Data Science Building Blocks
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| Author | : Jyothsna Sravanthi, Malaya Rout, Radhakrishnan Guhan |
| Publisher | : Notion Press |
| Total Pages | : 242 |
| Release | : 2020-04-14 |
| Genre | : Computers |
| ISBN | : 1648287298 |
Data Science Building Blocks is a result of the authors’ many years of industry experience in data science and their various interactions with learners whose conversations also figure in this content. The book is aimed at familiarising aspirants and beginners with the basics of data science. These building blocks will help you build your analytics dream house. When it is done, don’t forget to invite us and share your success story over a cup of coffee.
| Author | : John Soldatos |
| Publisher | : River Publishers |
| Total Pages | : 294 |
| Release | : 2016-11-23 |
| Genre | : Technology & Engineering |
| ISBN | : 8793519036 |
Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analytics This book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI).
| Author | : Rebecca W. Keller |
| Publisher | : Real Science-4-Kids |
| Total Pages | : |
| Release | : 2014-03-01 |
| Genre | : |
| ISBN | : 9781936114290 |
Introduce early learners to real science with the Exploring the Building Blocks of Science Book 1 Student Textbook. Foundational scientific concepts and terminology are presented clearly and in a manner that's easy for kids to understand. Using this book gives kids a solid base on which to build a further study of science. This year-long curriculum contains four chapters of each of five scientific disciplines: chemistry, biology, physics, geology, and astronomy, as well as an introduction to the material covered and a concluding chapter for a total of 22 chapters. The many graphics in this full color textbook reinforce the concepts presented and make the book fun for kids and teachers alike to read. This Student Textbook is accompanied by Exploring the Building Blocks of Science Book 1 Laboratory Notebook (experiments) and Exploring the Building Blocks of Science Book 1 Teacher's Manual. Other supplemental materials are available at www.realscience4kids.com.
| Author | : Ryan A. Estrellado |
| Publisher | : Routledge |
| Total Pages | : 315 |
| Release | : 2020-10-26 |
| Genre | : Education |
| ISBN | : 1000200906 |
Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a "learn by doing" approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development.
| Author | : Chris Fregly |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 524 |
| Release | : 2021-04-07 |
| Genre | : Computers |
| ISBN | : 1492079367 |
With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more
| Author | : Rebecca W. Keller |
| Publisher | : Real Science-4-Kids |
| Total Pages | : 248 |
| Release | : 2015-05-25 |
| Genre | : Science |
| ISBN | : 9781941181133 |
Foundational scientific concepts and terminology are easy to understand. Yearlong curriculum-5 scientific disciplines: chemistry, biology, physics, geology, astronomy. Full color textbook with many graphics. Covers: technology; microscopes; chemical reactions; protists; fungi; motion; Earth's layers; Earth as a system; solar systems; much more.
| Author | : Avrim Blum |
| Publisher | : Cambridge University Press |
| Total Pages | : 433 |
| Release | : 2020-01-23 |
| Genre | : Computers |
| ISBN | : 1108617360 |
This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
| Author | : Pamela J. Shoemaker |
| Publisher | : SAGE Publications |
| Total Pages | : 241 |
| Release | : 2003-12-10 |
| Genre | : Language Arts & Disciplines |
| ISBN | : 1452210438 |
Click ′Additional Materials′ to read the foreword by Jerald Hage As straightforward as its title, How to Build Social Science Theories sidesteps the well-traveled road of theoretical examination by demonstrating how new theories originate and how they are elaborated. Essential reading for students of social science research, this book traces theories from their most rudimentary building blocks (terminology and definitions) through multivariable theoretical statements, models, the role of creativity in theory building, and how theories are used and evaluated. Authors Pamela J. Shoemaker, James William Tankard, Jr., and Dominic L. Lasorsa intend to improve research in many areas of the social sciences by making research more theory-based and theory-oriented. The book begins with a discussion of concepts and their theoretical and operational definitions. It then proceeds to theoretical statements, including hypotheses, assumptions, and propositions. Theoretical statements need theoretical linkages and operational linkages; this discussion begins with bivariate relationships, as well as three-variable, four-variable, and further multivariate relationships. The authors also devote chapters to the creative component of theory-building and how to evaluate theories. How to Build Social Science Theories is a sophisticated yet readable analysis presented by internationally known experts in social science methodology. It is designed primarily as a core text for graduate and advanced undergraduate courses in communication theory. It will also be a perfect addition to any course dealing with theory and research methodology across the social sciences. Additionally, professional researchers will find it an indispensable guide to the genesis, dissemination, and evaluation of social science theories.
| Author | : Rebecca W. Keller |
| Publisher | : Real Science-4-Kids |
| Total Pages | : 208 |
| Release | : 2014-07-10 |
| Genre | : |
| ISBN | : 9781941181256 |
Introduces young learners to chemistry, biology, physics, geology, and astronomy. Includes coloring, drawing, making observations, doing simple experiments, answering questions, and more. Scientific concepts such as atoms, molecules, characteristics of living things, laws of motion, what Earth is made of, and the moon and planets are presented.
| Author | : Shraddha Kulhari |
| Publisher | : Nomos Verlagsgesellschaft |
| Total Pages | : 0 |
| Release | : 2018 |
| Genre | : Blockchains |
| ISBN | : 9783848752225 |
The General Data Protection Regulation (GDPR) replaced the old and battered Data Protection Directive on 25 May 2018 after a long-drawn reform. The rapidly evolving technological landscape will test the ability of the GDPR to effectively achieve the goals of protecting personal data and the free movement of data. This book proposes a technological supplement to achieve the goal of data protection as enshrined in the GDPR. The proposal comes in the form of digital identity management platforms built on blockchain technology. However, the very structure of blockchain poses some significant challenges in terms of compatibility with the GDPR. Accordingly, the claim of GDPR being a technologically neutral legislation is examined. The compatibility of a blockchain-based solution is scrutinised on the parameters of data protection principles like accountability, data minimisation, control and data protection by design in conjunction with the right to be forgotten and right to data portability.
