Agile Data Science

You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps.

Agile Data Science

Author: Russell Jurney

Publisher: "O'Reilly Media, Inc."

ISBN: 1449326927

Page: 178

View: 888

Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps. Create analytics applications by using the agile big data development methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track

Data Science Analytics and Applications

This book offers the proceedings of the Second International Data Science Conference (iDSC2019), organized by Salzburg University of Applied Sciences, Austria.

Data Science     Analytics and Applications

Author: Peter Haber

Publisher: Springer-Verlag

ISBN: 3658274956

Page: 102

View: 770

This book offers the proceedings of the Second International Data Science Conference (iDSC2019), organized by Salzburg University of Applied Sciences, Austria. The Conference brought together researchers, scientists, and business experts to discuss new ways of embracing agile approaches to various facets of data science, including machine learning and artificial intelligence, data mining, data visualization, and communication. The papers gathered here include case studies of applied techniques, and theoretical papers that push the field into the future. The full-length scientific-track papers on Data Analytics are broadly grouped by category, including Complexity; NLP and Semantics; Modelling; and Comprehensibility. Included among real-world applications of data science are papers on Exploring insider trading using hypernetworks Data-driven approach to detection of autism spectrum disorder Anonymization and sentiment analysis of Twitter posts Theoretical papers in the book cover such topics as Optimal Regression Tree Models Through Mixed Integer Programming; Chance Influence in Datasets with Large Number of Features; Adversarial Networks — A Technology for Image Augmentation; and Optimal Regression Tree Models Through Mixed Integer Programming. Five shorter student-track papers are also published here, on topics such as State-of-the-art Deep Learning Methods to effect Neural Machine Translation from Natural Language into SQL A Smart Recommendation System to Simplify Projecting for a HMI/SCADA Platform Use of Adversarial Networks as a Technology for Image Augmentation Using Supervised Learning to Predict the Reliability of a Welding Process The work collected in this volume of proceedings will provide researchers and practitioners with a detailed snapshot of current progress in the field of data science. Moreover, it will stimulate new study, research, and the development of new applications.

Data Science for Healthcare

A basic grasp of data science is recommended in order to fully benefit from this book. This book seeks to promote the exploitation of data science in healthcare systems.

Data Science for Healthcare

Author: Sergio Consoli

Publisher: Springer

ISBN: 3030052494

Page: 367

View: 141

This book seeks to promote the exploitation of data science in healthcare systems. The focus is on advancing the automated analytical methods used to extract new knowledge from data for healthcare applications. To do so, the book draws on several interrelated disciplines, including machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web technologies, and focuses on their direct application to healthcare. Building on three tutorial-like chapters on data science in healthcare, the following eleven chapters highlight success stories on the application of data science in healthcare, where data science and artificial intelligence technologies have proven to be very promising. This book is primarily intended for data scientists involved in the healthcare or medical sector. By reading this book, they will gain essential insights into the modern data science technologies needed to advance innovation for both healthcare businesses and patients. A basic grasp of data science is recommended in order to fully benefit from this book.

Data Science Analytics and Applications

The First International Conference on Data Science Analytics and Applications (
DaSAA 2017) was held during January 4–6, 2017, with a preconference tutorial
on January 3, by the Department of Computer Science and Engineering, CEG, ...

Data Science Analytics and Applications

Author: Shriram R

Publisher: Springer

ISBN: 9811086036

Page: 205

View: 970

This book constitutes the refereed proceedings of the First International Conference on Data Science Analytics and Applications, DaSAA 2017, held in Chennai, India, in January 2017. The 16 revised full papers and 4 revised short papers presented were carefully reviewed and selected from 77 submissions. The papers address issues such as data analytics, data mining, cloud computing, machine learning, text classification and analysis, information retrieval, DSS, security, image and video processing.

Trends of Data Science and Applications

This book is helpful for the students, practitioners, researchers as well as industry professional. This book includes an extended version of selected papers presented at the 11th Industry Symposium 2021 held during January 7–10, 2021.

Trends of Data Science and Applications

Author: Siddharth Swarup Rautaray

Publisher: Springer

ISBN: 9789813368149

Page: 341

View: 260

This book includes an extended version of selected papers presented at the 11th Industry Symposium 2021 held during January 7–10, 2021. The book covers contributions ranging from theoretical and foundation research, platforms, methods, applications, and tools in all areas. It provides theory and practices in the area of data science, which add a social, geographical, and temporal dimension to data science research. It also includes application-oriented papers that prepare and use data in discovery research. This book contains chapters from academia as well as practitioners on big data technologies, artificial intelligence, machine learning, deep learning, data representation and visualization, business analytics, healthcare analytics, bioinformatics, etc. This book is helpful for the students, practitioners, researchers as well as industry professional.

Data Science Analytics and Applications

Martin Atzmueller Tilburg University (TiCC), Jheronimus Academy of Data
Science, University of Kassel (ITeG) Abstract—Detecting anomalous behavior
can be of critical importance in an industrial application context. While modern
production ...

Data Science     Analytics and Applications

Author: Peter Haber

Publisher: Springer-Verlag

ISBN: 3658192879

Page: 105

View: 945

The iDSC Proceedings reports on state-of-the-art results in Data Science research, development and business. Topics and content of the IDSC2017 proceedings are • Reasoning and Predictive Analytics • Data Analytics in Community Networks • Data Analytics through Sentiment Analysis • User/Customer-centric Data Analytics • Data Analytics in Industrial Application Scenarios Advances in technology and changes in the business and social environment have led to an increasing flood of data, fueling both the need and the desire to generate value from these assets. The emerging field of Data Science is poised to deliver theoretical and practical solutions to the pressing issues of data-driven applications. The 1st International Data Science Conference (iDSC2017 / http://www.idsc.at) organized by Salzburg University of Applied Sciences in cooperation with Information Professionals GmbH, established a new key Data Science event, by pro viding a forum for the international exchange of Data Science technologies and applications.

Analytics in a Big Data World

The guide to targeting and leveraging business opportunities using big data & analytics By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of ...

Analytics in a Big Data World

Author: Bart Baesens

Publisher: John Wiley & Sons

ISBN: 1118892704

Page: 256

View: 884

The guide to targeting and leveraging business opportunities using big data & analytics By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments. The book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic. Includes numerous case studies on risk management, fraud detection, customer relationship management, and web analytics Offers the results of research and the author's personal experience in banking, retail, and government Contains an overview of the visionary ideas and current developments on the strategic use of analytics for business Covers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysis For organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities.

Data Science Concepts and Techniques with Applications

This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three sections: The first section is an introduction to data science.

Data Science Concepts and Techniques with Applications

Author: Usman Qamar

Publisher: Springer

ISBN: 9789811561320

Page: 196

View: 299

This book comprehensively covers the topic of data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three sections: The first section is an introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics. Followed by discussion on wide range of applications of data science and widely used techniques in data science. The second section is devoted to the tools and techniques of data science. It consists of data pre-processing, feature selection, classification and clustering concepts as well as an introduction to text mining and opining mining. And finally, the third section of the book focuses on two programming languages commonly used for data science projects i.e. Python and R programming language. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. The book is suitable for both undergraduate and postgraduate students as well as those carrying out research in data science. It can be used as a textbook for undergraduate students in computer science, engineering and mathematics. It can also be accessible to undergraduate students from other areas with the adequate background. The more advanced chapters can be used by postgraduate researchers intending to gather a deeper theoretical understanding.

Analytics and Data Science

This book explores emerging research and pedagogy in analytics and data science that have become core to many businesses as they work to derive value from data.

Analytics and Data Science

Author: Amit V. Deokar

Publisher: Springer

ISBN: 3319580973

Page: 297

View: 660

This book explores emerging research and pedagogy in analytics and data science that have become core to many businesses as they work to derive value from data. The chapters examine the role of analytics and data science to create, spread, develop and utilize analytics applications for practice. Selected chapters provide a good balance between discussing research advances and pedagogical tools in key topic areas in analytics and data science in a systematic manner. This book also focuses on several business applications of these emerging technologies in decision making, i.e., business analytics. The chapters in Analytics and Data Science: Advances in Research and Pedagogy are written by leading academics and practitioners that participated at the Business Analytics Congress 2015. Applications of analytics and data science technologies in various domains are still evolving. For instance, the explosive growth in big data and social media analytics requires examination of the impact of these technologies and applications on business and society. As organizations in various sectors formulate their IT strategies and investments, it is imperative to understand how various analytics and data science approaches contribute to the improvements in organizational information processing and decision making. Recent advances in computational capacities coupled by improvements in areas such as data warehousing, big data, analytics, semantics, predictive and descriptive analytics, visualization, and real-time analytics have particularly strong implications on the growth of analytics and data science.

Advanced Data Science and Analytics with Python

Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book.

Advanced Data Science and Analytics with Python

Author: Jesus Rogel-Salazar

Publisher: CRC Press

ISBN: 0429822324

Page: 384

View: 220

Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications. Features: Targets readers with a background in programming, who are interested in the tools used in data analytics and data science Uses Python throughout Presents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needs Focuses on the practical use of the tools rather than on lengthy explanations Provides the reader with the opportunity to use the book whenever needed rather than following a sequential path The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences – in this case, literally to the users’ fingertips in the form of an iPhone app. About the Author Dr. Jesús Rogel-Salazar is a lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.

Data Science and Big Data Analytics

Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use.

Data Science and Big Data Analytics

Author: EMC Education Services

Publisher: John Wiley & Sons

ISBN: 1118876229

Page: 432

View: 319

Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Corresponding data sets are available at www.wiley.com/go/9781118876138. Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!

Data Science and Analytics with Python R and SPSS Programming

The Book has been written completely as per AICTE recommended syllabus on "Data Sciences". SALIENT FEATURES OF THE BOOK: Explains how data is collected, managed and stored for data science.

Data Science and Analytics  with Python  R and SPSS Programming

Author: V.K. Jain

Publisher: KHANNA PUBLISHING HOUSE

ISBN: 9386173670

Page: 276

View: 153

The Book has been written completely as per AICTE recommended syllabus on "Data Sciences". SALIENT FEATURES OF THE BOOK: Explains how data is collected, managed and stored for data science. With complete courseware for understand the key concepts in data science including their real-world applications and the toolkit used by data scientists. Implement data collection and management. Provided with state of the arts subjectwise. With all required tutorials on R, Python and Bokeh, Anaconda, IBM SPSS-21 and Matplotlib.

Data Science Concepts and Techniques with Applications

This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three sections: The first section is an introduction to data science.

Data Science Concepts and Techniques with Applications

Author: Usman Qamar

Publisher: Springer Nature

ISBN: 9811561338

Page: 196

View: 335

This book comprehensively covers the topic of data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three sections: The first section is an introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics. Followed by discussion on wide range of applications of data science and widely used techniques in data science. The second section is devoted to the tools and techniques of data science. It consists of data pre-processing, feature selection, classification and clustering concepts as well as an introduction to text mining and opining mining. And finally, the third section of the book focuses on two programming languages commonly used for data science projects i.e. Python and R programming language. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. The book is suitable for both undergraduate and postgraduate students as well as those carrying out research in data science. It can be used as a textbook for undergraduate students in computer science, engineering and mathematics. It can also be accessible to undergraduate students from other areas with the adequate background. The more advanced chapters can be used by postgraduate researchers intending to gather a deeper theoretical understanding.

Agile Data Science 2 0

With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools.

Agile Data Science 2 0

Author: Russell Jurney

Publisher: "O'Reilly Media, Inc."

ISBN: 149196006X

Page: 352

View: 959

Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track

Computational Intelligence and Big Data Analytics

This book highlights major issues related to big data analysis using computational intelligence techniques, mostly interdisciplinary in nature.

Computational Intelligence and Big Data Analytics

Author: Ch. Satyanarayana

Publisher: Springer

ISBN: 9811305447

Page: 137

View: 513

This book highlights major issues related to big data analysis using computational intelligence techniques, mostly interdisciplinary in nature. It comprises chapters on computational intelligence technologies, such as neural networks and learning algorithms, evolutionary computation, fuzzy systems and other emerging techniques in data science and big data, ranging from methodologies, theory and algorithms for handling big data, to their applications in bioinformatics and related disciplines. The book describes the latest solutions, scientific results and methods in solving intriguing problems in the fields of big data analytics, intelligent agents and computational intelligence. It reflects the state of the art research in the field and novel applications of new processing techniques in computer science. This book is useful to both doctoral students and researchers from computer science and engineering fields and bioinformatics related domains.

Data Analytics Applications in Gaming and Entertainment

By now, data mining and analytics have become vital components of game development. The amount of work being done in this area nowadays makes this an ideal time to put together a book on this subject.

Data Analytics Applications in Gaming and Entertainment

Author: Günter Wallner

Publisher: CRC Press

ISBN: 1000001865

Page: 284

View: 577

The last decade has witnessed the rise of big data in game development as the increasing proliferation of Internet-enabled gaming devices has made it easier than ever before to collect large amounts of player-related data. At the same time, the emergence of new business models and the diversification of the player base have exposed a broader potential audience, which attaches great importance to being able to tailor game experiences to a wide range of preferences and skill levels. This, in turn, has led to a growing interest in data mining techniques, as they offer new opportunities for deriving actionable insights to inform game design, to ensure customer satisfaction, to maximize revenues, and to drive technical innovation. By now, data mining and analytics have become vital components of game development. The amount of work being done in this area nowadays makes this an ideal time to put together a book on this subject. Data Analytics Applications in Gaming and Entertainment seeks to provide a cross section of current data analytics applications in game production. It is intended as a companion for practitioners, academic researchers, and students seeking knowledge on the latest practices in game data mining. The chapters have been chosen in such a way as to cover a wide range of topics and to provide readers with a glimpse at the variety of applications of data mining in gaming. A total of 25 authors from industry and academia have contributed 12 chapters covering topics such as player profiling, approaches for analyzing player communities and their social structures, matchmaking, churn prediction and customer lifetime value estimation, communication of analytical results, and visual approaches to game analytics. This book’s perspectives and concepts will spark heightened interest in game analytics and foment innovative ideas that will advance the exciting field of online gaming and entertainment.

Marketing Data Science

This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R. Marketing Data Science will be an invaluable resource for all students, faculty, and ...

Marketing Data Science

Author: Thomas W. Miller

Publisher: FT Press

ISBN: 0133887340

Page: 225

View: 302

Now , a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications. Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis. Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes: The role of analytics in delivering effective messages on the web Understanding the web by understanding its hidden structures Being recognized on the web – and watching your own competitors Visualizing networks and understanding communities within them Measuring sentiment and making recommendations Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R. Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance.

Data Science Thinking

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, ...

Data Science Thinking

Author: Longbing Cao

Publisher: Springer

ISBN: 3319950924

Page: 390

View: 787

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. The topics cover an extremely wide spectrum of essential and relevant aspects of data science, spanning its evolution, concepts, thinking, challenges, discipline, and foundation, all the way to industrialization, profession, education, and the vast array of opportunities that data science offers. The book's three parts each detail layers of these different aspects. The book is intended for decision-makers, data managers (e.g., analytics portfolio managers, business analytics managers, chief data analytics officers, chief data scientists, and chief data officers), policy makers, management and decision strategists, research leaders, and educators who are responsible for pursuing new scientific, innovation, and industrial transformation agendas, enterprise strategic planning, a next-generation profession-oriented course development, as well as those who are involved in data science, technology, and economy from an advanced perspective. Research students in data science-related courses and disciplines will find the book useful for positing their innovative scientific journey, planning their unique and promising career, and competing within and being ready for the next generation of science, technology, and economy.

Data Science and Internet of Things

This book focuses on the combination of IoT and data science, in particular how methods, algorithms, and tools from data science can effectively support IoT.

Data Science and Internet of Things

Author: Giancarlo Fortino

Publisher: Springer

ISBN: 9783030671969

Page: 182

View: 843

This book focuses on the combination of IoT and data science, in particular how methods, algorithms, and tools from data science can effectively support IoT. The authors show how data science methodologies, techniques and tools, can translate data into information, enabling the effectiveness and usefulness of new services offered by IoT stakeholders. The authors posit that if IoT is indeed the infrastructure of the future, data structure is the key that can lead to a significant improvement of human life. The book aims to present innovative IoT applications as well as ongoing research that exploit modern data science approaches. Readers are offered issues and challenges in a cross-disciplinary scenario that involves both IoT and data science fields. The book features contributions from academics, researchers, and professionals from both fields.

Big Data Science Analytics

An accompanying website for this book contains additional support for instruction and learning (www.big-data-analytics-book.com) The book is organized into three main parts, comprising a total of twelve chapters.

Big Data Science   Analytics

Author: Arshdeep Bahga

Publisher: Vpt

ISBN: 9780996025539

Page: 544

View: 741

We are living in the dawn of what has been termed as the "Fourth Industrial Revolution," which is marked through the emergence of "cyber-physical systems" where software interfaces seamlessly over networks with physical systems, such as sensors, smartphones, vehicles, power grids or buildings, to create a new world of Internet of Things (IoT). Data and information are fuel of this new age where powerful analytics algorithms burn this fuel to generate decisions that are expected to create a smarter and more efficient world for all of us to live in. This new area of technology has been defined as Big Data Science and Analytics, and the industrial and academic communities are realizing this as a competitive technology that can generate significant new wealth and opportunity. Big data is defined as collections of datasets whose volume, velocity or variety is so large that it is difficult to store, manage, process and analyze the data using traditional databases and data processing tools. Big data science and analytics deals with collection, storage, processing and analysis of massive-scale data. Industry surveys, by Gartner and e-Skills, for instance, predict that there will be over 2 million job openings for engineers and scientists trained in the area of data science and analytics alone, and that the job market is in this area is growing at a 150 percent year-over-year growth rate. We have written this textbook, as part of our expanding "A Hands-On Approach"(TM) series, to meet this need at colleges and universities, and also for big data service providers who may be interested in offering a broader perspective of this emerging field to accompany their customer and developer training programs. The typical reader is expected to have completed a couple of courses in programming using traditional high-level languages at the college-level, and is either a senior or a beginning graduate student in one of the science, technology, engineering or mathematics (STEM) fields. An accompanying website for this book contains additional support for instruction and learning (www.big-data-analytics-book.com) The book is organized into three main parts, comprising a total of twelve chapters. Part I provides an introduction to big data, applications of big data, and big data science and analytics patterns and architectures. A novel data science and analytics application system design methodology is proposed and its realization through use of open-source big data frameworks is described. This methodology describes big data analytics applications as realization of the proposed Alpha, Beta, Gamma and Delta models, that comprise tools and frameworks for collecting and ingesting data from various sources into the big data analytics infrastructure, distributed filesystems and non-relational (NoSQL) databases for data storage, and processing frameworks for batch and real-time analytics. This new methodology forms the pedagogical foundation of this book. Part II introduces the reader to various tools and frameworks for big data analytics, and the architectural and programming aspects of these frameworks, with examples in Python. We describe Publish-Subscribe messaging frameworks (Kafka & Kinesis), Source-Sink connectors (Flume), Database Connectors (Sqoop), Messaging Queues (RabbitMQ, ZeroMQ, RestMQ, Amazon SQS) and custom REST, WebSocket and MQTT-based connectors. The reader is introduced to data storage, batch and real-time analysis, and interactive querying frameworks including HDFS, Hadoop, MapReduce, YARN, Pig, Oozie, Spark, Solr, HBase, Storm, Spark Streaming, Spark SQL, Hive, Amazon Redshift and Google BigQuery. Also described are serving databases (MySQL, Amazon DynamoDB, Cassandra, MongoDB) and the Django Python web framework. Part III introduces the reader to various machine learning algorithms with examples using the Spark MLlib and H2O frameworks, and visualizations using frameworks such as Lightning, Pygal and Seaborn.