total pages :414
  • ISBN : 9781449374297
GET BOOK HERE

Summary : Annotation This broad, deep, but not-too-technical guide introduces you to 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. By learning data science principles, you will understand the many data-mining techniques in use today. More importantly, these principles underpin the processes and strategies necessary to solve business problems through data mining techniques.

Data Science for Business

Data Science for Business
  • Author : Foster Provost
  • Publisher :Unknown
  • Release Date :2013
  • Total pages :229
  • ISBN : 1449374271
GET BOOK HERE

Summary :

Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions

Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions
  • Author : Matt Taddy
  • Publisher :Unknown
  • Release Date :2019-08-23
  • Total pages :384
  • ISBN : 9781260452785
GET BOOK HERE

Summary : Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: •Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling•Understand how use ML tools in real world business problems, where causation matters more that correlation•Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science.

Data Science for Business and Decision Making

Data Science for Business and Decision Making
  • Author : Luiz Paulo Fávero,Patrícia Belfiore
  • Publisher :Unknown
  • Release Date :2019-03-08
  • Total pages :1000
  • ISBN : 0128112166
GET BOOK HERE

Summary : Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other. As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work. Its emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics. Each chapter uses a didactic format that is followed by exercises and answers. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software®, and IBM SPSS Statistics Software®. Combines statistics and operations research modeling to teach the principles of business analytics Written for students who want to apply statistics, optimization and multivariate modeling to gain competitive advantages in business Shows how powerful software packages, such as SPSS and Stata, can create graphical and numerical outputs

Data Science for Business 2019 (2 BOOKS IN 1)

Data Science for Business 2019 (2 BOOKS IN 1)
  • Author : Riley Adams,Matt Henderson
  • Publisher :Unknown
  • Release Date :2019-05-12
  • Total pages :290
  • ISBN : 199917707X
GET BOOK HERE

Summary : ★This book includes 2 Manuscripts★ Are you looking for new ways to grow your business, with resources you already have? Do you want to know how the big players like Netflix, Amazon, or Shopify use data analytics to MULTIPLY their growth? Keep listening to learn how to use data analytics to maximize YOUR business.

Data Science for Business

Data Science for Business
  • Author : Herbert Jones
  • Publisher :Unknown
  • Release Date :2018-09-26
  • Total pages :100
  • ISBN : 1727618572
GET BOOK HERE

Summary : Do you want to learn about data science but aren't in the mood to read a boring textbook? Data science has a huge impact on how companies conduct business, and those who don't learn about this revolutionaryfield could be left behind. You see, data science will help you make better decisions, know what products and services to release, and how to provide better service to your customers. And it is all done by collecting and sorting through a large amount of information, so you have the right sources behind you when you make a major decision. In this guidebook, you will discover more about data science and how to get started in this field. This book will discuss the following topics: What is data science? How Big Data works and why it is so important How to do an explorative data analysis Working with data mining How to mine text to get the data Some amazing machine learning algorithms to help with data science How to do data modeling Data visualization How to use data science to help your business grow Tips to help you get started with data science And much, much more! So if you are ready to get started with data science, click "add to cart"!

Data Science for Business

Data Science for Business
  • Author : Charles Jensen
  • Publisher :Unknown
  • Release Date :2017-07-12
  • Total pages :51
  • ISBN : 1980292841
GET BOOK HERE

Summary : Basic data science explained Explore the field of data science, and the way to analyze big and small data. This technical book goes over the main aspects of analyzing data correctly by using various strategies you need to implement in order to get results that are precise and beneficial. Learn about: Modeling data and visualization. The three V's of big data and what to do with them. Software recommendations and applications. Machine algorithms and interesting side notes regarding them. Rules, infrastructure, adaptation, and other techniques. Perception and cognition basics that apply to data. Efficient uses of regression, database querying, machine learning, and data warehousing. Curious yet? Then don't wait and start reading, so you don't have to remain in the dark. Save yourself the time and learn from what worked for me. I will see you in the first chapter!

Big Data MBA

Big Data MBA
  • Author : Bill Schmarzo
  • Publisher :Unknown
  • Release Date :2015-12-11
  • Total pages :312
  • ISBN : 9781119238843
GET BOOK HERE

Summary : Integrate big data into business to drive competitive advantage and sustainable success Big Data MBA brings insight and expertise to leveraging big data in business so you can harness the power of analytics and gain a true business advantage. Based on a practical framework with supporting methodology and hands-on exercises, this book helps identify where and how big data can help you transform your business. You'll learn how to exploit new sources of customer, product, and operational data, coupled with advanced analytics and data science, to optimize key processes, uncover monetization opportunities, and create new sources of competitive differentiation. The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization's user experience to customers and front-end employees alike. You'll learn to “think like a data scientist” as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce. Business stakeholders no longer need to relinquish control of data and analytics to IT. In fact, they must champion the organization's data collection and analysis efforts. This book is a primer on the business approach to analytics, providing the practical understanding you need to convert data into opportunity. Understand where and how to leverage big data Integrate analytics into everyday operations Structure your organization to drive analytic insights Optimize processes, uncover opportunities, and stand out from the rest Help business stakeholders to “think like a data scientist” Understand appropriate business application of different analytic techniques If you want data to transform your business, you need to know how to put it to use. Big Data MBA shows you how to implement big data and analytics to make better decisions.

Data Science for Business Professionals

Data Science for Business Professionals
  • Author : Probyto Data Science and Consulting Pvt. Ltd.
  • Publisher :Unknown
  • Release Date :2020-05-06
  • Total pages :368
  • ISBN : 9789389423280
GET BOOK HERE

Summary : Primer into the multidisciplinary world of Data Science KEY FEATURES - Explore and use the key concepts of Statistics required to solve data science problems - Use Docker, Jenkins, and Git for Continuous Development and Continuous Integration of your web app - Learn how to build Data Science solutions with GCP and AWS DESCRIPTION The book will initially explain the What-Why of Data Science and the process of solving a Data Science problem. The fundamental concepts of Data Science, such as Statistics, Machine Learning, Business Intelligence, Data pipeline, and Cloud Computing, will also be discussed. All the topics will be explained with an example problem and will show how the industry approaches to solve such a problem. The book will pose questions to the learners to solve the problems and build the problem-solving aptitude and effectively learn. The book uses Mathematics wherever necessary and will show you how it is implemented using Python with the help of an example dataset. WHAT WILL YOU LEARN - Understand the multi-disciplinary nature of Data Science - Get familiar with the key concepts in Mathematics and Statistics - Explore a few key ML algorithms and their use cases - Learn how to implement the basics of Data Pipelines - Get an overview of Cloud Computing & DevOps - Learn how to create visualizations using Tableau WHO THIS BOOK IS FOR This book is ideal for Data Science enthusiasts who want to explore various aspects of Data Science. Useful for Academicians, Business owners, and Researchers for a quick reference on industrial practices in Data Science. TABLE OF CONTENTS 1. Data Science in Practice 2. Mathematics Essentials 3. Statistics Essentials 4. Exploratory Data Analysis 5. Data preprocessing 6. Feature Engineering 7. Machine learning algorithms 8. Productionizing ML models 9. Data Flows in Enterprises 10. Introduction to Databases 11. Introduction to Big Data 12. DevOps for Data Science 13. Introduction to Cloud Computing 14. Deploy Model to Cloud 15. Introduction to Business Intelligence 16. Data Visualization Tools 17. Industry Use Case 1 – FormAssist 18. Industry Use Case 2 – PeopleReporter 19. Data Science Learning Resources 20. Do It Your Self Challenges 21. MCQs for Assessments

Data Science from Scratch

Data Science from Scratch
  • Author : Joel Grus
  • Publisher :Unknown
  • Release Date :2015-04-14
  • Total pages :330
  • ISBN : 9781491904404
GET BOOK HERE

Summary : 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 Science for Business and Decision Making

Data Science for Business and Decision Making
  • Author : Luiz Paulo Fávero,Patrícia Belfiore
  • Publisher :Unknown
  • Release Date :2019-04-11
  • Total pages :1240
  • ISBN : 9780128112175
GET BOOK HERE

Summary : Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other. As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work. Its emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics. Each chapter uses a didactic format that is followed by exercises and answers. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software®, and IBM SPSS Statistics Software®. Combines statistics and operations research modeling to teach the principles of business analytics Written for students who want to apply statistics, optimization and multivariate modeling to gain competitive advantages in business Shows how powerful software packages, such as SPSS and Stata, can create graphical and numerical outputs

Data Science For Dummies

Data Science For Dummies
  • Author : Lillian Pierson
  • Publisher :Unknown
  • Release Date :2017-03-06
  • Total pages :384
  • ISBN : 9781119327639
GET BOOK HERE

Summary : Discover how data science can help you gain in-depth insight into your business - the easy way! 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, reading 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 Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate 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.

Business Intelligence

Business Intelligence
  • Author : Ramesh Sharda,Dursun Delen,Efraim Turban
  • Publisher :Unknown
  • Release Date :2017-01-13
  • Total pages :512
  • ISBN : 0134633288
GET BOOK HERE

Summary : For courses on Business Intelligence or Decision Support Systems. A managerial approach to understanding business intelligence systems. To help future managers use and understand analytics, Business Intelligence provides students with a solid foundation of BI that is reinforced with hands-on practice.

Applied Data Science

Applied Data Science
  • Author : Martin Braschler,Thilo Stadelmann,Kurt Stockinger
  • Publisher :Unknown
  • Release Date :2019-06-13
  • Total pages :465
  • ISBN : 9783030118211
GET BOOK HERE

Summary : This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.

Doing Data Science

Doing Data Science
  • Author : Cathy O'Neil,Rachel Schutt
  • Publisher :Unknown
  • Release Date :2013-10-09
  • Total pages :408
  • ISBN : 9781449363895
GET BOOK HERE

Summary : 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 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.

Data Science

Data Science
  • Author : Vijay Kotu,Bala Deshpande
  • Publisher :Unknown
  • Release Date :2018-11-27
  • Total pages :568
  • ISBN : 9780128147627
GET BOOK HERE

Summary : Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You’ll be able to: Gain the necessary knowledge of different data science techniques to extract value from data. Master the concepts and inner workings of 30 commonly used powerful data science algorithms. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more... Contains fully updated content on data science, including tactics on how to mine business data for information Presents simple explanations for over twenty powerful data science techniques Enables the practical use of data science algorithms without the need for programming Demonstrates processes with practical use cases Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language Describes the commonly used setup options for the open source tool RapidMiner

Data Science and Digital Business

Data Science and Digital Business
  • Author : Fausto Pedro García Márquez,Benjamin Lev
  • Publisher :Unknown
  • Release Date :2019-01-04
  • Total pages :316
  • ISBN : 9783319956510
GET BOOK HERE

Summary : This book combines the analytic principles of digital business and data science with business practice and big data. The interdisciplinary, contributed volume provides an interface between the main disciplines of engineering and technology and business administration. Written for managers, engineers and researchers who want to understand big data and develop new skills that are necessary in the digital business, it not only discusses the latest research, but also presents case studies demonstrating the successful application of data in the digital business.

Getting Started with Data Science

Getting Started with Data Science
  • Author : Murtaza Haider
  • Publisher :Unknown
  • Release Date :2015-12-14
  • Total pages :400
  • ISBN : 9780133991239
GET BOOK HERE

Summary : Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon.

Data Science for Business With R

Data Science for Business With R
  • Author : Jeffrey S. Saltz,Jeffrey M. Stanton
  • Publisher :Unknown
  • Release Date :2021-02-14
  • Total pages :424
  • ISBN : 9781544370460
GET BOOK HERE

Summary : Data Science for Business with R, written by Jeffrey S. Saltz and Jeffrey M. Stanton, focuses on the concepts foundational for students starting a business analytics or data science degree program. To keep the book practical and applied, the authors feature a running case using a global airline business’s customer survey dataset to illustrate how to turn data in business decisions, in addition to numerous examples throughout. To aid in usability beyond the classroom, the text features full integration of freely-available R and RStudio software, one of the most popular data science tools available. Designed for students with little to no experience in related areas like computer science, the book chapters follow a logical order from introduction and installation of R and RStudio, working with data architecture, undertaking data collection, performing data analysis, and transitioning to data archiving and presentation. Each chapter follows a familiar structure, starting with learning objectives and background, following the basic steps of functions alongside simple examples, applying these functions to the case study, and ending with chapter challenge questions, sources, and a list of R functions so students know what to expect in each step of their data science course. Data Science for Business with R provides readers with a straightforward and applied guide to this new and evolving field.

Machine Learning in Business

Machine Learning in Business
  • Author : John C Hull
  • Publisher :Unknown
  • Release Date :2020-05-08
  • Total pages :276
  • ISBN : 9798644074372
GET BOOK HERE

Summary : This book is for business executives and students who want to learn about the tools used in machine learning. In creating the second edition, John Hull has continued to improve his material and added three new chapters. The book explains the most popular algorithms clearly and succinctly without using calculus or matrix/vector algebra. The focus is on business applications. There are many illustrative examples. These include assessing the risk of a country for international investment, predicting the value of real estate, and classifying retail loans as acceptable or unacceptable. Data, worksheets, and Python code for the examples is on the author's website. A complete set of PowerPoint slides that can be used by instructors is also on the website. The opening chapter reviews different types of machine learning models. It explains the role of the training data set, the validation data set, and the test data set. It also explains the issues involved in cleaning data and reviews Bayes' theorem. Chapter 2 is devoted to unsupervised learning. It explains the k-means algorithm and alternative approaches to clustering. It also covers principal components analysis. Chapter 3 explains linear and logistic regression. It covers regularization using Ridge, Lasso, and Elastic Net. Chapter 4 covers decision trees. It includes a discussion of the naive Bayes classifier, random forests, and other ensemble methods. Chapter 5, explains how the SVM approach can be used for both linear and non-linear classification as well as for the prediction of a continuous variable. Chapter 6 is devoted to neural networks. It includes a discussion of the gradient descent algorithm, backpropagation, stopping rules, autoencoders, convolutional neural networks, and recurrent neural networks. Chapter 7 explains reinforcement learning using two games as examples. It covers Q-learning and deep Q-learning, and discusses applications. Chapter 8 covers natural language processing. It discusses how the algorithms introduced in the book can be used for sentiment analysis, language translation and information retrieval. Chapter 9 is concerned with model interpretability. It discusses the importance of making models understandable and the procedures that can be used for both white-box and black-box models. Chapter 10 explains two applications involving derivatives that the author has been involved in. The final chapter focuses on issues for society. The topics covered include data privacy, biases, ethical considerations, legal issues, and adversarial machine learning. At the ends of chapters there are short concept questions to test the readers understanding of the material and longer exercises. Answers are at the end of the book. The book includes a glossary of terms and an index.