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Data Science

Data Science
A Comprehensive Beginner's Guide to Learn the Realms Of Data Science

by William Vance

  • Publisher : joiningthedotstv
  • Release : 2020-07-24
  • Pages : 92
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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This book will introduce you to the digital world. Data science is one of the most amazing and trending fields in the digital era. Data science is what makes us humans what we are today. Not limited to computer-driven technologies, this book will guide you to visualize the digital facts and connections of our brain with data science, how to draw conclusions from simple information, and how to develop patterns for understanding different solutions for a similar problem. But our brains can only take us so far when it comes to raw computing. Our brains can't keep up with the amount of data we can capture, and with the extent of our curiosity. So we turned towards machines that are able to capture and store terabytes of information and to do part of the work for us, like recognizing patterns, creating connections, and supplying us with accurate results. Data science is a field where you will be able to get to learn every modern technique. Keeping in mind all these facts, we thought of writing this book targeting the data science beginner. This book provides an overview of data science, teaching you: · What is data science, and how it has emerged · What are the responsibilities of a data scientist and the fundamentals of data science · Overall process with the life cycle of data science · How data science tools, like statistics, probability, etc. · Help to draw insights from data · Basic concept about data modeling, and featurization · How to work with data variables and data science tools · How to visualize the data · How to work with machine learning algorithms and Artificial Neural Networks · Concepts of decision trees and cloud computing. We have included everything a beginner needs to venture into the data science world. Don’t waste another second. Now is your chance to get started!

Roundtable on Data Science Postsecondary Education

Roundtable on Data Science Postsecondary Education
A Compilation of Meeting Highlights

by National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Division on Engineering and Physical Sciences,Board on Science Education,Computer Science and Telecommunications Board,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics

  • Publisher : National Academies Press
  • Release : 2020-10-02
  • Pages : 223
  • ISBN : 030967770X
  • Language : En, Es, Fr & De
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Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting.

Data Science For Dummies

Data Science For Dummies
A Book

by Lillian Pierson,Ryan Swanstrom,Carl Anderson

  • Publisher : John Wiley & Sons
  • Release : 2015-03-09
  • Pages : 408
  • ISBN : 1118841557
  • Language : En, Es, Fr & De
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"Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles in organizations. Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organization's massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you'll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization."--Provided by publisher.

Introduction to Biomedical Data Science

Introduction to Biomedical Data Science
A Book

by Robert Hoyt,Robert Muenchen

  • Publisher : Lulu.com
  • Release : 2019-11-25
  • Pages : 258
  • ISBN : 179476173X
  • Language : En, Es, Fr & De
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Introduction to Biomedical Data Science aims to fill the data science knowledge gap experienced by many clinical, administrative and technical staff. The textbook begins with an overview of what biomedical data science is and then embarks on a tour of topics beginning with spreadsheet tips and tricks and ending with artificial intelligence. In between, important topics are covered such as biostatistics, data visualization, database systems, big data, programming languages, bioinformatics, and machine learning. The textbook is available as a paperback and ebook. Visit the companion website at https: //www.informaticseducation.org for more information. Key features: Real healthcare datasets are used for examples and exercises; Knowledge of a programming language or higher math is not required; Multiple free or open source software programs are presented; YouTube videos are embedded in most chapters; Extensive resources chapter for further reading and learning; PowerPoints and an Instructor Manual

Data Science from Scratch

Data Science from Scratch
First Principles with Python

by Joel Grus

  • Publisher : O'Reilly Media
  • Release : 2019-04-12
  • Pages : 406
  • ISBN : 1492041106
  • Language : En, Es, Fr & De
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Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. With this updated second edition, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.

The Essentials of Data Science: Knowledge Discovery Using R

The Essentials of Data Science: Knowledge Discovery Using R
A Book

by Graham J. Williams

  • Publisher : CRC Press
  • Release : 2017-07-28
  • Pages : 322
  • ISBN : 1351647490
  • Language : En, Es, Fr & De
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The Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data. Building on over thirty years’ experience in teaching and practising data science, the author encourages a programming-by-example approach to ensure students and practitioners attune to the practise of data science while building their data skills. Proven frameworks are provided as reusable templates. Real world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets. The book begins by introducing data science. It then reviews R’s capabilities for analysing data by writing computer programs. These programs are developed and explained step by step. From analysing and visualising data, the framework moves on to tried and tested machine learning techniques for predictive modelling and knowledge discovery. Literate programming and a consistent style are a focus throughout the book.

R for Data Science Cookbook

R for Data Science Cookbook
A Book

by Yu-Wei, Chiu (David Chiu)

  • Publisher : Packt Publishing Ltd
  • Release : 2016-07-29
  • Pages : 452
  • ISBN : 1784392049
  • Language : En, Es, Fr & De
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Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques About This Book Gain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packages Understand how to apply useful data analysis techniques in R for real-world applications An easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysis Who This Book Is For This book is for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages. What You Will Learn Get to know the functional characteristics of R language Extract, transform, and load data from heterogeneous sources Understand how easily R can confront probability and statistics problems Get simple R instructions to quickly organize and manipulate large datasets Create professional data visualizations and interactive reports Predict user purchase behavior by adopting a classification approach Implement data mining techniques to discover items that are frequently purchased together Group similar text documents by using various clustering methods In Detail This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis. Style and approach This easy-to-follow guide is full of hands-on examples of data analysis with R. Each topic is fully explained beginning with the core concept, followed by step-by-step practical examples, and concluding with detailed explanations of each concept used.

Think Like a Data Scientist

Think Like a Data Scientist
Tackle the Data Science Process Step-by-step

by Brian Godsey

  • Publisher : Manning Publications
  • Release : 2017-02-28
  • Pages : 340
  • ISBN : 9781633430273
  • Language : En, Es, Fr & De
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Data science is more than just a set of tools and techniques for extracting knowledge from data sets and data streams. Data science is also a process of getting from goals and questions to real, valuable outcomes by exploring, observing, and manipulating a world of data. Traversing this world can be difficult and confusing. Software developers and non-technical folks may struggle with the uncertainty and fuzzy answers that data invariably provide, and statisticians may have trouble working with any of the multitude of relevant software tools that lie outside of their expertise. Others may not even know where to begin. Think Like a Data Scientist presents a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems. This book helps you fill in conceptual knowledge gaps in the daunting fields of statistics and software development, and relates those skills to the real concerns of data science in the business world. As you work though the many practical examples, you'll use your existing knowledge of statistics and programming to solve real problems in data science. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Textual Data Science with R

Textual Data Science with R
A Book

by Mónica Bécue-Bertaut

  • Publisher : CRC Press
  • Release : 2019-03-11
  • Pages : 204
  • ISBN : 1351816357
  • Language : En, Es, Fr & De
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Textual Statistics with R comprehensively covers the main multidimensional methods in textual statistics supported by a specially-written package in R. Methods discussed include correspondence analysis, clustering, and multiple factor analysis for contigency tables. Each method is illuminated by applications. The book is aimed at researchers and students in statistics, social sciences, hiistory, literature and linguistics. The book will be of interest to anyone from practitioners needing to extract information from texts to students in the field of massive data, where the ability to process textual data is becoming essential.

Process Mining

Process Mining
Data Science in Action

by Wil M. P. van der Aalst

  • Publisher : Springer
  • Release : 2018-04-22
  • Pages : 467
  • ISBN : 9783662570418
  • Language : En, Es, Fr & De
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This is the second edition of Wil van der Aalst’s seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several additions and updates, e.g. on inductive mining techniques, the notion of alignments, a considerably expanded section on software tools and a completely new chapter of process mining in the large. It is self-contained, while at the same time covering the entire process-mining spectrum from process discovery to predictive analytics. After a general introduction to data science and process mining in Part I, Part II provides the basics of business process modeling and data mining necessary to understand the remainder of the book. Next, Part III focuses on process discovery as the most important process mining task, while Part IV moves beyond discovering the control flow of processes, highlighting conformance checking, and organizational and time perspectives. Part V offers a guide to successfully applying process mining in practice, including an introduction to the widely used open-source tool ProM and several commercial products. Lastly, Part VI takes a step back, reflecting on the material presented and the key open challenges. Overall, this book provides a comprehensive overview of the state of the art in process mining. It is intended for business process analysts, business consultants, process managers, graduate students, and BPM researchers.

Targeted Learning in Data Science

Targeted Learning in Data Science
Causal Inference for Complex Longitudinal Studies

by Mark J. van der Laan,Sherri Rose

  • Publisher : Springer
  • Release : 2018-03-28
  • Pages : 640
  • ISBN : 3319653040
  • Language : En, Es, Fr & De
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This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011. Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics. Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose’s methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics.

Data Science Programming All-In-One For Dummies

Data Science Programming All-In-One For Dummies
A Book

by John Paul Mueller,Luca Massaron

  • Publisher : John Wiley & Sons
  • Release : 2020-01-09
  • Pages : 768
  • ISBN : 1119626110
  • Language : En, Es, Fr & De
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Your logical, linear guide to the fundamentals of data science programming Data science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models. Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Get grounded: the ideal start for new data professionals What lies ahead: learn about specific areas that data is transforming Be meaningful: find out how to tell your data story See clearly: pick up the art of visualization Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s!

Mathematics of Data Science: A Computational Approach to Clustering and Classification

Mathematics of Data Science: A Computational Approach to Clustering and Classification
A Book

by Daniela Calvetti,Erkki Somersalo

  • Publisher : SIAM
  • Release : 2020-11-20
  • Pages : 199
  • ISBN : 1611976375
  • Language : En, Es, Fr & De
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This textbook provides a solid mathematical basis for understanding popular data science algorithms for clustering and classification and shows that an in-depth understanding of the mathematics powering these algorithms gives insight into the underlying data. It presents a step-by-step derivation of these algorithms, outlining their implementation from scratch in a computationally sound way. Mathematics of Data Science: A Computational Approach to Clustering and Classification proposes different ways of visualizing high-dimensional data to unveil hidden internal structures, and nearly every chapter includes graphical explanations and computed examples using publicly available data sets to highlight similarities and differences among the algorithms. This self-contained book is geared toward advanced undergraduate and beginning graduate students in the mathematical sciences, engineering, and computer science and can be used as the main text in a semester course. Researchers in any application area where data science methods are used will also find the book of interest. No advanced mathematical or statistical background is assumed.

Mathematical Foundations of Data Science Using R

Mathematical Foundations of Data Science Using R
A Book

by Frank Emmert-Streib,Salissou Moutari,Matthias Dehmer

  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release : 2020-06-08
  • Pages : 429
  • ISBN : 3110565021
  • Language : En, Es, Fr & De
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In order best exploit the incredible quantities of data being generated in most diverse disciplines data sciences increasingly gain worldwide importance. The book gives the mathematical foundations to handle data properly. It introduces basics and functionalities of the R programming language which has become the indispensable tool for data sciences. Thus it delivers the reader the skills needed to build own tool kits of a modern data scientist.

It's All Analytics!

It's All Analytics!
The Foundations of Al, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government

by Scott Burk,Gary D. Miner

  • Publisher : CRC Press
  • Release : 2020-07-02
  • Pages : 272
  • ISBN : 100006722X
  • Language : En, Es, Fr & De
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It's All Analytics! The Foundations of AI, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government (978-0-367-35968-3, 325690) Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially in the last 25 years, there has been an explosion of terms and methods that automate and improve decision-making and operations. One term, "analytics," is an overarching description of a compilation of methodologies. But AI (artificial intelligence), statistics, decision science, and optimization, which have been around for decades, have resurged. Also, things like business intelligence, online analytical processing (OLAP) and many, many more have been born or reborn. How is someone to make sense of all this methodology and terminology? This book, the first in a series of three, provides a look at the foundations of artificial intelligence and analytics and why readers need an unbiased understanding of the subject. The authors include the basics such as algorithms, mental concepts, models, and paradigms in addition to the benefits of machine learning. The book also includes a chapter on data and the various forms of data. The authors wrap up this book with a look at the next frontiers such as applications and designing your environment for success, which segue into the topics of the next two books in the series.

Advances in Data Science: Methodologies and Applications

Advances in Data Science: Methodologies and Applications
A Book

by Gloria Phillips-Wren,Anna Esposito,Lakhmi C. Jain

  • Publisher : Springer Nature
  • Release : 2020-08-26
  • Pages : 333
  • ISBN : 3030518701
  • Language : En, Es, Fr & De
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Big data and data science are transforming our world today in ways we could not have imagined at the beginning of the twenty-first century. The accompanying wave of innovation has sparked advances in healthcare, engineering, business, science, and human perception, among others. The tremendous advances in computing power and intelligent techniques have opened many opportunities for managing data and investigating data in virtually every field, and the scope of data science is expected to grow over the next decade. These future research achievements will solve old challenges and create new opportunities for growth and development. Thus, the research presented in this book is interdisciplinary and covers themes embracing emotions, artificial intelligence, robotics applications, sentiment analysis, smart city problems, assistive technologies, speech melody, and fall and abnormal behavior detection. The book is directed to the researchers, practitioners, professors and students interested in recent advances in methodologies and applications of data science. An introduction to the topic is provided, and research challenges and future research opportunities are highlighted throughout.

Data Science: New Issues, Challenges and Applications

Data Science: New Issues, Challenges and Applications
A Book

by Gintautas Dzemyda,Jolita Bernatavičienė,Janusz Kacprzyk

  • Publisher : Springer
  • Release : 2020-02-14
  • Pages : 313
  • ISBN : 9783030392499
  • Language : En, Es, Fr & De
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This book contains 16 chapters by researchers working in various fields of data science. They focus on theory and applications in language technologies, optimization, computational thinking, intelligent decision support systems, decomposition of signals, model-driven development methodologies, interoperability of enterprise applications, anomaly detection in financial markets, 3D virtual reality, monitoring of environmental data, convolutional neural networks, knowledge storage, data stream classification, and security in social networking. The respective papers highlight a wealth of issues in, and applications of, data science. Modern technologies allow us to store and transfer large amounts of data quickly. They can be very diverse - images, numbers, streaming, related to human behavior and physiological parameters, etc. Whether the data is just raw numbers, crude images, or will help solve current problems and predict future developments, depends on whether we can effectively process and analyze it. Data science is evolving rapidly. However, it is still a very young field. In particular, data science is concerned with visualizations, statistics, pattern recognition, neurocomputing, image analysis, machine learning, artificial intelligence, databases and data processing, data mining, big data analytics, and knowledge discovery in databases. It also has many interfaces with optimization, block chaining, cyber-social and cyber-physical systems, Internet of Things (IoT), social computing, high-performance computing, in-memory key-value stores, cloud computing, social computing, data feeds, overlay networks, cognitive computing, crowdsource analysis, log analysis, container-based virtualization, and lifetime value modeling. Again, all of these areas are highly interrelated. In addition, data science is now expanding to new fields of application: chemical engineering, biotechnology, building energy management, materials microscopy, geographic research, learning analytics, radiology, metal design, ecosystem homeostasis investigation, and many others.

Data Science and Social Research II

Data Science and Social Research II
Methods, Technologies and Applications

by Paolo Mariani,Mariangela Zenga

  • Publisher : Springer
  • Release : 2020-11-26
  • Pages : 394
  • ISBN : 9783030512217
  • Language : En, Es, Fr & De
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The peer-reviewed contributions gathered in this book address methods, software and applications of statistics and data science in the social sciences. The data revolution in social science research has not only produced new business models, but has also provided policymakers with better decision-making support tools. In this volume, statisticians, computer scientists and experts on social research discuss the opportunities and challenges of the social data revolution in order to pave the way for addressing new research problems. The respective contributions focus on complex social systems and current methodological advances in extracting social knowledge from large data sets, as well as modern social research on human behavior and society using large data sets. Moreover, they analyze integrated systems designed to take advantage of new social data sources, and discuss quality-related issues. The papers were originally presented at the 2nd International Conference on Data Science and Social Research, held in Milan, Italy, on February 4-5, 2019.

AI for Data Science

AI for Data Science
Artificial Intelligence Frameworks and Functionality for Deep Learning, Optimization, and Beyond

by Zacharias Voulgaris PhD,Yunus Emrah Bulut

  • Publisher : Technics Publications
  • Release : 2018-10-01
  • Pages : 329
  • ISBN : 1634624114
  • Language : En, Es, Fr & De
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Master the approaches and principles of Artificial Intelligence (AI) algorithms, and apply them to Data Science projects with Python and Julia code. Aspiring and practicing Data Science and AI professionals, along with Python and Julia programmers, will practice numerous AI algorithms and develop a more holistic understanding of the field of AI, and will learn when to use each framework to tackle projects in our increasingly complex world. The first two chapters introduce the field, with Chapter 1 surveying Deep Learning models and Chapter 2 providing an overview of algorithms beyond Deep Learning, including Optimization, Fuzzy Logic, and Artificial Creativity. The next chapters focus on AI frameworks; they contain data and Python and Julia code in a provided Docker, so you can practice. Chapter 3 covers Apache’s MXNet, Chapter 4 covers TensorFlow, and Chapter 5 investigates Keras. After covering these Deep Learning frameworks, we explore a series of optimization frameworks, with Chapter 6 covering Particle Swarm Optimization (PSO), Chapter 7 on Genetic Algorithms (GAs), and Chapter 8 discussing Simulated Annealing (SA). Chapter 9 begins our exploration of advanced AI methods, by covering Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Chapter 10 discusses optimization ensembles and how they can add value to the Data Science pipeline. Chapter 11 contains several alternative AI frameworks including Extreme Learning Machines (ELMs), Capsule Networks (CapsNets), and Fuzzy Inference Systems (FIS). Chapter 12 covers other considerations complementary to the AI topics covered, including Big Data concepts, Data Science specialization areas, and useful data resources to experiment on. A comprehensive glossary is included, as well as a series of appendices covering Transfer Learning, Reinforcement Learning, Autoencoder Systems, and Generative Adversarial Networks. There is also an appendix on the business aspects of AI in data science projects, and an appendix on how to use the Docker image to access the book’s data and code. The field of AI is vast, and can be overwhelming for the newcomer to approach. This book will arm you with a solid understanding of the field, plus inspire you to explore further.

Data Science and Social Research

Data Science and Social Research
Epistemology, Methods, Technology and Applications

by N. Carlo Lauro,Enrica Amaturo,Maria Gabriella Grassia,Biagio Aragona,Marina Marino

  • Publisher : Springer
  • Release : 2017-11-17
  • Pages : 300
  • ISBN : 3319554778
  • Language : En, Es, Fr & De
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This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources. This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices.