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: McGraw Hill Professional

Published: 2019-08-23

Total Pages: 384

ISBN-13: 1260452786

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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.

Machine Learning for Decision Makers
Machine Learning for Decision Makers

Author: Patanjali Kashyap

Publisher: Apress

Published: 2018-01-04

Total Pages: 381

ISBN-13: 1484229886

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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.

Business Case Analysis with R
Business Case Analysis with R

Author: Robert D. Brown III

Publisher: Apress

Published: 2018-03-01

Total Pages: 287

ISBN-13: 1484234952

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This tutorial teaches you how to use the statistical programming language R to develop a business case simulation and analysis. It presents a methodology for conducting business case analysis that minimizes decision delay by focusing stakeholders on what matters most and suggests pathways for minimizing the risk in strategic and capital allocation decisions. Business case analysis, often conducted in spreadsheets, exposes decision makers to additional risks that arise just from the use of the spreadsheet environment. R has become one of the most widely used tools for reproducible quantitative analysis, and analysts fluent in this language are in high demand. The R language, traditionally used for statistical analysis, provides a more explicit, flexible, and extensible environment than spreadsheets for conducting business case analysis. The main tutorial follows the case in which a chemical manufacturing company considers constructing a chemical reactor and production facility to bring a new compound to market. There are numerous uncertainties and risks involved, including the possibility that a competitor brings a similar product online. The company must determine the value of making the decision to move forward and where they might prioritize their attention to make a more informed and robust decision. While the example used is a chemical company, the analysis structure it presents can be applied to just about any business decision, from IT projects to new product development to commercial real estate. The supporting tutorials include the perspective of the founder of a professional service firm who wants to grow his business and a member of a strategic planning group in a biomedical device company who wants to know how much to budget in order to refine the quality of information about critical uncertainties that might affect the value of a chosen product development pathway. What You’ll Learn Set up a business case abstraction in an influence diagram to communicate the essence of the problem to other stakeholders Model the inherent uncertainties in the problem with Monte Carlo simulation using the R language Communicate the results graphically Draw appropriate insights from the results Develop creative decision strategies for thorough opportunity cost analysis Calculate the value of information on critical uncertainties between competing decision strategies to set the budget for deeper data analysis Construct appropriate information to satisfy the parameters for the Monte Carlo simulation when little or no empirical data are available Who This Book Is For Financial analysts, data practitioners, and risk/business professionals; also appropriate for graduate level finance, business, or data science students

Developing Analytic Talent
Developing Analytic Talent

Author: Vincent Granville

Publisher: John Wiley & Sons

Published: 2014-03-24

Total Pages: 336

ISBN-13: 1118810090

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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.

Data Science for Business and Decision Making
Data Science for Business and Decision Making

Author: Luiz Paulo Fávero

Publisher: Academic Press

Published: 2019-04-11

Total Pages: 1240

ISBN-13: 0128112174

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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

Concurrent Engineering
Concurrent Engineering

Author: Andrew Kusiak

Publisher: John Wiley & Sons

Published: 1993-01-12

Total Pages: 614

ISBN-13: 9780471554929

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Presents a top-down approach to the design, development, testing and recyclability of products, components and systems across a wide range of industries. Starting with the desired result and working back through the details, it shows how to produce goods, taking into account the challenges of actual manufacture, what the reliability requirements should be, quality control, associated costs, customer needs and more. Additional features include case studies and team negotiating. Also well-illustrated with figures, photographs, charts and tables and includes an extensive bibliography.

Econometrics and Data Science
Econometrics and Data Science

Author: Tshepo Chris Nokeri

Publisher: Apress

Published: 2021-10-27

Total Pages: 228

ISBN-13: 9781484274330

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Get up to speed on the application of machine learning approaches in macroeconomic research. This book brings together economics and data science. Author Tshepo Chris Nokeri begins by introducing you to covariance analysis, correlation analysis, cross-validation, hyperparameter optimization, regression analysis, and residual analysis. In addition, he presents an approach to contend with multi-collinearity. He then debunks a time series model recognized as the additive model. He reveals a technique for binarizing an economic feature to perform classification analysis using logistic regression. He brings in the Hidden Markov Model, used to discover hidden patterns and growth in the world economy. The author demonstrates unsupervised machine learning techniques such as principal component analysis and cluster analysis. Key deep learning concepts and ways of structuring artificial neural networks are explored along with training them and assessing their performance. The Monte Carlo simulation technique is applied to stimulate the purchasing power of money in an economy. Lastly, the Structural Equation Model (SEM) is considered to integrate correlation analysis, factor analysis, multivariate analysis, causal analysis, and path analysis. After reading this book, you should be able to recognize the connection between econometrics and data science. You will know how to apply a machine learning approach to modeling complex economic problems and others beyond this book. You will know how to circumvent and enhance model performance, together with the practical implications of a machine learning approach in econometrics, and you will be able to deal with pressing economic problems. What You Will Learn Examine complex, multivariate, linear-causal structures through the path and structural analysis technique, including non-linearity and hidden states Be familiar with practical applications of machine learning and deep learning in econometrics Understand theoretical framework and hypothesis development, and techniques for selecting appropriate models Develop, test, validate, and improve key supervised (i.e., regression and classification) and unsupervised (i.e., dimension reduction and cluster analysis) machine learning models, alongside neural networks, Markov, and SEM models Represent and interpret data and models Who This Book Is For Beginning and intermediate data scientists, economists, machine learning engineers, statisticians, and business executives

Information Technology and Business Process Reengineering
Information Technology and Business Process Reengineering

Author: Hui Liang Tsai

Publisher: Praeger

Published: 2003-03-30

Total Pages: 0

ISBN-13: 9781567206326

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Information technology matters. The formulation of business models and strategies is likely to be seriously flawed if it neglects new technological changes and repercussions. Information technology and organizational learning are both critical to attaining and sustaining competitive advantage. Based on these premises, and on the increasingly evident obsolescence of the traditional, hierarchical business model, the author takes an integrated approach to the discussion of organizational learning, new value propositions, supply-chain optimization, e-commerce, new perspectives on business process reengineering, collaborative business models, globalization of business, and the continuing trend toward virtual integration. This book is not another tired exhortation to take advantage of technology to improve corporate performance. One cannot utilize technology to its full potential without also overhauling the rigid, hierarchical business model that was designed for the command-and-control corporate environment of the industrial era. Tsai takes a comprehensive view of everything that needs to be done in order to make the most of emerging technologies. The new information economy calls for companies that foster strategic collaboration through a more horizontal organizational structure. A more synchronized supply chain yields better customer service, higher quality, faster delivery, and lower inventory. In the customer-centric environment, a new value proposition is essential. Business processes must be fast, focused, flexible, responsive, and nimble. With the knowledge gained from this book, one should be able to face the future with an ability and capacity to discern technology policies and make decisions concerning strategic positioning and competitive business strategy.

Learning Analytics
Learning Analytics

Author: Cristina Hall

Publisher:

Published: 2020

Total Pages: 0

ISBN-13: 9781789663006

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Utilize organizational data and analytics to make better decisions about managing the learning and development of your workforce.

Data Science For Business
Data Science For Business

Author: Dr Daniel James

Publisher:

Published: 2021-06-15

Total Pages: 106

ISBN-13:

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Fundаmеntаl соnсерtѕ A ѕеt оf саnоnісаl data mining tаѕkѕ; The dаtа mining рrосеѕѕ; Supervised vеrѕuѕ unsupervised dаtа mining. An іmроrtаnt рrіnсірlе оf dаtа ѕсіеnсе is that data mining іѕ a рrосеѕѕ wіth fairly wеllundеrѕtооd ѕtаgеѕ. Some іnvоlvе the application оf information tесhnоlоgу, ѕuсh аѕ thе аutоmаtеd discovery and еvаluаtіоn of раttеrnѕ frоm dаtа, whіlе others mostly require аn аnаlуѕt'ѕ сrеаtіvіtу, buѕіnеѕѕ knowledge, and соmmоn sense. Undеrѕtаndіng the whole рrосеѕѕ hеlрѕ tо structure dаtа mіnіng рrоjесtѕ, ѕо thеу are closer tо ѕуѕtеmаtіс analyses rаthеr thаn heroic еndеаvоrѕ driven bу chance аnd individual асumеn. Sіnсе thе dаtа mining рrосеѕѕ breaks up thе оvеrаll tаѕk оf fіndіng раttеrnѕ frоm dаtа іntо a ѕеt of wеll-dеfіnеd subtasks, it is аlѕо uѕеful fоr ѕtruсturіng discussions about dаtа ѕсіеnсе. In this bооk, we wіll uѕе the process аѕ аn оvеrаrсhіng frаmеwоrk fоr оur dіѕсuѕѕіоn. Thіѕ chapter іntrоduсеѕ thе data mіnіng process, but fіrѕt we рrоvіdе аddіtіоnаl соntеxt by dіѕсuѕѕіng common tуреѕ оf dаtа mining tasks. Introducing thеѕе аllоwѕ us to bе mоrе соnсrеtе when рrеѕеntіng the overall рrосеѕѕ, аѕ well аѕ whеn introducing other соnсерtѕ іn subsequent сhарtеrѕ. Fundаmеntаl соnсерtѕ A ѕеt оf саnоnісаl data mining tаѕkѕ; The dаtа mining рrосеѕѕ; Supervised vеrѕuѕ unsupervised dаtа mining. An іmроrtаnt рrіnсірlе оf dаtа ѕсіеnсе is that data mining іѕ a рrосеѕѕ wіth fairly wеllundеrѕtооd ѕtаgеѕ. Some іnvоlvе the application оf information tесhnоlоgу, ѕuсh аѕ thе аutоmаtеd discovery and еvаluаtіоn of раttеrnѕ frоm dаtа, whіlе others mostly require аn аnаlуѕt'ѕ сrеаtіvіtу, buѕіnеѕѕ knowledge, and соmmоn sense. Undеrѕtаndіng the whole рrосеѕѕ hеlрѕ tо structure dаtа mіnіng рrоjесtѕ, ѕо thеу are closer tо ѕуѕtеmаtіс analyses rаthеr thаn heroic еndеаvоrѕ driven bу chance аnd individual асumеn. Sіnсе thе dаtа mining рrосеѕѕ breaks up thе оvеrаll tаѕk оf fіndіng раttеrnѕ frоm dаtа іntо a ѕеt of wеll-dеfіnеd subtasks, it is аlѕо uѕеful fоr ѕtruсturіng discussions about dаtа ѕсіеnсе. In this bооk, we wіll uѕе the process аѕ аn оvеrаrсhіng frаmеwоrk fоr оur dіѕсuѕѕіоn. Thіѕ chapter іntrоduсеѕ thе data mіnіng process, but fіrѕt we рrоvіdе аddіtіоnаl соntеxt by dіѕсuѕѕіng common tуреѕ оf dаtа mining tasks. Introducing thеѕе аllоwѕ us to bе mоrе соnсrеtе when рrеѕеntіng the overall рrосеѕѕ, аѕ well аѕ whеn introducing other соnсерtѕ іn subsequent сhарtеrѕ.