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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
Total Pages: 384
Release: 2019-08-23
Genre: Business & Economics
ISBN: 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.

Developing Analytic Talent

Developing Analytic Talent
Author: Vincent Granville
Publisher: John Wiley & Sons
Total Pages: 336
Release: 2014-03-24
Genre: Computers
ISBN: 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.

Performance Evaluation and Benchmarking of Intelligent Systems

Performance Evaluation and Benchmarking of Intelligent Systems
Author: Raj Madhavan
Publisher: Springer Science & Business Media
Total Pages: 351
Release: 2009-09-18
Genre: Computers
ISBN: 1441904921
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To design and develop capable, dependable, and affordable intelligent systems, their performance must be measurable. Scienti?c methodologies for standardization and benchmarking are crucial for quantitatively evaluating the performance of eme- ing robotic and intelligent systems’ technologies. There is currently no accepted standard for quantitatively measuring the performance of these systems against user-de?ned requirements; and furthermore, there is no consensus on what obj- tive evaluation procedures need to be followed to understand the performance of these systems. The lack of reproducible and repeatable test methods has precluded researchers working towards a common goal from exchanging and communic- ing results, inter-comparing system performance, and leveraging previous work that could otherwise avoid duplication and expedite technology transfer. Currently, this lack of cohesion in the community hinders progress in many domains, such as m- ufacturing, service, healthcare, and security. By providing the research community with access to standardized tools, reference data sets, and open source libraries of solutions, researchers and consumers will be able to evaluate the cost and be- ?ts associated with intelligent systems and associated technologies. In this vein, the edited book volume addresses performance evaluation and metrics for intel- gent systems, in general, while emphasizing the need and solutions for standardized methods. To the knowledge of the editors, there is not a single book on the market that is solely dedicated to the subject of performance evaluation and benchmarking of intelligent systems.

Data Science for Business and Decision Making

Data Science for Business and Decision Making
Author: Luiz Paulo Fávero
Publisher: Academic Press
Total Pages: 1240
Release: 2019-04-11
Genre: Business & Economics
ISBN: 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

Digital Talent Management

Digital Talent Management
Author: Sorin Dan
Publisher: Springer Nature
Total Pages: 88
Release: 2021-07-23
Genre: Business & Economics
ISBN: 3030767507
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This book highlights the importance of talent management practices in recruiting, developing and retaining talented professionals in the digital and IT&C industry. It unpacks the distinctive characteristics of ‘digital talent’ represented by a wide spectrum of professionals and managers with digital abilities, competencies and skills who add considerable value to organizations and industries worldwide. It shows that despite digital talent’s increased variety and significant contribution to digital transformation processes, much of the existing human resource and talent management research and practice fail to account for their distinctiveness. This book calls for the need for a new kind of talent management, referred to as ‘digital talent management’ (DTM) that is applicable to digital talent and decidedly integrates digital talent’s distinctive characteristics into talent management strategies and practices in a human-centered manner. Drawing upon existing, yet disconnected, streams of literature and empirical evidence derived from the information technology and communication (IT&C) industry, this book defines digital talent and delineates strategies to attract, develop and retain them for an uncertain and renewed future.

Workspace Strategies

Workspace Strategies
Author: Jacqueline C. Vischer
Publisher: Springer Science & Business Media
Total Pages: 253
Release: 2012-12-06
Genre: Science
ISBN: 1468477846
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We live in era of transformation--of technology, of social values, and of the way work is done. This book represents a timely and innovative ad dition to current thinking and writing about transformation in organiza tions. In order to meet an increasingly global and competitive environment, organizations are undergoing reengineering, work process redesign, "right sizing," creating a "virtual office," and other forms of restructur ing and basic change of the way work is accomplished. Such transfor mation means analyzing and redesigning core processes in organizations around new kinds of principles such as "total quality" and customer service. The eventual effect of these changes is likely to be the networked or "boundary-Iess" organization, in which the tradi tional boundaries between functions and between producers and their suppliers-and sometimes even between organizations and their com petitors-are broken down. The goal of such transformation is to make the work of the organization more efficient and productive-to produce more with fewer resources and at a lower cost. In the conventional view of the transformation process, certain sec ondary concerns, such as the need to protect the environment or to help an increasingly heterogeneous work force deal with its personal issues, are seen as problematic for this core thrust. Some recent work, however, is beginning to show that if these so-called secondary concerns are con sidered central, far from being problematic, they actually present strat egy opportunities for productive innovation and change.

Concurrent Engineering

Concurrent Engineering
Author: Andrew Kusiak
Publisher: John Wiley & Sons
Total Pages: 614
Release: 1993-01-12
Genre: Business & Economics
ISBN: 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.

Machine Learning in Business

Machine Learning in Business
Author: JOHN. HULL C
Publisher:
Total Pages: 0
Release: 2021
Genre: Big data
ISBN:
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"The big data revolution is changing the way businesses operate and the skills required by managers. In creating the third edition, John Hull has continued to improve his material and added many new examples. The book explains the most popular machine learning algorithms clearly and succinctly; provides many examples of applications of machine learning in business; provides the knowledge managers need to work productively with data science professionals; has an accompanying website with data, worksheets, and Python code"--Back of cover.

Artificial Intelligence

Artificial Intelligence
Author: Harvard Business Review
Publisher: HBR Insights
Total Pages: 160
Release: 2019
Genre: Business & Economics
ISBN: 9781633697898
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Companies that don't use AI to their advantage will soon be left behind. Artificial intelligence and machine learning will drive a massive reshaping of the economy and society. What should you and your company be doing right now to ensure that your business is poised for success? These articles by AI experts and consultants will help you understand today's essential thinking on what AI is capable of now, how to adopt it in your organization, and how the technology is likely to evolve in the near future. Artificial Intelligence: The Insights You Need from Harvard Business Review will help you spearhead important conversations, get going on the right AI initiatives for your company, and capitalize on the opportunity of the machine intelligence revolution. Catch up on current topics and deepen your understanding of them with the Insights You Need series from Harvard Business Review. Featuring some of HBR's best and most recent thinking, Insights You Need titles are both a primer on today's most pressing issues and an extension of the conversation, with interesting research, interviews, case studies, and practical ideas to help you explore how a particular issue will impact your company and what it will mean for you and your business.

Data Science For Business

Data Science For Business
Author: Dr Daniel James
Publisher:
Total Pages: 106
Release: 2021-06-15
Genre:
ISBN:
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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.