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Data Protection on the Move: Current Developments in ICT and Privacy/Data Protection (Law, Governance and Technology Series #24)
by Serge Gutwirth Ronald Leenes Paul HertThis volume brings together papers that offer methodologies, conceptual analyses, highlight issues, propose solutions, and discuss practices regarding privacy and data protection. It is one of the results of the eight annual International Conference on Computers, Privacy, and Data Protection, CPDP 2015, held in Brussels in January 2015. The book explores core concepts, rights and values in (upcoming) data protection regulation and their (in)adequacy in view of developments such as Big and Open Data, including the right to be forgotten, metadata, and anonymity. It discusses privacy promoting methods and tools such as a formal systems modeling methodology, privacy by design in various forms (robotics, anonymous payment), the opportunities and burdens of privacy self management, the differentiating role privacy can play in innovation. The book also discusses EU policies with respect to Big and Open Data and provides advice to policy makers regarding these topics. Also attention is being paid to regulation and its effects, for instance in case of the so-called 'EU-cookie law' and groundbreaking cases, such as Europe v. Facebook. This interdisciplinary book was written during what may turn out to be the final stages of the process of the fundamental revision of the current EU data protection law by the Data Protection Package proposed by the European Commission. It discusses open issues and daring and prospective approaches. It will serve as an insightful resource for readers with an interest in privacy and data protection.
Data Protection: Ensuring Data Availability
by Preston de GuiseThis is the fundamental truth about data protection: backup is dead. Or rather, backup and recovery, as a standalone topic, no longer has relevance in IT. As a standalone topic, it’s been killed off by seemingly exponential growth in storage and data, by the cloud, and by virtualization. So what is data protection? This book takes a holistic, business-based approach to data protection. It explains how data protection is a mix of proactive and reactive planning, technology and activities that allow for data continuity. It shows how truly effective data protection comes from a holistic approach considering the entire data lifecycle and all required SLAs. Data protection is neither RAID nor is it continuous availability, replication, snapshots or backups—it is all of them, combined in a considered and measured approach to suit the criticality of the data and meet all the requirements of the business. The book also discusses how businesses seeking to creatively leverage their IT investments and to drive through cost optimization are increasingly looking at data protection as a mechanism to achieve those goals. In addition to being a type of insurance policy, data protection is becoming an enabler for new processes around data movement and data processing. This book arms readers with information critical for making decisions on how data can be protected against loss in the cloud, on-premises, or in a mix of the two. It explains the changing face of recovery in a highly virtualized data center and techniques for dealing with big data. Moreover, it presents a model for where data recovery processes can be integrated with IT governance and management in order to achieve the right focus on recoverability across the business.
Data Protection: Ensuring Data Availability
by Preston de GuiseThe second edition of Data Protection goes beyond the traditional topics including deduplication, continuous availability, snapshots, replication, backup, and recovery, and explores such additional considerations as legal, privacy, and ethical issues. A new model is presented for understanding and planning the various aspects of data protection, which is essential to developing holistic strategies. The second edition also addresses the cloud and the growing adoption of software and function as a service, as well as effectively planning over the lifespan of a workload: what the best mix of traditional and cloud native data protection services might be. Virtualization continues to present new challenges to data protection, and the impact of containerization is examined. The book takes a holistic, business-based approach to data protection. It explains how data protection is a mix of proactive and reactive planning, technology, and activities that allow for data continuity. There are three essential activities that refer to themselves as data protection; while they all overlap in terms of scope and function, each operates as a reasonably self-contained field with its own specialists and domain nomenclature. These three activities are: • Data protection as a storage and recovery activity • Data protection as a security activity • Data protection as a privacy activity These activities are covered in detail, with a focus on how organizations can use them to leverage their IT investments and optimize costs. The book also explains how data protection is becoming an enabler for new processes around data movement and data processing. This book arms readers with information critical for making decisions on how data can be protected against loss in the cloud, on premises, or in a mix of the two. It explains the changing face of recovery in a highly virtualized datacenter and techniques for dealing with big data. Moreover, it presents a model for where data recovery processes can be integrated with IT governance and management in order to achieve the right focus on recoverability across the business. About the Author Preston de Guise has been working with data recovery products for his entire career—designing, implementing, and supporting solutions for governments, universities, and businesses ranging from SMEs to Fortune 500 companies. This broad exposure to industry verticals and business sizes has enabled Preston to understand not only the technical requirements of data protection and recovery, but the management and procedural aspects too.
Data Protection: Governance, Risk Management, and Compliance
by David G. HillFailure to appreciate the full dimensions of data protection can lead to poor data protection management, costly resource allocation issues, and exposure to unnecessary risks. Data Protection: Governance, Risk Management, and Compliance explains how to gain a handle on the vital aspects of data protection.The author begins by building the foundatio
Data Protection: The Wake of AI and Machine Learning
by Dushantha Nalin K. Jayakody Chaminda Hewage Lasith YasakethuThis book provides a thorough and unique overview of the challenges, opportunities and solutions related with data protection in the age of AI and ML technologies. It investigates the interface of data protection and new technologies, emphasising the growing need to safeguard personal and confidential data from unauthorised access and change. The authors emphasize the crucial need of strong data protection regulations, focusing on the consequences of AI and ML breakthroughs for privacy and individual rights. This book emphasizes the multifarious aspect of data protection, which goes beyond technological solutions to include ethical, legislative and societal factors. This book explores into the complexity of data protection in the age of AI and ML. It investigates how massive volumes of personal and sensitive data are utilized to train and develop AI models, demanding novel privacy-preserving strategies such as anonymization, differential privacy and federated learning. The duties and responsibilities of engineers, policy makers and ethicists in minimizing algorithmic bias and ensuring ethical AI use are carefully defined. Key developments, such as the influence of the European Union's General Data Protection Regulation (GDPR) and the EU AI Act on data protection procedures, are reviewed critically. This investigation focusses not only on the tactics used, but also on the problems and successes in creating a secure and ethical AI ecosystem. This book provides a comprehensive overview of the efforts to integrate data protection into AI innovation, including valuable perspectives on the effectiveness of these measures and the ongoing adjustments required to address the fluid nature of privacy concerns. This book is a helpful resource for upper-undergraduate and graduate computer science students, as well as others interested in cybersecurity and data protection. Researchers in AI, ML, and data privacy as well as data protection officers, politicians, lawmakers and decision-makers will find this book useful as a reference.
Data Push Apps with HTML5 SSE: Pragmatic Solutions for Real-World Clients
by Darren CookMake sure your website or web application users get content updates right now with minimal latency. This concise guide shows you how to push new data from the server to clients with HTML5 Server-Sent Events (SSE), an exceptional technology that doesn’t require constant polling or user interaction. You’ll learn how to build a real-world SSE application from start to finish that solves a demanding domain problem.You’ll also discover how to increase that application’s desktop and mobile browser support from 60% to 99%, using different fallback solutions. If you’re familiar with HTML, HTTP, and basic JavaScript, you’re ready to get started.Determine whether SSE, WebSockets, or data pull is best for your organizationDevelop a working SSE application complete with backend and frontend solutionsAddress error handling, system recovery, and other issues to make the application production-qualityExplore two fallback solutions for browsers that don’t support SSETackle security issues, including authorization and "disallowed origin"Develop realistic, repeatable data that’s useful in test-driven SSE designLearn SSE protocol elements not covered in the example application
Data Quality Engineering in Financial Services: Applying Manufacturing Techniques to Data
by Brian BuzzelliData quality will either make you or break you in the financial services industry. Missing prices, wrong market values, trading violations, client performance restatements, and incorrect regulatory filings can all lead to harsh penalties, lost clients, and financial disaster. This practical guide provides data analysts, data scientists, and data practitioners in financial services firms with the framework to apply manufacturing principles to financial data management, understand data dimensions, and engineer precise data quality tolerances at the datum level and integrate them into your data processing pipelines.You'll get invaluable advice on how to:Evaluate data dimensions and how they apply to different data types and use casesDetermine data quality tolerances for your data quality specificationChoose the points along the data processing pipeline where data quality should be assessed and measuredApply tailored data governance frameworks within a business or technical function or across an organizationPrecisely align data with applications and data processing pipelinesAnd more
Data Quality Fundamentals: A Practitioner's Guide to Building Trustworthy Data Pipelines
by Barr Moses Lior Gavish Molly VorwerckDo your product dashboards look funky? Are your quarterly reports stale? Is the data set you're using broken or just plain wrong? These problems affect almost every team, yet they're usually addressed on an ad hoc basis and in a reactive manner. If you answered yes to these questions, this book is for you. Many data engineering teams today face the "good pipelines, bad data" problem. It doesn't matter how advanced your data infrastructure is if the data you're piping is bad. In this book, Barr Moses, Lior Gavish, and Molly Vorwerck, from the data observability company Monte Carlo, explain how to tackle data quality and trust at scale by leveraging best practices and technologies used by some of the world's most innovative companies. Build more trustworthy and reliable data pipelinesWrite scripts to make data checks and identify broken pipelines with data observabilityLearn how to set and maintain data SLAs, SLIs, and SLOsDevelop and lead data quality initiatives at your companyLearn how to treat data services and systems with the diligence of production softwareAutomate data lineage graphs across your data ecosystemBuild anomaly detectors for your critical data assets
Data Quality Management in the Data Age: Excellence in Data Quality for Enhanced Digital Economic Growth (SpringerBriefs in Service Science)
by Haiyan YuThis book addresses data quality management for data markets, including foundational quality issues in modern data science. By clarifying the concept of data quality, its impact on real-world applications, and the challenges stemming from poor data quality, it will equip data scientists and engineers with advanced skills in data quality management, with a particular focus on applications within data markets. This will help them create an environment that encourages potential data sellers with high-quality data to join the market, ultimately leading to an improvement in overall data quality. High-quality data, as a novel factor of production, has assumed a pivotal role in driving digital economic development. The acquisition of such data is particularly important for contemporary decision-making models. Data markets facilitate the procurement of high-quality data and thereby enhance the data supply. Consequently, potential data sellers with high-quality data are incentivized to enter the market, an aspect that is particularly relevant in data-scarce domains such as personalized medicine and services. Data scientists have a pivotal role to play in both the intellectual vitality and the practical utility of high-quality data. Moreover, data quality control presents opportunities for data scientists to engage with less structured or ambiguous problems. The book will foster fruitful discussions on the contributions that various scientists and engineers can make to data quality and the further evolution of data markets.
Data Quality and Trust in Big Data: 5th International Workshop, QUAT 2018, Held in Conjunction with WISE 2018, Dubai, UAE, November 12–15, 2018, Revised Selected Papers (Lecture Notes in Computer Science #11235)
by Quan Z. Sheng Rui Zhou Hakim Hacid Tetsuya Yoshida Azadeh SarkheyliThis book constitutes revised selected papers from the International Workshop on Data Quality and Trust in Big Data, QUAT 2018, which was held in conjunction with the International Conference on Web Information Systems Engineering, WISE 2018, in Dubai, UAE, in November 2018. The 9 papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with novel ideas and solutions related to the problems of exploring, assessing, monitoring, improving, and maintaining the quality of data and trust for Big Data.
Data Quality in the Age of AI: Building a foundation for AI strategy and data culture
by Andrew JonesUnlock the power of data with expert insights to enhance data quality, maximizing the potential of AI, and establishing a data-centric cultureKey FeaturesGain a profound understanding of the interplay between data quality and AIExplore strategies to improve data quality with practical implementation and real-world resultsAcquire the skills to measure and evaluate data quality, empowering data-driven decisionsPurchase of the Kindle book includes a free PDF eBookBook DescriptionAs organizations worldwide seek to revamp their data strategies to leverage AI advancements and benefit from newfound capabilities, data quality emerges as the cornerstone for success. Without high-quality data, even the most advanced AI models falter. Enter Data Quality in the Age of AI, a detailed report that illuminates the crucial role of data quality in shaping effective data strategies. Packed with actionable insights, this report highlights the critical role of data quality in your overall data strategy. It equips teams and organizations with the knowledge and tools to thrive in the evolving AI landscape, serving as a roadmap for harnessing the power of data quality, enabling them to unlock their data's full potential, leading to improved performance, reduced costs, increased revenue, and informed strategic decisions.What you will learnDiscover actionable steps to establish data quality as the foundation of your data cultureEnhance data quality directly at its source with effective strategies and best practicesElevate data quality standards and enhance data literacy within your organizationIdentify and measure data quality within the datasetAdopt a product mindset to address data quality challengesExplore emerging architectural patterns like data mesh and data contractsAssign roles, responsibilities, and incentives for data generatorsGain insights from real-world case studiesWho this book is forThis report is for data leaders and decision-makers, including CTOs, CIOs, CISOs, CPOs, and CEOs responsible for shaping their organization's data strategy to maximize data value, especially those interested in harnessing recent AI advancements.
Data Quality: Empowering Businesses with Analytics and AI
by Prashanth SouthekalDiscover how to achieve business goals by relying on high-quality, robust data In Data Quality: Empowering Businesses with Analytics and AI, veteran data and analytics professional delivers a practical and hands-on discussion on how to accelerate business results using high-quality data. In the book, you’ll learn techniques to define and assess data quality, discover how to ensure that your firm’s data collection practices avoid common pitfalls and deficiencies, improve the level of data quality in the business, and guarantee that the resulting data is useful for powering high-level analytics and AI applications. The author shows you how to: Profile for data quality, including the appropriate techniques, criteria, and KPIs Identify the root causes of data quality issues in the business apart from discussing the 16 common root causes that degrade data quality in the organization. Formulate the reference architecture for data quality, including practical design patterns for remediating data quality Implement the 10 best data quality practices and the required capabilities for improving operations, compliance, and decision-making capabilities in the businessAn essential resource for data scientists, data analysts, business intelligence professionals, chief technology and data officers, and anyone else with a stake in collecting and using high-quality data, Data Quality: Empowering Businesses with Analytics and AI will also earn a place on the bookshelves of business leaders interested in learning more about what sets robust data apart from the rest.
Data Rules: Reinventing the Market Economy (Acting with Technology)
by Jannis Kallinikos Cristina AlaimoA new social science framework for studying the unprecedented social and economic restructuring driven by digital data.Digital data have become the critical frontier where emerging economic practices and organizational forms confront the traditional economic order and its institutions. In Data Rules, Cristina Alaimo and Jannis Kallinikos establish a social science framework for analyzing the unprecedented social and economic restructuring brought about by data. Working at the intersection of information systems and organizational studies, they draw extensively on intellectual currents in sociology, semiotics, cognitive science and technology, and social theory. Making the case for turning &“data-making&” into an area of inquiry of its own, the authors uncover how data are deeply implicated in rewiring the institutions of the market economy.The authors associate digital data with the decentering of organizations. As they point out, centered systems make sense only when firms (and formal organizations more broadly) can keep the external world at arm&’s length and maintain a relative operation independence from it. These patterns no longer hold. Data transform the production of goods and services to an endless series of exchanges and interactions that defeat the functional logics of markets and organizations. The diffusion of platforms and ecosystems is indicative of these broader transformations. Rather than viewing data as simply a force of surveillance and control, the authors place the transformative potential of data at the center of an emerging socioeconomic order that restructures society and its institutions.
Data Scheduling and Transmission Strategies in Asymmetric Telecommunication Environments
by Abhishek Roy Navrati SaxenaThis book presents a framework for a new hybrid scheduling strategy for heterogeneous, asymmetric telecommunication environments. It discusses comparative advantages and disadvantages of push, pull, and hybrid transmission strategies, together with practical consideration and mathematical reasoning.
Data Science with Python: Combine Python with machine learning principles to discover hidden patterns in raw data
by Rohan Chopra Aaron England Mohamed Noordeen AlaudeenLeverage the power of the Python data science libraries and advanced machine learning techniques to analyse large unstructured datasets and predict the occurrence of a particular future event. Key Features Explore the depths of data science, from data collection through to visualization Learn pandas, scikit-learn, and Matplotlib in detail Study various data science algorithms using real-world datasets Book Description Data Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression. As you make your way through chapters, you will study the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, study how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome. By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book. What you will learn Pre-process data to make it ready to use for machine learning Create data visualizations with Matplotlib Use scikit-learn to perform dimension reduction using principal component analysis (PCA) Solve classification and regression problems Get predictions using the XGBoost library Process images and create machine learning models to decode them Process human language for prediction and classification Use TensorBoard to monitor training metrics in real time Find the best hyperparameters for your model with AutoML Who this book is for Data Science with Python is designed for data analysts, data scientists, database engineers, and business analysts who want to move towards using Python and machine learning techniques to analyze data and predict outcomes. Basic knowledge of Python and data analytics will prove beneficial to understand the various concepts explained through this book.
Data Science & Exploration in Artificial Intelligence: Proceedings of the First International Conference On Data Science & Exploration in Artificial Intelligence (CODE-AI 2024) Bangalore, India, 3rd- 4th July, 2024 (Volume 1)
by Francesco Flammini H. L. Gururaj J. ShreyasThe book captures the essence of the International Conference on Data Science & Exploration in Artificial Intelligence and offers a comprehensive exploration of cutting-edge research in AI, data science, and their applications.It covers a wide array of topics including advanced Data Science, IoT, Security, Cloud Computing, Networks, Security, Image, Video and Signal Processing, Computational Biology, Computer and Information Technology. It highlights innovative research contributions and practical applications, offering readers a detailed understanding of current trends and challenges. The findings emphasize the role of global collaboration and interdisciplinary approaches in pushing the boundaries of AI and data science. Selected papers published by Taylor and Francis showcase pioneering work that is shaping the future of these fields.This is an ideal read for AI and data science researchers, industry professionals, and students seeking to stay updated on the latest advancements and ethical considerations in these areas.
Data Science & Exploration in Artificial Intelligence: Proceedings of the First International Conference On Data Science & Exploration in Artificial Intelligence (CODE-AI 2024) Bangalore, India, 3rd- 4th July, 2024 (Volume 2)
by Francesco Flammini H. L. Gururaj J. ShreyasThe book captures the essence of the International Conference on Data Science & Exploration in Artificial Intelligence and offers a comprehensive exploration of cutting-edge research in AI, data science, and their applications.It covers a wide array of topics including advanced Data Science, IoT, Security, Cloud Computing, Networks, Security, Image, Video and Signal Processing, Computational Biology, Computer and Information Technology. It highlights innovative research contributions and practical applications, offering readers a detailed understanding of current trends and challenges. The findings emphasize the role of global collaboration and interdisciplinary approaches in pushing the boundaries of AI and data science. Selected papers published by Taylor and Francis showcase pioneering work that is shaping the future of these fields.This is an ideal read for AI and data science researchers, industry professionals, and students seeking to stay updated on the latest advancements and ethical considerations in these areas.
Data Science (The MIT Press Essential Knowledge series)
by John D. Kelleher Brendan TierneyA concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges.It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.
Data Science Algorithms in a Week
by David NatinggaBuild strong foundation of machine learning algorithms In 7 days. About This Book • Get to know seven algorithms for your data science needs in this concise, insightful guide • Ensure you're confident in the basics by learning when and where to use various data science algorithms • Learn to use machine learning algorithms in a period of just 7 days Who This Book Is For This book is for aspiring data science professionals who are familiar with Python and have a statistics background. It is ideal for developers who are currently implementing one or two data science algorithms and want to learn more to expand their skill set. What You Will Learn • Find out how to classify using Naive Bayes, Decision Trees, and Random Forest to achieve accuracy to solve complex problems • Identify a data science problem correctly and devise an appropriate prediction solution using Regression and Time-series • See how to cluster data using the k-Means algorithm • Get to know how to implement the algorithms efficiently in the Python and R languages In Detail Machine learning applications are highly automated and self-modifying, and they continue to improve over time with minimal human intervention as they learn with more data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed that solve these problems perfectly. Data science helps you gain new knowledge from existing data through algorithmic and statistical analysis. This book will address the problems related to accurate and efficient data classification and prediction. Over the course of 7 days, you will be introduced to seven algorithms, along with exercises that will help you learn different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. You will then find out how to predict data based on the existing trends in your datasets. This book covers algorithms such as: k-Nearest Neighbors, Naive Bayes, Decision Trees, Random Forest, k-Means, Regression, and Time-series. On completion of the book, you will understand which machine learning algorithm to pick for clustering, classification, or regression and which is best suited for your problem. Style and approach Machine learning applications are highly automated and self-modifying which continue to improve over time with minimal human intervention as they learn with more data. To address the complex nature of various real world data problems, specialized machine learning algorithms have been developed that solve these problems perfectly.
Data Science Algorithms in a Week: Top 7 algorithms for scientific computing, data analysis, and machine learning, 2nd Edition
by Dávid NatinggaBuild a strong foundation of machine learning algorithms in 7 daysKey FeaturesUse Python and its wide array of machine learning libraries to build predictive models Learn the basics of the 7 most widely used machine learning algorithms within a weekKnow when and where to apply data science algorithms using this guideBook DescriptionMachine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well.Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis.By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problemWhat you will learnUnderstand how to identify a data science problem correctlyImplement well-known machine learning algorithms efficiently using PythonClassify your datasets using Naive Bayes, decision trees, and random forest with accuracyDevise an appropriate prediction solution using regressionWork with time series data to identify relevant data events and trendsCluster your data using the k-means algorithmWho this book is forThis book is for aspiring data science professionals who are familiar with Python and have a little background in statistics. You’ll also find this book useful if you’re currently working with data science algorithms in some capacity and want to expand your skill set
Data Science Analytics and Applications: First International Conference, Dasaa 2017, Chennai, India, January 4-6, 2017, Revised Selected Papers (Communications In Computer And Information Science #804)
by Shriram R Mak SharmaThis 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.
Data Science Bookcamp: Five real-world Python projects
by Leonard ApeltsinLearn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease outbreaks, and more, as you build a flexible and intuitive understanding of data science.In Data Science Bookcamp you will learn: - Techniques for computing and plotting probabilities - Statistical analysis using Scipy - How to organize datasets with clustering algorithms - How to visualize complex multi-variable datasets - How to train a decision tree machine learning algorithm In Data Science Bookcamp you&’ll test and build your knowledge of Python with the kind of open-ended problems that professional data scientists work on every day. Downloadable data sets and thoroughly-explained solutions help you lock in what you&’ve learned, building your confidence and making you ready for an exciting new data science career. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology A data science project has a lot of moving parts, and it takes practice and skill to get all the code, algorithms, datasets, formats, and visualizations working together harmoniously. This unique book guides you through five realistic projects, including tracking disease outbreaks from news headlines, analyzing social networks, and finding relevant patterns in ad click data. About the book Data Science Bookcamp doesn&’t stop with surface-level theory and toy examples. As you work through each project, you&’ll learn how to troubleshoot common problems like missing data, messy data, and algorithms that don&’t quite fit the model you&’re building. You&’ll appreciate the detailed setup instructions and the fully explained solutions that highlight common failure points. In the end, you&’ll be confident in your skills because you can see the results. What's inside - Web scraping - Organize datasets with clustering algorithms - Visualize complex multi-variable datasets - Train a decision tree machine learning algorithm About the reader For readers who know the basics of Python. No prior data science or machine learning skills required. About the author Leonard Apeltsin is the Head of Data Science at Anomaly, where his team applies advanced analytics to uncover healthcare fraud, waste, and abuse. Table of Contents CASE STUDY 1 FINDING THE WINNING STRATEGY IN A CARD GAME 1 Computing probabilities using Python 2 Plotting probabilities using Matplotlib 3 Running random simulations in NumPy 4 Case study 1 solution CASE STUDY 2 ASSESSING ONLINE AD CLICKS FOR SIGNIFICANCE 5 Basic probability and statistical analysis using SciPy 6 Making predictions using the central limit theorem and SciPy 7 Statistical hypothesis testing 8 Analyzing tables using Pandas 9 Case study 2 solution CASE STUDY 3 TRACKING DISEASE OUTBREAKS USING NEWS HEADLINES 10 Clustering data into groups 11 Geographic location visualization and analysis 12 Case study 3 solution CASE STUDY 4 USING ONLINE JOB POSTINGS TO IMPROVE YOUR DATA SCIENCE RESUME 13 Measuring text similarities 14 Dimension reduction of matrix data 15 NLP analysis of large text datasets 16 Extracting text from web pages 17 Case study 4 solution CASE STUDY 5 PREDICTING FUTURE FRIENDSHIPS FROM SOCIAL NETWORK DATA 18 An introduction to graph theory and network analysis 19 Dynamic graph theory techniques for node ranking and social network analysis 20 Network-driven supervised machine learning 21 Training linear classifiers with logistic regression 22 Training nonlinear classifiers with decision tree techniques 23 Case study 5 solution
Data Science Careers, Training, and Hiring: A Comprehensive Guide to the Data Ecosystem: How to Build a Successful Data Science Career, Program, or Unit (SpringerBriefs in Computer Science)
by Renata Rawlings-GossThis book is an information packed overview of how to structure a data science career, a data science degree program, and how to hire a data science team, including resources and insights from the authors experience with national and international large-scale data projects as well as industry, academic and government partnerships, education, and workforce. Outlined here are tips and insights into navigating the data ecosystem as it currently stands, including career skills, current training programs, as well as practical hiring help and resources. Also, threaded through the book is the outline of a data ecosystem, as it could ultimately emerge, and how career seekers, training programs, and hiring managers can steer their careers, degree programs, and organizations to align with the broader future of data science. Instead of riding the current wave, the author ultimately seeks to help professionals, programs, and organizations alike prepare a sustainable plan for growth in this ever-changing world of data. The book is divided into three sections, the first “Building Data Careers”, is from the perspective of a potential career seeker interested in a career in data, the second “Building Data Programs” is from the perspective of a newly forming data science degree or training program, and the third “Building Data Talent and Workforce” is from the perspective of a Data and Analytics Hiring Manager. Each is a detailed introduction to the topic with practical steps and professional recommendations. The reason for presenting the book from different points of view is that, in the fast-paced data landscape, it is helpful to each group to more thoroughly understand the desires and challenges of the other. It will, for example, help the career seekers to understand best practices for hiring managers to better position themselves for jobs. It will be invaluable for data training programs to gain the perspective of career seekers, who they want to help and attract as students. Also, hiring managers will not only need data talent to hire, but workforce pipelines that can only come from partnerships with universities, data training programs, and educational experts. The interplay gives a broader perspective from which to build.
Data Science Concepts and Techniques with Applications
by Muhammad Summair Raza Usman QamarThis 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.
Data Science Concepts and Techniques with Applications
by Muhammad Summair Raza Usman QamarThis textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general 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 presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared to the first edition, is devoted to various techniques and tools applied in data science. Its chapters 4 to 10 detail data pre-processing, classification, clustering, text mining, deep learning, frequent pattern mining, and regression analysis. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main data science programming languages, and shows in a completely new chapter practical data science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different machine learning and data mining tasks. An appendix explaining the basic mathematical concepts of data science completes the book. This textbook is suitable for advanced undergraduate and graduate students as well as for industrial practitioners who carry out research in data science. They both will not only benefit from the comprehensive presentation of important topics, but also from the many application examples and the comprehensive list of further readings, which point to additional publications providing more in-depth research results or provide sources for a more detailed description of related topics. "This book delivers a systematic, carefully thoughtful material on Data Science." from the Foreword by Witold Pedrycz, U Alberta, Canada.