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| data science hierarchy of needs: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. |
| data science hierarchy of needs: Data Science Without Makeup Mikhail Zhilkin, 2021-11-01 Mikhail Zhilkin, a data scientist who has worked on projects ranging from Candy Crush games to Premier League football players’ physical performance, shares his strong views on some of the best and, more importantly, worst practices in data analytics and business intelligence. Why data science is hard, what pitfalls analysts and decision-makers fall into, and what everyone involved can do to give themselves a fighting chance—the book examines these and other questions with the skepticism of someone who has seen the sausage being made. Honest and direct, full of examples from real life, Data Science Without Makeup: A Guidebook for End-Users, Analysts and Managers will be of great interest to people who aspire to work with data, people who already work with data, and people who work with people who work with data—from students to professional researchers and from early-career to seasoned professionals. Mikhail Zhilkin is a data scientist at Arsenal FC. He has previously worked on the popular Candy Crush mobile games and in sports betting. |
| data science hierarchy of needs: Data Feminism Catherine D'Ignazio, Lauren F. Klein, 2020-03-31 A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed. |
| data science hierarchy of needs: The Humachine Nada R. Sanders, John D. Wood, 2019-09-09 There is a lot of hype, hand-waving, and ink being spilled about artificial intelligence (AI) in business. The amount of coverage of this topic in the trade press and on shareholder calls is evidence of a large change currently underway. It is awesome and terrifying. You might think of AI as a major environmental factor that is creating an evolutionary pressure that will force enterprise to evolve or perish. For those companies that do survive the silicon wave sweeping through the global economy, the issue becomes how to keep their humanity amidst the tumult. What started as an inquiry into how executives can adopt AI to harness the best of human and machine capabilities turned into a much more profound rumination on the future of humanity and enterprise. This is a wake-up call for business leaders across all sectors of the economy. Not only should you implement AI regardless of your industry, but once you do, you should fight to stay true to your purpose, your ethical convictions, indeed your humanity, even as our organizations continue to evolve. While not holding any punches about the dangers posed by overpowered AI, this book uniquely surveys where technology is limited, and gives reason for cautious optimism about the true opportunities that lie amidst all the disruptive change currently underway. As such, it is distinctively more optimistic than many of the competing titles on Big Technology. This compelling book weaves together business strategy and philosophy of mind, behavioral psychology and the limits of technology, leadership and law. The authors set out to identify where humans and machines can best complement one another to create an enterprise greater than the sum total of its parts: the Humachine. Combining the global business and forecasting acumen of Professor Nada R. Sanders, PhD, with the legal and philosophical insight of John D. Wood, Esq., the authors combine their strengths to bring us this profound yet accessible book. This is a must read for anyone interested in AI and the future of human enterprise. |
| data science hierarchy of needs: Fundamentals of Data Engineering Joe Reis, Matt Housley, 2022-06-22 Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, and governance that are critical in any data environment regardless of the underlying technology. This book will help you: Get a concise overview of the entire data engineering landscape Assess data engineering problems using an end-to-end framework of best practices Cut through marketing hype when choosing data technologies, architecture, and processes Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle |
| data science hierarchy of needs: Agile Data Science Russell Jurney, 2013-10-15 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 hierarchy of needs: Agile Data Science 2.0 Russell Jurney, 2017-06-07 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 |
| data science hierarchy of needs: Data Science and Machine Learning Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman, 2019-11-20 Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code |
| data science hierarchy of needs: Agile Data Science 2.0 Russell Jurney, 2017-06-07 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 |
| data science hierarchy of needs: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| data science hierarchy of needs: Strategic Analytics: The Insights You Need from Harvard Business Review Harvard Business Review, Eric Siegel, Edward L. Glaeser, Cassie Kozyrkov, Thomas H. Davenport, 2020-04-21 Is your company ready for the next wave of analytics? Data analytics offer the opportunity to predict the future, use advanced technologies, and gain valuable insights about your business. But unless you're staying on top of the latest developments, your company is wasting that potential--and your competitors will be gaining speed while you fall behind. Strategic Analytics: The Insights You Need from Harvard Business Review will provide you with today's essential thinking about what data analytics are capable of, what critical talents your company needs to reap their benefits, and how to adopt analytics throughout your organization--before it's too late. Business is changing. Will you adapt or be left behind? Get up to speed and deepen your understanding of the topics that are shaping your company's future with the Insights You Need from Harvard Business Review series. Featuring HBR's smartest thinking on fast-moving issues--blockchain, cybersecurity, AI, and more--each book provides the foundational introduction and practical case studies your organization needs to compete today and collects the best research, interviews, and analysis to get it ready for tomorrow. You can't afford to ignore how these issues will transform the landscape of business and society. The Insights You Need series will help you grasp these critical ideas--and prepare you and your company for the future. |
| data science hierarchy of needs: Agile Data Science Russell Jurney, 2013-10-15 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 hierarchy of needs: A Theory of Human Motivation Abraham Maslow, 2013 |
| data science hierarchy of needs: The Oxford Handbook of Supply Chain Management Thomas Y. Choi, Julie Juan Li, Dale S. Rogers, 2021-09-13 Abstract: Supply chain management contends with structures and processes for delivering goods and services to customers. It addresses the core functions of connected businesses to meet downstream demand. This innovative volume provides an authoritative and timely guide to the overarching issues that are ubiquitous throughout the supply chain. In particular, it addresses emerging issues that are applicable across supply chains-such as data science, financial flows, human capital, internet technologies, risk management, cyber security, and supply networks. With chapters from an international roster of leading scholars in the field, The Oxford Handbook of Supply Chain Management is a necessary resource for all students and researchers of the field as well as for forward-thinking practitioners. Keywords: supply chain management; value; human society; goods and services; competitive advantage; people and welfare; data and technology; moving goods and services; structure and strategy; growing and sustaining-- |
| data science hierarchy of needs: Data Science and Machine Learning for Non-Programmers Dothang Truong, 2024-02-23 As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize machine learning effectively. Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders. Designed as a guiding companion for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers, and industry professionals from various backgrounds. |
| data science hierarchy of needs: Practical DataOps Harvinder Atwal, 2019-12-09 Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles. This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. What You Will LearnDevelop a data strategy for your organization to help it reach its long-term goals Recognize and eliminate barriers to delivering data to users at scale Work on the right things for the right stakeholders through agile collaboration Create trust in data via rigorous testing and effective data management Build a culture of learning and continuous improvement through monitoring deployments and measuring outcomes Create cross-functional self-organizing teams focused on goals not reporting lines Build robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data products Who This Book Is For Data science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production. |
| data science hierarchy of needs: Confident Data Skills Kirill Eremenko, 2020-09-10 Data has dramatically changed how our world works. Understanding and using data is now one of the most transferable and desirable skills. Whether you're an entrepreneur wanting to boost your business, a jobseeker looking for that employable edge, or simply hoping to make the most of your current career, Confident Data Skills is here to help. This updated second edition takes you through the basics of data: from data mining and preparing and analysing your data, to visualizing and communicating your insights. It now contains exciting new content on neural networks and deep learning. Featuring in-depth international case studies from companies including Amazon, LinkedIn and Mike's Hard Lemonade Co, as well as easy-to understand language and inspiring advice and guidance, Confident Data Skills will help you use your new-found data skills to give your career that cutting-edge boost. About the Confident series... From coding and web design to data, digital content and cyber security, the Confident books are the perfect beginner's resource for enhancing your professional life, whatever your career path. |
| data science hierarchy of needs: Automating Data Quality Monitoring Jeremy Stanley, Paige Schwartz, 2024-01-09 The world's businesses ingest a combined 2.5 quintillion bytes of data every day. But how much of this vast amount of data--used to build products, power AI systems, and drive business decisions--is poor quality or just plain bad? This practical book shows you how to ensure that the data your organization relies on contains only high-quality records. Most data engineers, data analysts, and data scientists genuinely care about data quality, but they often don't have the time, resources, or understanding to create a data quality monitoring solution that succeeds at scale. In this book, Jeremy Stanley and Paige Schwartz from Anomalo explain how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on every category of issue, and resolve problems immediately. This book will help you: Learn why data quality is a business imperative Understand and assess unsupervised learning models for detecting data issues Implement notifications that reduce alert fatigue and let you triage and resolve issues quickly Integrate automated data quality monitoring with data catalogs, orchestration layers, and BI and ML systems Understand the limits of automated data quality monitoring and how to overcome them Learn how to deploy and manage your monitoring solution at scale Maintain automated data quality monitoring for the long term |
| data science hierarchy of needs: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-06 Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder |
| data science hierarchy of needs: Minding the Machines Jeremy Adamson, 2021-06-25 Organize, plan, and build an exceptional data analytics team within your organization In Minding the Machines: Building and Leading Data Science and Analytics Teams, AI and analytics strategy expert Jeremy Adamson delivers an accessible and insightful roadmap to structuring and leading a successful analytics team. The book explores the tasks, strategies, methods, and frameworks necessary for an organization beginning their first foray into the analytics space or one that is rebooting its team for the umpteenth time in search of success. In this book, you’ll discover: A focus on the three pillars of strategy, process, and people and their role in the iterative and ongoing effort of building an analytics team Repeated emphasis on three guiding principles followed by successful analytics teams: start early, go slow, and fully commit The importance of creating clear goals and objectives when creating a new analytics unit in an organization Perfect for executives, managers, team leads, and other business leaders tasked with structuring and leading a successful analytics team, Minding the Machines is also an indispensable resource for data scientists and analysts who seek to better understand how their individual efforts fit into their team’s overall results. |
| data science hierarchy of needs: Causation in Population Health Informatics and Data Science Olaf Dammann, Benjamin Smart, 2018-10-29 Marketing text: This book covers the overlap between informatics, computer science, philosophy of causation, and causal inference in epidemiology and population health research. Key concepts covered include how data are generated and interpreted, and how and why concepts in health informatics and the philosophy of science should be integrated in a systems-thinking approach. Furthermore, a formal epistemology for the health sciences and public health is suggested. Causation in Population Health Informatics and Data Science provides a detailed guide of the latest thinking on causal inference in population health informatics. It is therefore a critical resource for all informaticians and epidemiologists interested in the potential benefits of utilising a systems-based approach to causal inference in health informatics. |
| data science hierarchy of needs: Business Models in Emerging Technologies Stylianos Kampakis, Theodosis Mourouzis, Gerard Cardoso, Marialena Zinopoulou, 2022-09-27 This book is a practical guide to two of the most important emerging technologies: data science/AI and blockchain. The world of technology progresses so quickly that we often don’t realize how far we’ve come. Over the last 20 years, technologies like data science, artificial intelligence, the Internet of Things, and blockchain have transformed the world of business, industry, and society. These emerging technologies offer a wide range of opportunities. However, they also create new challenges businesses must face, such as developing new business models, and discovering the best adoption strategies. This book is a practical guide to two of the most important emerging technologies: data science/AI and blockchain. With broad applicability across all sectors, decision-makers would greatly benefit from understanding these fields. |
| data science hierarchy of needs: Designing Machine Learning Systems Chip Huyen, 2022-05-17 Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. This book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems |
| data science hierarchy of needs: Strategies for Attracting, Maintaining, and Balancing a Mature Workforce Hughes, Claretha, 2019-12-27 There is no end in sight as the Fourth Industrial Revolution becomes more prevalent across the world. Artificial intelligence (AI) is making it imperative that machines and technology be integrated within the workplace. As the workforce ages, there has to be a way to acquire the tacit and explicit knowledge of these workers. The fields of human resource development and workforce development must lead in efforts to train and develop these workers for continuous technological change. Strategies for Attracting, Maintaining, and Balancing a Mature Workforce is an essential reference source that examines efforts for engaging, retaining, and utilizing an aging workforce in a workplace that is increasingly becoming more technology-centered and provides reskilling and upskilling strategies to address the skills gaps. The title compiles vital human resource and workforce development strategies that assist these professionals with helping all employees at all levels within the workforce attain work, keep their jobs, and grow in their development to assist others. Featuring research on topics such as organizational culture, career learning, and agile workforce, this book is ideally designed for managers, executives, recruiters, hiring professionals, managing directors, human resources professionals, business researchers, industry professionals, academicians, and students. |
| data science hierarchy of needs: Advances on Intelligent Computing and Data Science Faisal Saeed, Fathey Mohammed, Errais Mohammed, Tawfik Al-Hadhrami, Mohammed Al-Sarem, 2023-08-16 This book presents the papers included in the proceedings of the 3rd International Conference of Advanced Computing and Informatics (ICACin’22) that was held in Casablanca, Morocco, on October 15–16, 2022. A total of 98 papers were submitted to the conference, but only 60 papers were accepted and published in this book with an acceptance rate of 61%. The book presents several hot research topics which include artificial intelligence and data science, big data analytics, Internet of Things (IoT) and smart cities, information security, cloud computing and networking, and computational informatics. |
| data science hierarchy of needs: Data Privacy Nishant Bhajaria, 2022-02-15 Privacy engineering : why it's needed, how to scale it -- Understanding data and privacy -- Data classification -- Data inventory -- Data sharing -- The technical privacy review -- Data deletion -- Exporting user data : data subject access requests -- Building a consent management platform -- Closing security vulnerabilities -- Scaling, hiring, and considering regulations. |
| data science hierarchy of needs: Google Cloud Platform for Data Engineering Alasdair Gilchrist, Google Cloud Platform for Data Engineering is designed to take the beginner through a journey to become a competent and certified GCP data engineer. The book, therefore, is split into three parts; the first part covers fundamental concepts of data engineering and data analysis from a platform and technology-neutral perspective. Reading part 1 will bring a beginner up to speed with the generic concepts, terms and technologies we use in data engineering. The second part, which is a high-level but comprehensive introduction to all the concepts, components, tools and services available to us within the Google Cloud Platform. Completing this section will provide the beginner to GCP and data engineering with a solid foundation on the architecture and capabilities of the GCP. Part 3, however, is where we delve into the moderate to advanced techniques that data engineers need to know and be able to carry out. By this time the raw beginner you started the journey at the beginning of part 1 will be a knowledgable albeit inexperienced data engineer. However, by the conclusion of part 3, they will have gained the advanced knowledge of data engineering techniques and practices on the GCP to pass not only the certification exam but also most interviews and practical tests with confidence. In short part 3, will provide the prospective data engineer with detailed knowledge on setting up and configuring DataProc - GCPs version of the Spark/Hadoop ecosystem for big data. They will also learn how to build and test streaming and batch data pipelines using pub/sub/ dataFlow and BigQuery. Furthermore, they will learn how to integrate all the ML and AI Platform components and APIs. They will be accomplished in connecting data analysis and visualisation tools such as Datalab, DataStudio and AI notebooks amongst others. They will also by now know how to build and train a TensorFlow DNN using APIs and Keras and optimise it to run large public data sets. Also, they will know how to provision and use Kubeflow and Kube Pipelines within Google Kubernetes engines to run container workloads as well as how to take advantage of serverless technologies such as Cloud Run and Cloud Functions to build transparent and seamless data processing platforms. The best part of the book though is its compartmental design which means that anyone from a beginner to an intermediate can join the book at whatever point they feel comfortable. |
| data science hierarchy of needs: Mastering Marketing Data Science Iain Brown, 2024-04-29 Unlock the Power of Data: Transform Your Marketing Strategies with Data Science In the digital age, understanding the symbiosis between marketing and data science is not just an advantage; it's a necessity. In Mastering Marketing Data Science: A Comprehensive Guide for Today's Marketers, Dr. Iain Brown, a leading expert in data science and marketing analytics, offers a comprehensive journey through the cutting-edge methodologies and applications that are defining the future of marketing. This book bridges the gap between theoretical data science concepts and their practical applications in marketing, providing readers with the tools and insights needed to elevate their strategies in a data-driven world. Whether you're a master's student, a marketing professional, or a data scientist keen on applying your skills in a marketing context, this guide will empower you with a deep understanding of marketing data science principles and the competence to apply these principles effectively. Comprehensive Coverage: From data collection to predictive analytics, NLP, and beyond, explore every facet of marketing data science. Practical Applications: Engage with real-world examples, hands-on exercises in both Python & SAS, and actionable insights to apply in your marketing campaigns. Expert Guidance: Benefit from Dr. Iain Brown's decade of experience as he shares cutting-edge techniques and ethical considerations in marketing data science. Future-Ready Skills: Learn about the latest advancements, including generative AI, to stay ahead in the rapidly evolving marketing landscape. Accessible Learning: Tailored for both beginners and seasoned professionals, this book ensures a smooth learning curve with a clear, engaging narrative. Mastering Marketing Data Science is designed as a comprehensive how-to guide, weaving together theory and practice to offer a dynamic, workbook-style learning experience. Dr. Brown's voice and expertise guide you through the complexities of marketing data science, making sophisticated concepts accessible and actionable. |
| data science hierarchy of needs: Understanding the Role of Artificial Intelligence and Its Future Social Impact Sheikh, Salim, 2020-07-17 The influence of AI is beginning to filter into every aspect of life, spanning across education, healthcare, business, and more. However, as its prevalence grows, challenges must be addressed including AI replication and even exacerbation of human bias and discrimination and the development of policies and laws that appropriately regulate AI. Stakeholders from all sectors of society need to collaborate on co-designing innovative, agile frameworks for governing AI that allow for its continued adoption while minimizing risk and reducing disruption. Understanding the Role of Artificial Intelligence and Its Future Social Impact is a pivotal reference source that provides vital research on the application of AI within contemporary society and comprehends the future effects of this technology within modern civilization. While highlighting topics such as cognitive computing, ethical issues, and robotics, this publication explores the possible consequences of AI adoption as well as its disruption within industries and emerging markets. This book is ideally designed for researchers, developers, strategists, managers, practitioners, executives, analysts, scientists, policymakers, academicians, and students seeking current research on the future of AI and its influence on the global culture and society. |
| data science hierarchy of needs: Applied Data Science Martin Braschler, Thilo Stadelmann, Kurt Stockinger, 2019-06-13 This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry. |
| data science hierarchy of needs: Non-Academic Careers for Quantitative Social Scientists Natalie Jackson, 2023-08-14 This book is a guide to non-academic careers for quantitative social scientists. Written by social science PhDs working in large corporations, non-profits, tech startups, and alt-academic positions in higher education, this book consists of more than a dozen chapters on various topics on finding rewarding careers outside the academy. Chapters are organized in three parts. Part I provides an introduction to the types of jobs available to social science PhDs, where those jobs can be found, and what the work looks like in those positions. Part II creates a guide for social science PhDs on how to set themselves up for such careers, including navigating the academic world of graduate school while contemplating non-academic options, and selling their academic experience in a non-academic setting. Part III offers perspectives on timelines for making non-academic career decisions, lifestyle differences between academia and non-academic jobs, and additional resources for those considering a non-academic route. Providing valuable insight on non-academic careers from those who have successfully made the transition, this volume will be an asset to graduate students, advisors, and recent PhDs, in quantitative social science. |
| data science hierarchy of needs: Practical MLOps Noah Gift, Alfredo Deza, 2021-09-14 Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start. You'll discover how to: Apply DevOps best practices to machine learning Build production machine learning systems and maintain them Monitor, instrument, load-test, and operationalize machine learning systems Choose the correct MLOps tools for a given machine learning task Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware |
| data science hierarchy of needs: Encyclopedia of Educational Psychology Neil J. Salkind, Kristin Rasmussen, 2008-01-17 The field of educational psychology draws from a variety of diverse disciplines including human development across the life span, measurement and statistics, learning and motivation, and teaching. And within these different disciplines, many other fields are featured including psychology, anthropology, education, sociology, public health, school psychology, counseling, history, and philosophy. In fact, when taught at the college or university level, educational psychology is an ambitious course that undertakes the presentation of many different topics all tied together by the theme of how the individual can best function in an educational setting, loosely defined as anything from pre-school through adult education. Educational psychology can be defined as the application of what we know about learning and motivation, development, and measurement and statistics to educational settings (both school- and community-based). |
| data science hierarchy of needs: The Data Science Design Manual Steven S. Skiena, 2017-07-01 This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com) |
| data science hierarchy of needs: Auditor Essentials Hernan Murdock, 2018-09-21 Internal auditors must know many concepts, techniques, control frameworks, and remain knowledgeable despite the many changes occurring in the marketplace and their profession. This easy to use reference makes this process easier and ensures auditors can obtain needed information quickly and accurately. This book consists of 100 topics, concepts, tips, tools and techniques that relate to how internal auditors interact with internal constitutencies and addresses a variety of technical and non-technical subjects. Non-auditors have an easy-to-use guide that increases their understanding of what internal auditors do and how, making it easier for them to partner with them more effectively. |
| data science hierarchy of needs: Project Management Waterfall-Agile-It-Data Science Dr. Festus Elleh PhD PMP PMI-ACP, 2023-03-22 This book is intended to introduce learners to waterfall, agile, information technology, and data science project management methodologies. Readers will learn about the concepts, processes, tools, and techniques that are useful for executing projects in waterfall, agile information technology, and data science environments. The objective is for learners to become contributors to the field of project management and deploy a structured approach to managing projects. Learners who read this book will be able to think critically about the concepts and practices of project management and perform exceptionally well in the PMP and PMI-ACP examinations. |
| data science hierarchy of needs: Artificial Intelligence for Managers Malay A. Upadhyay, 2020-09-18 Understand how to adopt and implement AI in your organizationKey Featuresa- 7 Principles of an AI Journeya- The TUSCANE Approach to Become Data Readya- The FAB-4 Model to Choose the Right AI Solutiona- Major AI Techniques & their Applications:- CART & Ensemble Learning- Clustering, Association Rules & Search- Reinforcement Learning- Natural Language Processing- Image RecognitionDescriptionMost AI initiatives in organizations fail today not because of a lack of good AI solutions, but because of a lack of understanding of AI among its end users, decision makers and investors. Today, organizations need managers who can leverage AI to solve business problems and provide a competitive advantage. This book is designed to enable you to fill that need, and create an edge for your career.The chapters offer unique managerial frameworks to guide an organization's AI journey. The first section looks at what AI is; and how you can prepare for it, decide when to use it, and avoid pitfalls on the way. The second section dives into the different AI techniques and shows you where to apply them in business. The final section then prepares you from a strategic AI leadership perspective to lead the future of organizations.By the end of the book, you will be ready to offer any organization the capability to use AI successfully and responsibly - a need that is fast becoming a necessity.What will you learna- Understand the major AI techniques & how they are used in business.a- Determine which AI technique(s) can solve your business problem.a- Decide whether to build or buy an AI solution.a- Estimate the financial value of an AI solution or company.a- Frame a robust policy to guide the responsible use of AI.Who this book is forThis book is for Executives, Managers and Students on both Business and Technical teams who would like to use Artificial Intelligence effectively to solve business problems or get an edge in their careers.Table of Contents1.Preface2.Acknowledgement3.About the Author4.Section 1: Beginning an AI Journeya. AI Fundamentalsb. 7 Principles of an AI Journeyc. Getting Ready to Use AI5.Section 2: Choosing the Right AI Techniquesa. Inside the AI Laboratoryb. How AI Predicts Values & Categoriesc. How AI Understands and Predicts Behaviors & Scenariosd. How AI Communicates & Learns from Mistakese. How AI Starts to Think Like Humans6.Section 3: Using AI Successfully & Responsiblya. AI Adoption & Valuationb. AI Strategy, Policy & Risk Management7.EpilogueAbout the AuthorsMalay A. Upadhyay is a Customer Journey executive, certified in Machine Learning. Over the course of his role heading the function at a N. American AI SaaS firm in Toronto, Malay trained 150+ N. American managers on the basics of AI and its successful adoption, held executive thought leadership sessions for CEOs and CHROs on AI strategy & IT modernization roadmaps, and worked as the primary liaison to realize AI value on unique customer datasets. It was here that he learnt the growing need for greater knowledge and awareness of how to use AI both responsibly and successfully.Malay was also one of 25 individuals chosen globally to envision the industrial future for the Marzotto Group, Italy, on its 175th anniversary. He holds an MBA, M.Sc. and B.E., with experiences across India, UAE, Italy and Canada.A Duke of Edinburgh awardee, Malay has been driving the subject of responsible AI management as an advisor, author, online instructor and member of the European AI Alliance that informed the HLEG on the European Commission's AI policy. |
| data science hierarchy of needs: The Data Science Framework Juan J. Cuadrado-Gallego, Yuri Demchenko, 2020-10-01 This edited book first consolidates the results of the EU-funded EDISON project (Education for Data Intensive Science to Open New science frontiers), which developed training material and information to assist educators, trainers, employers, and research infrastructure managers in identifying, recruiting and inspiring the data science professionals of the future. It then deepens the presentation of the information and knowledge gained to allow for easier assimilation by the reader. The contributed chapters are presented in sequence, each chapter picking up from the end point of the previous one. After the initial book and project overview, the chapters present the relevant data science competencies and body of knowledge, the model curriculum required to teach the required foundations, profiles of professionals in this domain, and use cases and applications. The text is supported with appendices on related process models. The book can be used to develop new courses in data science, evaluate existing modules and courses, draft job descriptions, and plan and design efficient data-intensive research teams across scientific disciplines. |
| data science hierarchy of needs: Strategic Risk, Intelligence And Digital Transformation Eduardo Rodriguez, 2024-03-19 In this book, the study of strategic risk is not only for its control and mitigation using analytics and digital transformation in organizations, but also it is about the strategic risks that digital transformation can bring to organizations. Strategic risk control is one of the goals in creating intelligent organizations and at the same time it is part of the appetite for creating smarter organizations to support organizations' development. Knowledge that is created by data analytics and the capacity to operationalize that knowledge through digital transformation can produce potential sustainable competitive advantages.The core of the volume is connecting data analytics and artificial intelligence, risk management and digitalization to create strategic intelligence as the capacity of adaptation that organizations need to compete and to succeed. Strategic intelligence is a symbiotic work of artificial intelligence, business intelligence and competitive intelligence. Strategic risk is represented by the probability of having variations in the performance results of the organizations that can limit their capacity to maintain sustainable competitive advantages. There is an emphasis in the book about the conversion of models that support data analytics into actions to mitigate strategic risk based on digital transformation.This book reviews the steps that organizations have taken in using technology that connects the data analytics modeling process and digital operations, such as the shift from the use of statistical learning and machine learning for data analytics to the improvement and use of new technologies. The digitalization process is a potential opportunity for organizations however the results are not necessarily good for everyone. Hence, organizations implement strategic risk control in cloud computing, blockchain, artificial intelligence and create digital networks that are connected internally and externally to deal with internal and external customers, with suppliers and buyers, and with competitors and substitutes. The new risks appear once new knowledge emerges and is in use, but at the same time the new knowledge supports the initiatives to deal with risks arising from novel ways of competing and collaborating. |
| data science hierarchy of needs: Data Science Class 8 VK Jain, 2021-10-11 TAGLINE Data Science is a multidisciplinary field that also interacts with various other technologies like Artificial Intelligence, Machine Learning, Deep Learning, the Internet of Things, etc. KEY FEATURES ● National Education Policy 2020 ● Activity: This section contains a topic based practical activity for the students to explore and learn. ● Higher Order Thinking Skills: This section contains the questions that are out of the box and helps the learner to think differently. ● Glossary: This section contains definition of common data science terms. ● Applied Project: This section contains an activity that applies the concepts of the chapter in real-life. ● Digital Solutions DESCRIPTION “Touchpad” Data Science book is designed as per the latest CBSE curriculum with an inter-disciplinary approach towards Mathematics, Statistics and Information Technology. The book inculcates real-life scenarios to explain the concepts and helps the students become better Data Science literates and pursue future endeavours confidently. To enrich the subject, this book contains different types of exercises like Objective Type Questions, Standard Questions and Higher Order Thinking Skills (HOTS). This book also includes Do You Know? and Activity which helps the students to learn and think outside the box. It helps the students to think and not just memorize, at the same time improving their cognitive ability. WHAT WILL YOU LEARN You will learn about: ● Data ● Data Science ● Data Visualisation ● Data Science and Artificial Intelligence WHO THIS BOOK IS FOR Grade - 8 TABLE OF CONTENTS 1. Introduction to Data 2. Introduction to Data Science 3. Data Visualisation 4. Data Science and Artificial Intelligence 5. Projects 6. Glossary |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
