Building An Effective Data Science Practice

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Building an Effective Data Science Practice

Vineet Raina,Srinath Krishnamurthy
Building an Effective Data Science Practice Book PDF

Author : Vineet Raina,Srinath Krishnamurthy
Publisher : Unknown
Page : 0 pages
File Size : 50,8 Mb
Release : 2022
Category : Electronic
ISBN : 1484274202

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Building an Effective Data Science Practice by Vineet Raina,Srinath Krishnamurthy Pdf

Gain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation. You'll start by delving into the fundamentals of data science - classes of data science problems, data science techniques and their applications - and gradually build up to building a professional reference operating model for a data science function in an organization. This operating model covers the roles and skills required in a team, the techniques and technologies they use, and the best practices typically followed in executing data science projects. Building an Effective Data Science Practice provides a common base of reference knowledge and solutions, and addresses the kinds of challenges that arise to ensure your data science team is both productive and aligned with the business goals from the very start. Reinforced with real examples, this book allows you to confidently determine the strategic answers to effectively align your business goals with the operations of the data science practice. You will: Transform business objectives into concrete problems that can be solved using data science Evaluate how problems and the specifics of a business drive the techniques and model evaluation guidelines used in a project Build and operate an effective interdisciplinary data science team within an organization Evaluating the progress of the team towards the business RoI Understand the important regulatory aspects that are applicable to a data science practice .

Building an Effective Data Science Practice

Vineet Raina,Srinath Krishnamurthy
Building an Effective Data Science Practice Book PDF

Author : Vineet Raina,Srinath Krishnamurthy
Publisher : Apress
Page : 368 pages
File Size : 53,7 Mb
Release : 2021-12-09
Category : Computers
ISBN : 1484274180

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Building an Effective Data Science Practice by Vineet Raina,Srinath Krishnamurthy Pdf

Gain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation. You’ll start by delving into the fundamentals of data science – classes of data science problems, data science techniques and their applications – and gradually build up to building a professional reference operating model for a data science function in an organization. This operating model covers the roles and skills required in a team, the techniques and technologies they use, and the best practices typically followed in executing data science projects. Building an Effective Data Science Practice provides a common base of reference knowledge and solutions, and addresses the kinds of challenges that arise to ensure your data science team is both productive and aligned with the business goals from the very start. Reinforced with real examples, this book allows you to confidently determine the strategic answers to effectively align your business goals with the operations of the data science practice. What You’ll Learn Transform business objectives into concrete problems that can be solved using data science Evaluate how problems and the specifics of a business drive the techniques and model evaluation guidelines used in a project Build and operate an effective interdisciplinary data science team within an organization Evaluating the progress of the team towards the business RoI Understand the important regulatory aspects that are applicable to a data science practice Who This Book Is For Technology leaders, data scientists, and project managers

R for Data Science

Hadley Wickham,Garrett Grolemund
R for Data Science Book PDF

Author : Hadley Wickham,Garrett Grolemund
Publisher : "O'Reilly Media, Inc."
Page : 521 pages
File Size : 41,6 Mb
Release : 2016-12-12
Category : Computers
ISBN : 9781491910368

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R for Data Science by Hadley Wickham,Garrett Grolemund Pdf

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

Managing Data Science

Kirill Dubovikov
Managing Data Science Book PDF

Author : Kirill Dubovikov
Publisher : Packt Publishing Ltd
Page : 276 pages
File Size : 45,9 Mb
Release : 2019-11-12
Category : Computers
ISBN : 9781838824563

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Managing Data Science by Kirill Dubovikov Pdf

Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key FeaturesLearn the basics of data science and explore its possibilities and limitationsManage data science projects and assemble teams effectively even in the most challenging situationsUnderstand management principles and approaches for data science projects to streamline the innovation processBook Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learnUnderstand the underlying problems of building a strong data science pipelineExplore the different tools for building and deploying data science solutionsHire, grow, and sustain a data science teamManage data science projects through all stages, from prototype to productionLearn how to use ModelOps to improve your data science pipelinesGet up to speed with the model testing techniques used in both development and production stagesWho this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book.

Effective Data Science Infrastructure

Ville Tuulos
Effective Data Science Infrastructure Book PDF

Author : Ville Tuulos
Publisher : Simon and Schuster
Page : 350 pages
File Size : 43,7 Mb
Release : 2022-08-30
Category : Computers
ISBN : 9781638350989

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Effective Data Science Infrastructure by Ville Tuulos Pdf

Simplify data science infrastructure to give data scientists an efficient path from prototype to production. In Effective Data Science Infrastructure you will learn how to: Design data science infrastructure that boosts productivity Handle compute and orchestration in the cloud Deploy machine learning to production Monitor and manage performance and results Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, Conda, and Docker Architect complex applications for multiple teams and large datasets Customize and grow data science infrastructure Effective Data Science Infrastructure: How to make data scientists more productive is a hands-on guide to assembling infrastructure for data science and machine learning applications. It reveals the processes used at Netflix and other data-driven companies to manage their cutting edge data infrastructure. In it, you’ll master scalable techniques for data storage, computation, experiment tracking, and orchestration that are relevant to companies of all shapes and sizes. You’ll learn how you can make data scientists more productive with your existing cloud infrastructure, a stack of open source software, and idiomatic Python. The author is donating proceeds from this book to charities that support women and underrepresented groups in data science. About the technology Growing data science projects from prototype to production requires reliable infrastructure. Using the powerful new techniques and tooling in this book, you can stand up an infrastructure stack that will scale with any organization, from startups to the largest enterprises. About the book Effective Data Science Infrastructure teaches you to build data pipelines and project workflows that will supercharge data scientists and their projects. Based on state-of-the-art tools and concepts that power data operations of Netflix, this book introduces a customizable cloud-based approach to model development and MLOps that you can easily adapt to your company’s specific needs. As you roll out these practical processes, your teams will produce better and faster results when applying data science and machine learning to a wide array of business problems. What's inside Handle compute and orchestration in the cloud Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, AWS, and the Python data ecosystem Architect complex applications that require large datasets and models, and a team of data scientists About the reader For infrastructure engineers and engineering-minded data scientists who are familiar with Python. About the author At Netflix, Ville Tuulos designed and built Metaflow, a full-stack framework for data science. Currently, he is the CEO of a startup focusing on data science infrastructure. Table of Contents 1 Introducing data science infrastructure 2 The toolchain of data science 3 Introducing Metaflow 4 Scaling with the compute layer 5 Practicing scalability and performance 6 Going to production 7 Processing data 8 Using and operating models 9 Machine learning with the full stack

Build a Career in Data Science

Emily Robinson,Jacqueline Nolis
Build a Career in Data Science Book PDF

Author : Emily Robinson,Jacqueline Nolis
Publisher : Manning Publications
Page : 352 pages
File Size : 55,7 Mb
Release : 2020-03-24
Category : Computers
ISBN : 9781617296246

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Build a Career in Data Science by Emily Robinson,Jacqueline Nolis Pdf

Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder

Data Science for Undergraduates

National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Board on Science Education,Division on Engineering and Physical Sciences,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics,Computer Science and Telecommunications Board,Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective
Data Science for Undergraduates Book PDF

Author : National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Board on Science Education,Division on Engineering and Physical Sciences,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics,Computer Science and Telecommunications Board,Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective
Publisher : National Academies Press
Page : 139 pages
File Size : 43,6 Mb
Release : 2018-11-11
Category : Education
ISBN : 9780309475594

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Data Science for Undergraduates by National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Board on Science Education,Division on Engineering and Physical Sciences,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics,Computer Science and Telecommunications Board,Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective Pdf

Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.

Practical Statistics for Data Scientists

Peter Bruce,Andrew Bruce
Practical Statistics for Data Scientists Book PDF

Author : Peter Bruce,Andrew Bruce
Publisher : "O'Reilly Media, Inc."
Page : 395 pages
File Size : 45,5 Mb
Release : 2017-05-10
Category : Computers
ISBN : 9781491952917

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Practical Statistics for Data Scientists by Peter Bruce,Andrew Bruce Pdf

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

Data Analytics Essentials You Always Wanted To Know

Vibrant Publishers,Dr. Bianca Szasz
Data Analytics Essentials You Always Wanted To Know Book PDF

Author : Vibrant Publishers,Dr. Bianca Szasz
Publisher : Vibrant Publishers
Page : 170 pages
File Size : 46,8 Mb
Release : 2024-02-29
Category : Business & Economics
ISBN : 9781636511191

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Data Analytics Essentials You Always Wanted To Know by Vibrant Publishers,Dr. Bianca Szasz Pdf

Upon reading this book, you will get:  A fundamental comprehension of data analytics, including its types  An understanding of data analytics processes, software tools, and a range of analytics methodologies  A comprehension of what daily tasks and procedures the data analysts follow  An investigation into the vast field of big data analytics, covering its possibilities and challenges  An understanding of the existing legal frameworks, as well as ethical and privacy issues in data analytics  Application-based learning using a variety of real-world case studies From raw data to actionable insights - journey through the essentials of data analytics. Data Analytics Essentials You Always Wanted To Know is an approachable and captivating guide to understand the complicated world of data Data analytics is becoming increasingly important in today's data-driven society, and so has the demand for data analysts. Data Analytics Essentials You Always Wanted to Know (Data Analytics Essentials) is a comprehensive yet succinct manual, perfect for you if you are trying to understand the fundamentals of data analytics. It gives a concise introduction to data analytics and its current applicability. This book is a great tool for professionals switching to a career in data analytics and for students who want to learn the basics of data analytics. It will give you a strong foundation by explaining everything in an easy-to-understand language. Data Analytics Essentials goes beyond a theoretical manual and contains real-world case studies and fun facts to help you enhance your knowledge. The chapter summaries and self- assessment tests along with every chapter will help you test yourself as you move from one concept to the next.

Enhancing and Predicting Digital Consumer Behavior with AI

Musiolik, Thomas Heinrich,Rodriguez, Raul Villamarin,Kannan, Hemachandran
Enhancing and Predicting Digital Consumer Behavior with AI Book PDF

Author : Musiolik, Thomas Heinrich,Rodriguez, Raul Villamarin,Kannan, Hemachandran
Publisher : IGI Global
Page : 464 pages
File Size : 47,6 Mb
Release : 2024-05-13
Category : Business & Economics
ISBN : 9798369344545

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Enhancing and Predicting Digital Consumer Behavior with AI by Musiolik, Thomas Heinrich,Rodriguez, Raul Villamarin,Kannan, Hemachandran Pdf

Understanding consumer behavior in today's digital landscape is more challenging than ever. Businesses must navigate a sea of data to discern meaningful patterns and correlations that drive effective customer engagement and product development. However, the ever-changing nature of consumer behavior presents a daunting task, making it difficult for companies to gauge the wants and needs of their target audience accurately. Enhancing and Predicting Digital Consumer Behavior with AI offers a comprehensive solution to this pressing issue. A strong focus on concepts, theories, and analytical techniques for tracking consumer behavior changes provides the roadmap for businesses to navigate the complexities of the digital age. By covering topics such as digital consumers, emotional intelligence, and data analytics, this book serves as a timely and invaluable resource for academics and practitioners seeking to understand and adapt to the evolving landscape of consumer behavior.

Malware Data Science

Joshua Saxe,Hillary Sanders
Malware Data Science Book PDF

Author : Joshua Saxe,Hillary Sanders
Publisher : No Starch Press
Page : 274 pages
File Size : 49,6 Mb
Release : 2018-09-25
Category : Computers
ISBN : 9781593278595

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Malware Data Science by Joshua Saxe,Hillary Sanders Pdf

Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization. Security has become a "big data" problem. The growth rate of malware has accelerated to tens of millions of new files per year while our networks generate an ever-larger flood of security-relevant data each day. In order to defend against these advanced attacks, you'll need to know how to think like a data scientist. In Malware Data Science, security data scientist Joshua Saxe introduces machine learning, statistics, social network analysis, and data visualization, and shows you how to apply these methods to malware detection and analysis. You'll learn how to: - Analyze malware using static analysis - Observe malware behavior using dynamic analysis - Identify adversary groups through shared code analysis - Catch 0-day vulnerabilities by building your own machine learning detector - Measure malware detector accuracy - Identify malware campaigns, trends, and relationships through data visualization Whether you're a malware analyst looking to add skills to your existing arsenal, or a data scientist interested in attack detection and threat intelligence, Malware Data Science will help you stay ahead of the curve.

SQL for Data Scientists

Renee M. P. Teate
SQL for Data Scientists Book PDF

Author : Renee M. P. Teate
Publisher : John Wiley & Sons
Page : 400 pages
File Size : 51,9 Mb
Release : 2021-08-17
Category : Computers
ISBN : 9781119669395

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SQL for Data Scientists by Renee M. P. Teate Pdf

Jump-start your career as a data scientist—learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that’s dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls. You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data. This guide for data scientists differs from other instructional guides on the subject. It doesn’t cover SQL broadly. Instead, you’ll learn the subset of SQL skills that data analysts and data scientists use frequently. You’ll also gain practical advice and direction on "how to think about constructing your dataset." Gain an understanding of relational database structure, query design, and SQL syntax Develop queries to construct datasets for use in applications like interactive reports and machine learning algorithms Review strategies and approaches so you can design analytical datasets Practice your techniques with the provided database and SQL code In this book, author Renee Teate shares knowledge gained during a 15-year career working with data, in roles ranging from database developer to data analyst to data scientist. She guides you through SQL code and dataset design concepts from an industry practitioner’s perspective, moving your data scientist career forward!

The Data Science Design Manual

Steven S. Skiena
The Data Science Design Manual Book PDF

Author : Steven S. Skiena
Publisher : Springer
Page : 445 pages
File Size : 44,5 Mb
Release : 2017-07-01
Category : Computers
ISBN : 9783319554440

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The Data Science Design Manual by Steven S. Skiena Pdf

This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)

Foundations of Data Science

Avrim Blum,John Hopcroft,Ravindran Kannan
Foundations of Data Science Book PDF

Author : Avrim Blum,John Hopcroft,Ravindran Kannan
Publisher : Cambridge University Press
Page : 433 pages
File Size : 52,7 Mb
Release : 2020-01-23
Category : Computers
ISBN : 9781108485067

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Foundations of Data Science by Avrim Blum,John Hopcroft,Ravindran Kannan Pdf

Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.

Practical DataOps

Harvinder Atwal
Practical DataOps Book PDF

Author : Harvinder Atwal
Publisher : Apress
Page : 289 pages
File Size : 41,7 Mb
Release : 2019-12-09
Category : Computers
ISBN : 9781484251041

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Practical DataOps by Harvinder Atwal Pdf

Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles. This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. What You Will LearnDevelop a data strategy for your organization to help it reach its long-term goals Recognize and eliminate barriers to delivering data to users at scale Work on the right things for the right stakeholders through agile collaboration Create trust in data via rigorous testing and effective data management Build a culture of learning and continuous improvement through monitoring deployments and measuring outcomes Create cross-functional self-organizing teams focused on goals not reporting lines Build robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data products Who This Book Is For Data science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production.