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Author: Tshepo Chris Nokeri
Publisher: Apress
ISBN: 9781484268698
Category : Computers
Languages : en
Pages : 252
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Book Description
Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning. This book teaches you how to select variables, optimize hyper parameters, develop pipelines, and train, test, and validate machine and deep learning models. Each chapter includes a set of examples allowing you to understand the concepts, assumptions, and procedures behind each model. The book covers parametric methods or linear models that combat under- or over-fitting using techniques such as Lasso and Ridge. It includes complex regression analysis with time series smoothing, decomposition, and forecasting. It takes a fresh look at non-parametric models for binary classification (logistic regression analysis) and ensemble methods such as decision trees, support vector machines, and naive Bayes. It covers the most popular non-parametric method for time-event data (the Kaplan-Meier estimator). It also covers ways of solving classification problems using artificial neural networks such as restricted Boltzmann machines, multi-layer perceptrons, and deep belief networks. The book discusses unsupervised learning clustering techniques such as the K-means method, agglomerative and Dbscan approaches, and dimension reduction techniques such as Feature Importance, Principal Component Analysis, and Linear Discriminant Analysis. And it introduces driverless artificial intelligence using H2O. After reading this book, you will be able to develop, test, validate, and optimize statistical machine learning and deep learning models, and engineer, visualize, and interpret sets of data. What You Will Learn Design, develop, train, and validate machine learning and deep learning models Find optimal hyper parameters for superior model performance Improve model performance using techniques such as dimension reduction and regularization Extract meaningful insights for decision making using data visualization Who This Book Is For Beginning and intermediate level data scientists and machine learning engineers
Author: Tshepo Chris Nokeri
Publisher: Apress
ISBN: 9781484268698
Category : Computers
Languages : en
Pages : 252
Get Book
Book Description
Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning. This book teaches you how to select variables, optimize hyper parameters, develop pipelines, and train, test, and validate machine and deep learning models. Each chapter includes a set of examples allowing you to understand the concepts, assumptions, and procedures behind each model. The book covers parametric methods or linear models that combat under- or over-fitting using techniques such as Lasso and Ridge. It includes complex regression analysis with time series smoothing, decomposition, and forecasting. It takes a fresh look at non-parametric models for binary classification (logistic regression analysis) and ensemble methods such as decision trees, support vector machines, and naive Bayes. It covers the most popular non-parametric method for time-event data (the Kaplan-Meier estimator). It also covers ways of solving classification problems using artificial neural networks such as restricted Boltzmann machines, multi-layer perceptrons, and deep belief networks. The book discusses unsupervised learning clustering techniques such as the K-means method, agglomerative and Dbscan approaches, and dimension reduction techniques such as Feature Importance, Principal Component Analysis, and Linear Discriminant Analysis. And it introduces driverless artificial intelligence using H2O. After reading this book, you will be able to develop, test, validate, and optimize statistical machine learning and deep learning models, and engineer, visualize, and interpret sets of data. What You Will Learn Design, develop, train, and validate machine learning and deep learning models Find optimal hyper parameters for superior model performance Improve model performance using techniques such as dimension reduction and regularization Extract meaningful insights for decision making using data visualization Who This Book Is For Beginning and intermediate level data scientists and machine learning engineers
Author: Sara Shirowzhan
Publisher: BoD – Books on Demand
ISBN: 1839629436
Category : Computers
Languages : en
Pages : 118
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Book Description
Real-time, web-based, and interactive visualisations are proven to be outstanding methodologies and tools in numerous fields when knowledge in sophisticated data science and visualisation techniques is available. The rationale for this is because modern data science analytical approaches like machine/deep learning or artificial intelligence, as well as digital twinning, promise to give data insights, enable informed decision-making, and facilitate rich interactions among stakeholders.The benefits of data visualisation, data science, and digital twinning technologies motivate this book, which exhibits and presents numerous developed and advanced data science and visualisation approaches. Chapters cover such topics as deep learning techniques, web and dashboard-based visualisations during the COVID pandemic, 3D modelling of trees for mobile communications, digital twinning in the mining industry, data science libraries, and potential areas of future data science development.
Author: Longbing Cao
Publisher: Springer
ISBN: 3319950924
Category : Computers
Languages : en
Pages : 390
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Book Description
This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective.
Author: Wang, John
Publisher: IGI Global
ISBN: 1799892212
Category : Computers
Languages : en
Pages : 3296
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Book Description
Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.
Author: Murtaza Haider
Publisher: IBM Press
ISBN: 0133991237
Category : Business & Economics
Languages : en
Pages : 942
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Book Description
Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon.
Author: Scott Mongeau
Publisher: Springer Nature
ISBN: 3030748960
Category : Computers
Languages : en
Pages : 410
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Book Description
This book encompasses a systematic exploration of Cybersecurity Data Science (CSDS) as an emerging profession, focusing on current versus idealized practice. This book also analyzes challenges facing the emerging CSDS profession, diagnoses key gaps, and prescribes treatments to facilitate advancement. Grounded in the management of information systems (MIS) discipline, insights derive from literature analysis and interviews with 50 global CSDS practitioners. CSDS as a diagnostic process grounded in the scientific method is emphasized throughout Cybersecurity Data Science (CSDS) is a rapidly evolving discipline which applies data science methods to cybersecurity challenges. CSDS reflects the rising interest in applying data-focused statistical, analytical, and machine learning-driven methods to address growing security gaps. This book offers a systematic assessment of the developing domain. Advocacy is provided to strengthen professional rigor and best practices in the emerging CSDS profession. This book will be of interest to a range of professionals associated with cybersecurity and data science, spanning practitioner, commercial, public sector, and academic domains. Best practices framed will be of interest to CSDS practitioners, security professionals, risk management stewards, and institutional stakeholders. Organizational and industry perspectives will be of interest to cybersecurity analysts, managers, planners, strategists, and regulators. Research professionals and academics are presented with a systematic analysis of the CSDS field, including an overview of the state of the art, a structured evaluation of key challenges, recommended best practices, and an extensive bibliography.
Author: Steven S. Skiena
Publisher: Springer
ISBN: 3319554441
Category : Computers
Languages : en
Pages : 445
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Book Description
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)
Author: William R. Hersh
Publisher: Lulu.com
ISBN: 1387642413
Category : Science
Languages : en
Pages : 488
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Book Description
Health informatics is the discipline concerned with the management of healthcare data and information through the application of computers and other information technologies. The field focuses more on identifying and applying information in the healthcare field and less on the technology involved. Our goal is to stimulate and educate healthcare and IT professionals and students about the key topics in this rapidly changing field. This seventh edition reflects the current knowledge in the topics listed below and provides learning objectives, key points, case studies and extensive references. Available as a paperback and eBook. Visit the textbook companion website at http://informaticseducation.org for more information.--Page 4 de la couverture.
Author: Ann K. Yoshihashi
Publisher: Lulu.com
ISBN: 1365524809
Category : Science
Languages : en
Pages : 114
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Book Description
Health Informatics: Practical Guide for Health and Information Technology Professionals Sixth Edition Supplement adds 3 new chapters. The supplement has learning objectives, case studies, recommended reading, future trends, key points, and references. Introduction to Data Science, provides a comprehensive overview with topics including databases, machine learning, big data and predictive analytics. Clinical Decision Support (CDS), covers current and salient aspects of CDS functionality, implementation, benefits, challenges and lessons learned. International Health Informatics, highlights the informatics initiatives of developed and developing countries on each continent. Available as a paperback and eBook. For more information about the textbook, visit www.informaticseducation.org. For instructors, an Instructor Manual, PDF version and PowerPoint slides are available under the Instructor's tab.
Author: Christopher Hian-ann Ting
Publisher: World Scientific
ISBN: 9811238324
Category : Business & Economics
Languages : en
Pages : 409
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Book Description
Why is data science a branch of science? Is data science just a catchy rebranding of statistics?Data science provides tools for statistical analysis and machine learning. But, as much as application problems without tools are lame, tools without application problems are vain. Through example after example, this book presents the algorithmic aspects of statistics and show how some of the tools are applied to answer questions of interest to finance.This book champions a fundamental principle of science — objective reproducibility of evidence independently by others. From a companion web site, readers can download many easy-to-understand Python programs and real-world data. Independently, readers can draw for themselves the figures in the book. Even so, readers are encouraged to run the statistical tests described as examples to verify their own results against what the book claims.This book covers some topics that are seldom discussed in other textbooks. They include the methods to adjust for dividend payment and stock splits, how to reproduce a stock market index such as Nikkei 225 index, and so on. By running the Python programs provided, readers can verify their results against the data published by free data resources such as Yahoo! finance. Though practical, this book provides detailed proofs of propositions such as why certain estimators are unbiased, how the ubiquitous normal distribution is derived from the first principles, and so on.This see-for-yourself textbook is essential to anyone who intends to learn the nuts and bots of data science, especially in the application domain of finance. Advanced readers may find the book helpful in its mathematical treatment. Practitioners may find some tips from the book on how an ETF is constructed, as well as some insights on a novel algorithmic framework for pair trading to generate statistical arbitrage.
