Data Science for Business and Decision Making Book
Score: 5
From 2 Ratings

Data Science for Business and Decision Making


  • Author : Luiz Paulo Fávero
  • Publisher : Academic Press
  • Release Date : 2019-03-08
  • Genre: Business & Economics
  • Pages : 1000
  • ISBN 10 : 0128112166

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Data Science for Business and Decision Making Excerpt :

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 and Decision Making Book

Data Science for Business and Decision Making


  • Author : Luiz Paulo Fávero
  • Publisher : Academic Press
  • Release Date : 2019-04-11
  • Genre: Business & Economics
  • Pages : 1240
  • ISBN 10 : 9780128112175

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Data Science for Business and Decision Making Excerpt :

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 Book
Score: 3.5
From 8 Ratings

Data Science for Business


  • Author : Foster Provost
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2013-07-27
  • Genre: Computers
  • Pages : 414
  • ISBN 10 : 9781449374280

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Data Science for Business Excerpt :

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

Business Analytics for Decision Making Book

Business Analytics for Decision Making


  • Author : Steven Orla Kimbrough
  • Publisher : CRC Press
  • Release Date : 2016-07-31
  • Genre: Business & Economics
  • Pages : 330
  • ISBN 10 : 9781315360249

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Business Analytics for Decision Making Excerpt :

Business Analytics for Decision Making, the first complete text suitable for use in introductory Business Analytics courses, establishes a national syllabus for an emerging first course at an MBA or upper undergraduate level. This timely text is mainly about model analytics, particularly analytics for constrained optimization. It uses implementations that allow students to explore models and data for the sake of discovery, understanding, and decision making. Business analytics is about using data and models to solve various kinds of decision problems. There are three aspects for those who want to make the most of their analytics: encoding, solution design, and post-solution analysis. This textbook addresses all three. Emphasizing the use of constrained optimization models for decision making, the book concentrates on post-solution analysis of models. The text focuses on computationally challenging problems that commonly arise in business environments. Unique among business analytics texts, it emphasizes using heuristics for solving difficult optimization problems important in business practice by making best use of methods from Computer Science and Operations Research. Furthermore, case studies and examples illustrate the real-world applications of these methods. The authors supply examples in Excel®, GAMS, MATLAB®, and OPL. The metaheuristics code is also made available at the book's website in a documented library of Python modules, along with data and material for homework exercises. From the beginning, the authors emphasize analytics and de-emphasize representation and encoding so students will have plenty to sink their teeth into regardless of their computer programming experience.

Customer and Business Analytics Book

Customer and Business Analytics


  • Author : Daniel S. Putler
  • Publisher : CRC Press
  • Release Date : 2015-09-15
  • Genre: Business & Economics
  • Pages : 315
  • ISBN 10 : 9781498759700

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Customer and Business Analytics Excerpt :

Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R explains and demonstrates, via the accompanying open-source software, how advanced analytical tools can address various business problems. It also gives insight into some of the challenges faced when deploying these tools. Extensively classroom-tested, the text is ideal for students in customer and business analytics or applied data mining as well as professionals in small- to medium-sized organizations. The book offers an intuitive understanding of how different analytics algorithms work. Where necessary, the authors explain the underlying mathematics in an accessible manner. Each technique presented includes a detailed tutorial that enables hands-on experience with real data. The authors also discuss issues often encountered in applied data mining projects and present the CRISP-DM process model as a practical framework for organizing these projects. Showing how data mining can improve the performance of organizations, this book and its R-based software provide the skills and tools needed to successfully develop advanced analytics capabilities.

Business Analytics  Data Analysis   Decision Making Book

Business Analytics Data Analysis Decision Making


  • Author : S. Christian Albright
  • Publisher : Cengage Learning
  • Release Date : 2016-03-31
  • Genre: Business & Economics
  • Pages : 984
  • ISBN 10 : 9781337225274

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Business Analytics Data Analysis Decision Making Excerpt :

Master data analysis, modeling, and spreadsheet use with BUSINESS ANALYTICS: DATA ANALYSIS AND DECISION MAKING, 6E! Popular with students, instructors, and practitioners, this quantitative methods text delivers the tools to succeed with its proven teach-by-example approach, user-friendly writing style, and complete Excel 2016 integration. It is also compatible with Excel 2013, 2010, and 2007. Completely rewritten, Chapter 17, Data Mining, and Chapter 18, Importing Data into Excel, include increased emphasis on the tools commonly included under the Business Analytics umbrella -- including Microsoft Excel’s “Power BI” suite. In addition, up-to-date problem sets and cases provide realistic examples to show the relevance of the material. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Data Science and Multiple Criteria Decision Making Approaches in Finance Book

Data Science and Multiple Criteria Decision Making Approaches in Finance


  • Author : Gökhan Silahtaroğlu
  • Publisher : Springer Nature
  • Release Date : 2021-05-29
  • Genre: Business & Economics
  • Pages : 173
  • ISBN 10 : 9783030741761

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Data Science and Multiple Criteria Decision Making Approaches in Finance Excerpt :

This book considers and assesses essential financial issues by utilizing data science and fuzzy multiple criteria decision making (MCDM) methods. It introduces readers to a range of data science methods, and demonstrates their application in the fields of business, health, economics, finance and engineering. In addition, it provides suggestions based on the assessment results on each topic, which can help to enhance the efficiency of the financial system and the sustainability of economic development. Given its scope, the book will help readers broaden their perspective on the assessment and evaluation of financial issues using data science and MCDM approaches.

The Decision Maker s Handbook to Data Science Book

The Decision Maker s Handbook to Data Science


  • Author : Stylianos Kampakis
  • Publisher : Apress
  • Release Date : 2019-11-26
  • Genre: Computers
  • Pages : 156
  • ISBN 10 : 9781484254943

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The Decision Maker s Handbook to Data Science Excerpt :

Data science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. It is easy for novices to the subject to feel paralyzed by intimidating buzzwords, but what many don’t realize is that data science is in fact quite multidisciplinary—useful in the hands of business analysts, communications strategists, designers, and more. With the second edition of The Decision Maker’s Handbook to Data Science, you will learn how to think like a veteran data scientist and approach solutions to business problems in an entirely new way. Author Stylianos Kampakis provides you with the expertise and tools required to develop a solid data strategy that is continuously effective. Ethics and legal issues surrounding data collection and algorithmic bias are some common pitfalls that Kampakis helps you avoid, while guiding you on the path to build a thriving data science culture at your organization. This updated and revised second edition, includes plenty of case studies, tools for project assessment, and expanded content for hiring and managing data scientists Data science is a language that everyone at a modern company should understand across departments. Friction in communication arises most often when management does not connect with what a data scientist is doing or how impactful data collection and storage can be for their organization. The Decision Maker’s Handbook to Data Science bridges this gap and readies you for both the present and future of your workplace in this engaging, comprehensive guide. What You Will Learn Understand how data science can be used within your business. Recognize the differences between AI, machine learning, and statistics.Become skilled at thinking like a data scientist, without being one.Discover how to hire and manage data scientists.

A PRACTITIONER S GUIDE TO BUSINESS ANALYTICS  Using Data Analysis Tools to Improve Your Organization   s Decision Making and Strategy Book

A PRACTITIONER S GUIDE TO BUSINESS ANALYTICS Using Data Analysis Tools to Improve Your Organization s Decision Making and Strategy


  • Author : Randy Bartlett
  • Publisher : McGraw Hill Professional
  • Release Date : 2013-01-25
  • Genre: Business & Economics
  • Pages : 256
  • ISBN 10 : 9780071807609

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A PRACTITIONER S GUIDE TO BUSINESS ANALYTICS Using Data Analysis Tools to Improve Your Organization s Decision Making and Strategy Excerpt :

Gain the competitive edge with the smart use of business analytics In today’s volatile business environment, the strategic use of business analytics is more important than ever. A Practitioners Guide to Business Analytics helps you get the organizational commitment you need to get business analytics up and running in your company. It provides solutions for meeting the strategic challenges of applying analytics, such as: Integrating analytics into decision making, corporate culture, and business strategy Leading and organizing analytics within the corporation Applying statistical qualifications, statistical diagnostics, and statistical review Providing effective building blocks to support analytics—statistical software, data collection, and data management Randy Bartlett, Ph.D., is Chief Statistical Officer of the consulting company Blue Sigma Analytics. He currently works with Infosys, where he has helped build their new Business Analytics practice.

Getting Started with Business Analytics Book

Getting Started with Business Analytics


  • Author : David Roi Hardoon
  • Publisher : CRC Press
  • Release Date : 2013-03-26
  • Genre: Business & Economics
  • Pages : 190
  • ISBN 10 : 9781439896532

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Getting Started with Business Analytics Excerpt :

Assuming no prior knowledge or technical skills, Getting Started with Business Analytics: Insightful Decision-Making explores the contents, capabilities, and applications of business analytics. It bridges the worlds of business and statistics and describes business analytics from a non-commercial standpoint. The authors demystify the main concepts and terminologies and give many examples of real-world applications. The first part of the book introduces business data and recent technologies that have promoted fact-based decision-making. The authors look at how business intelligence differs from business analytics. They also discuss the main components of a business analytics application and the various requirements for integrating business with analytics. The second part presents the technologies underlying business analytics: data mining and data analytics. The book helps you understand the key concepts and ideas behind data mining and shows how data mining has expanded into data analytics when considering new types of data such as network and text data. The third part explores business analytics in depth, covering customer, social, and operational analytics. Each chapter in this part incorporates hands-on projects based on publicly available data. Helping you make sound decisions based on hard data, this self-contained guide provides an integrated framework for data mining in business analytics. It takes you on a journey through this data-rich world, showing you how to deploy business analytics solutions in your organization.

Management Decision Making  Big Data and Analytics Book

Management Decision Making Big Data and Analytics


  • Author : Simone Gressel
  • Publisher : SAGE
  • Release Date : 2020-10-12
  • Genre: Business & Economics
  • Pages : 336
  • ISBN 10 : 9781529738285

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Management Decision Making Big Data and Analytics Excerpt :

Accessible and concise, this exciting new textbook examines data analytics from a managerial and organizational perspective and looks at how they can help managers become more effective decision-makers. The book successfully combines theory with practical application, featuring case studies, examples and a ‘critical incidents’ feature that make these topics engaging and relevant for students of business and management. The book features chapters on cutting-edge topics, including: • Big data • Analytics • Managing emerging technologies and decision-making • Managing the ethics, security, privacy and legal aspects of data-driven decision-making The book is accompanied by an Instructor’s Manual, PowerPoint slides and access to journal articles. Suitable for management students studying business analytics and decision-making at undergraduate, postgraduate and MBA levels.

Business Data Science  Combining Machine Learning and Economics to Optimize  Automate  and Accelerate Business Decisions Book
Score: 5
From 1 Ratings

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


  • Author : Matt Taddy
  • Publisher : McGraw Hill Professional
  • Release Date : 2019-08-23
  • Genre: Business & Economics
  • Pages : 384
  • ISBN 10 : 9781260452785

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

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.

Business Intelligence Book

Business Intelligence


  • Author : Carlo Vercellis
  • Publisher : John Wiley & Sons
  • Release Date : 2011-08-10
  • Genre: Mathematics
  • Pages : 436
  • ISBN 10 : 9781119965473

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Business Intelligence Excerpt :

Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made. Business Intelligence provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence. This book: Combines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence. Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation. Is made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies. Explains how to utilise mathematical models and analysis models to make effective and good quality business decisions. This book is aimed at postgraduate students following data analysis and data mining courses. Researchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide.

Data Driven Decision Making using Analytics Book

Data Driven Decision Making using Analytics


  • Author : Parul Gandhi
  • Publisher : CRC Press
  • Release Date : 2021-12-21
  • Genre: Computers
  • Pages : 150
  • ISBN 10 : 9781000506433

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Data Driven Decision Making using Analytics Excerpt :

This book aims to explain Data Analytics towards decision making in terms of models and algorithms, theoretical concepts, applications, experiments in relevant domains or focused on specific issues. It explores the concepts of database technology, machine learning, knowledge-based system, high performance computing, information retrieval, finding patterns hidden in large datasets and data visualization. Also, it presents various paradigms including pattern mining, clustering, classification, and data analysis. Overall aim is to provide technical solutions in the field of data analytics and data mining. Features: Covers descriptive statistics with respect to predictive analytics and business analytics. Discusses different data analytics platforms for real-time applications. Explain SMART business models. Includes algorithms in data sciences alongwith automated methods and models. Explores varied challenges encountered by researchers and businesses in the realm of real-time analytics. This book aims at researchers and graduate students in data analytics, data sciences, data mining, and signal processing.

Introduction to Business Analytics Book

Introduction to Business Analytics


  • Author : Steven Orla Kimbrough
  • Publisher : Chapman and Hall/CRC
  • Release Date : 2016-01-15
  • Genre: Business & Economics
  • Pages : 650
  • ISBN 10 : 1482221764

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Introduction to Business Analytics Excerpt :

If you want to solve a problem, strip the problem of nonessentials, simplify, and specialize without sacrificing its core. This book highlights this spirit using concrete, specific, simple examples pertaining to business analytics. Offering examples in thorough detail and designed to illuminate topics that often ramify to great complexity in practice, it associates concepts through generalizations and refers the interested to further sources. This book establishes a national syllabus for an emerging first course at an MBA level in Business Analytics.