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

Author: Matt Taddy
Publisher: McGraw Hill Professional
ISBN: 1260452786
Category : Business & Economics
Languages : en
Pages : 384

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Book Description
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.

Author: Chunlei Tang
Publisher: John Wiley & Sons
ISBN: 111913840X
Category : Mathematics
Languages : en
Pages : 217

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Book Description
Provides an introduction of the data industry to the field of economics This book bridges the gap between economics and data science to help data scientists understand the economics of big data, and enable economists to analyze the data industry. It begins by explaining data resources and introduces the data asset. This book defines a data industry chain, enumerates data enterprises’ business models versus operating models, and proposes a mode of industrial development for the data industry. The author describes five types of enterprise agglomerations, and multiple industrial cluster effects. A discussion on the establishment and development of data industry related laws and regulations is provided. In addition, this book discusses several scenarios on how to convert data driving forces into productivity that can then serve society. This book is designed to serve as a reference and training guide for ata scientists, data-oriented managers and executives, entrepreneurs, scholars, and government employees. Defines and develops the concept of a “Data Industry,” and explains the economics of data to data scientists and statisticians Includes numerous case studies and examples from a variety of industries and disciplines Serves as a useful guide for practitioners and entrepreneurs in the business of data technology The Data Industry: The Business and Economics of Information and Big Data is a resource for practitioners in the data science industry, government, and students in economics, business, and statistics. CHUNLEI TANG, Ph.D., is a research fellow at Harvard University. She is the co-founder of Fudan’s Institute for Data Industry and proposed the concept of the “data industry”. She received a Ph.D. in Computer and Software Theory in 2012 and a Master of Software Engineering in 2006 from Fudan University, Shanghai, China.

Author: Vincent Granville
Publisher: John Wiley & Sons
ISBN: 1118810090
Category : Computers
Languages : en
Pages : 336

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Book Description
Learn what it takes to succeed in the the most in-demand tech job Harvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code. The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you're looking to become a data scientist or to hire one. Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of terms Shows what companies are looking for and how the growing importance of big data has increased the demand for data scientists Features job interview questions, sample resumes, salary surveys, and examples of job ads Case studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business-critical situations Developing Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates.

Author: K. Scott Proctor
Publisher: John Wiley & Sons
ISBN: 1118102630
Category : Business & Economics
Languages : en
Pages : 285

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Book Description
A valuable guide to making better IT decisions within business Optimizing and Assessing Information Technology is designed to be both easy-to-use and immediately useful. Engaging and accessible, this book has been created to help you focus on improving business project execution through effective IT optimization and assessment. While it skillfully outlines a framework for optimizing and assessing IT, it does not get into specific technologies per se, given the rapid and increasing pace of technical change across the world today. Optimizing and Assessing Information Technology involves a step-by-step process whereby various aspects of IT are evaluated. In addition to the book itself, a companion website offers templates, checklists, and related materials for your reference and use. With this book as your guide, you'll be able to generate an accurate and reliable assessment of a company's IT operations and identify areas on which to focus to optimize IT. Topics such as "against what to assess operations" and "optimized as compared to what" will be addressed throughout the course of this reliable resource. Introduces the concept of the IT Pillars Model (IPM) for optimizing and assessing IT and examines where and how the IPM fits into the overall operations of a business Filled with the author's experience of working across the field of IT in both small and large companies Offers the most detailed, hands-on user's guide to the principles and practice of the IPM by examining each aspect of the IPM in the context of case studies Covers the topic of tools and reporting, including analytical tools such as ROI, benchmarking, and metrics Optimizing and Assessing Information Technology provides valuable insights into this discipline, but the coverage of IT in this book extends beyond technology itself. It also covers various aspects of the people, processes, and technology components associated with IT as a whole.

Author: Patanjali Kashyap
Publisher: Apress
ISBN: 1484229886
Category : Computers
Languages : en
Pages : 381

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Book Description
Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other. This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making. The book uses case studies and jargon busting to help you grasp the theory of machine learning quickly. You'll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business. What You Will Learn Discover the machine learning, big data, and cloud and cognitive computing technology stack Gain insights into machine learning concepts and practices Understand business and enterprise decision-making using machine learning Absorb machine-learning best practices Who This Book Is For Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them.

Author: Jon McCormack
Publisher: Springer Science & Business Media
ISBN: 3642317278
Category : Computers
Languages : en
Pages : 441

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Book Description
This interdisciplinary volume introduces new theories and ideas on creativity from the perspectives of science and art. Featuring contributions from leading researchers, theorists and artists working in artificial intelligence, generative art, creative computing, music composition, and cybernetics, the book examines the relationship between computation and creativity from both analytic and practical perspectives. Each contributor describes innovative new ways creativity can be understood through, and inspired by, computers. The book tackles critical philosophical questions and discusses the major issues raised by computational creativity, including: whether a computer can exhibit creativity independently of its creator; what kinds of creativity are possible in light of our knowledge from computational simulation, artificial intelligence, evolutionary theory and information theory; and whether we can begin to automate the evaluation of aesthetics and creativity in silico. These important, often controversial questions are contextualised by current thinking in computational creative arts practice. Leading artistic practitioners discuss their approaches to working creatively with computational systems in a diverse array of media, including music, sound art, visual art, and interactivity. The volume also includes a comprehensive review of computational aesthetic evaluation and judgement research, alongside discussion and insights from pioneering artists working with computation as a creative medium over the last fifty years. A distinguishing feature of this volume is that it explains and grounds new theoretical ideas on creativity through practical applications and creative practice. Computers and Creativity will appeal to theorists, researchers in artificial intelligence, generative and evolutionary computing, practicing artists and musicians, students and any reader generally interested in understanding how computers can impact upon creativity. It bridges concepts from computer science, psychology, neuroscience, visual art, music and philosophy in an accessible way, illustrating how computers are fundamentally changing what we can imagine and create, and how we might shape the creativity of the future. Computers and Creativity will appeal to theorists, researchers in artificial intelligence, generative and evolutionary computing, practicing artists and musicians, students and any reader generally interested in understanding how computers can impact upon creativity. It bridges concepts from computer science, psychology, neuroscience, visual art, music and philosophy in an accessible way, illustrating how computers are fundamentally changing what we can imagine and create, and how we might shape the creativity of the future.

Author: Rado Kotorov
Publisher: Business Expert Press
ISBN: 195152781X
Category : Business & Economics
Languages : en
Pages : 161

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Book Description
Today the fastest growing companies have no physical assets. Instead, they create innovative digital products and new data-driven business models. They capture huge market share fast and their capitalizations skyrocket. The success of these digital giants is pushing all companies to rethink their business models and to start digitizing their products and services. Whether you are a new start-up building a digital product or service, or an employee of an established company that is transitioning to digital, you need to consider how digitization has transformed every aspect of management. Data-driven business models scale not through asset accumulation and product standardization, but through disaggregation of supply and demand. The winners in the new economy master the demand for one and the supply to millions. Throughout the book the author illustrates with examples and use cases how the market competition has changed and how companies adept to the new rules of the game. The economic levers of scale and scope are also different in the digital economy and companies have to learn new tactics how to achieve and sustain their competitive advantage. While data is at the core of all digital business models, the monetization strategies vary across products, services and business models. Our Monetization Matrix is a model that helps managers, marketers, sales professionals, and technical product designers to align the digital product design with the data-driven business model.

Author: Martyn A. Ould
Publisher: John Wiley & Sons
ISBN:
Category : Business & Economics
Languages : en
Pages : 232

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Book Description
After carefully establishing the objectives of modelling, the author presents a process modelling method, STRIM, and notations which has been developed by Praxis during the last few years. Ould provides detailed descriptions of the notations and the modelling technique along with examples of its use for a variety of purposes. Covers the full method--from organising a modelling project through process analysis to process support system development. can be used by practitioners who have no prior knowledge of the area.

Author: Ahmed Sherif
Publisher: Packt Publishing Ltd
ISBN: 1785889974
Category : Computers
Languages : en
Pages : 345

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Book Description
Learn to get the most out of your business data to optimize your business About This Book This book will enable and empower you to break free of the shackles of spreadsheets Learn to make informed decisions using the data at hand with this highly practical, comprehensive guide This book includes real-world use cases that teach you how analytics can be put to work to optimize your business Using a fictional transactional dataset in raw form, you'll work your way up to ultimately creating a fully-functional warehouse and a fleshed-out BI platform Who This Book Is For This book is for anyone who has wrangled with data to try to perform automated data analysis through visualizations for themselves or their customers. This highly-customized guide is for developers who know a bit about analytics but don't know how to make use of it in the field of business intelligence. What You Will Learn Create a BI environment that enables self-service reporting Understand SQL and the aggregation of data Develop a data model suitable for analytical reporting Connect a data warehouse to the analytic reporting tools Understand the specific benefits behind visualizations with D3.js, R, Tableau, QlikView, and Python Get to know the best practices to develop various reports and applications when using BI tools Explore the field of data analysis with all the data we will use for reporting In Detail Business Intelligence (BI) is at the crux of revolutionizing enterprise. Everyone wants to minimize losses and maximize profits. Thanks to Big Data and improved methodologies to analyze data, Data Analysts and Data Scientists are increasingly using data to make informed decisions. Just knowing how to analyze data is not enough, you need to start thinking how to use data as a business asset and then perform the right analysis to build an insightful BI solution. Efficient BI strives to achieve the automation of data for ease of reporting and analysis. Through this book, you will develop the ability to think along the right lines and use more than one tool to perform analysis depending on the needs of your business. We start off by preparing you for data analytics. We then move on to teach you a range of techniques to fetch important information from various databases, which can be used to optimize your business. The book aims to provide a full end-to-end solution for an environment setup that can help you make informed business decisions and deliver efficient and automated BI solutions to any company. It is a complete guide for implementing Business intelligence with the help of the most powerful tools like D3.js, R, Tableau, Qlikview and Python that are available on the market. Style and approach Packed with real-world examples, this pragmatic guide helps you polish your data and make informed decisions for your business. We cover both business and data analysis perspectives, blending theory and practical hands-on work so that you perceive data as a business asset.

Author: Patrick H. Garrett
Publisher: CRC Press
ISBN: 1351836439
Category : Technology & Engineering
Languages : en
Pages : 155

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Book Description
Improvements in process control, such as defined-accuracy instrumentation structures and computationally intelligent process modeling, enable advanced capabilities such as molecular manufacturing. High Performance Instrumentation and Automation demonstrates how systematizing the design of instrumentation and automation leads to higher performance through more homogeneous systems, which are frequently assisted by rule-based, fuzzy logic, and neural network process descriptions. Incorporate Advanced Performance Enhancements into Your Automation Enterprise The book illustrates generic common core process-to-control concurrent engineering linkages applied to a variety of laboratory and industry automation systems. It outlines: Product properties translated into realizable process variables Axiomatic decoupling of subprocess variables for improved robustness Production planner model-driven goal state execution In situ sensor and control structures for attenuating process disorder Apparatus tolerance design for minimizing process variabilities Production planner remodeling based on product features measurement for quality advancement Coverage also includes multisensor data fusion, high-performance computer I/O design guided by comprehensive error modeling, multiple sensor algorithmic error propagation, robotic axes volumetric accuracy, quantitative video digitization and reconstruction evaluation, and in situ process measurement methods. High Performance Instrumentation and Automation reflects the experience of engineer and author Patrick Garrett, including his role as co-principal investigator for an Air Force intelligent manufacturing initiative. You can download Analysis Suite.xls,, computer-aided design instrumentation software, available in the book's description on the CRC Press website.