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| data science accelerator program: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data science accelerator program: Data Science for Entrepreneurship Werner Liebregts, Willem-Jan van den Heuvel, Arjan van den Born, 2023-03-23 The fast-paced technological development and the plethora of data create numerous opportunities waiting to be exploited by entrepreneurs. This book provides a detailed, yet practical, introduction to the fundamental principles of data science and how entrepreneurs and would-be entrepreneurs can take advantage of it. It walks the reader through sections on data engineering, and data analytics as well as sections on data entrepreneurship and data use in relation to society. The book also offers ways to close the research and practice gaps between data science and entrepreneurship. By having read this book, students of entrepreneurship courses will be better able to commercialize data-driven ideas that may be solutions to real-life problems. Chapters contain detailed examples and cases for a better understanding. Discussion points or questions at the end of each chapter help to deeply reflect on the learning material. |
| data science accelerator program: FOUNDATION OF DATA SCIENCE Dr. Santosh Kumar Sahu, Dr. Herison Surbakti, Ismail Keshta, Dr. Haewon Byeon, 2023-08-21 The 1960s saw the beginning of computer science as an academic field of study. The programming languages, compilers, and operating systems, as well as the mathematical theory that underpinned these fields, were the primary focuses of this course. Finite automata, regular expressions, context-free languages, and computability were some of the topics that were addressed in theoretical computer science courses. In the 1970s, the study of algorithms became an essential component of theory when it had previously been neglected. The goal was to find practical applications for computers. At this time, a significant shift is taking place, and more attention is being paid to the diverse range of applications. This shift came about for a variety of different causes. The convergence of computer and communication technologies has been a significant contributor to this change. Our current conception of data and how best to work with it in a contemporary environment has to be revised in light of recent advances in the capacity to monitor, collect, and store data in a variety of domains, including the natural sciences, business, and other areas. The rise of the internet and social networks as fundamental components of everyday life carries with it a wealth of theoretical possibilities as well as difficulties. Traditional subfields of computer science continue to hold a significant amount of weight in the field as a whole, but researchers of the future will focus more on how to use computers to comprehend and extract usable information from massive amounts of data arising from applications rather than how to make computers useful for solving particular problems in a well-defined manner. With this in mind, we have prepared this book to cover the theory that we anticipate will be important in the next 40 years, in the same way that a grasp of automata theory, algorithms, and other similar areas provided students an advantage in the previous 40 years. An increased focus on probability, statistical approaches, and numerical methods is one of the key shifts that has taken place. The book's early draughts have been assigned reading at a variety of academic levels, from undergraduate to graduate. The appendix contains the necessary background information for a course taken at the 1 | P a ge undergraduate level. Because of this, the appendix contains problems for your homework. |
| data science accelerator program: INTRODUCTION TO DATA SCIENCE Dr. Sushil Dohare, Dr. V SelvaKumar, Sachin Raval, Dr. Sumegh Shrikant Tharewal, 2023-04-06 The response to this inquiry is not at all easy to comprehend. I'm not sure how simple it is to discover someone who has a complete comprehension of what data science is, but I am certain that it would be challenging to locate two individuals who have fewer than three points of view on the topic. I am not sure how simple it is to locate someone who is well-versed in all aspects of what data science entails. Finding a person who is well-versed in all facets of data science may not be as simple as it initially appears to be. I cannot give you a definite answer. It's safe to say that it's a buzzword, and it seems like every data scientist desires it these days; as a result, having a background in data science is a useful thing to add to a résumé. Because of this, the role of data scientist has become increasingly common. But what exactly does it mean? Because I am unable to provide you with a definition that the vast majority of people will comprehend, I will instead provide you with the definition that I personally employ: The branch of study known as Data Science concentrates on the process of deriving information from other types of information that has been gathered. Data This description touches on so many different areas and almost encompasses so much ground that it is almost incomprehensible. It's not a mystery to me at all. Having said that, I believe that the discipline of data science encompasses a huge breadth of subject areas and subfields. There is nothing that makes me feel less ashamed than that. It is possible that the purpose of any scientific endeavor is to gather information from the evidence that has been gathered, and you may be correct if you argue this point. On the other hand, I would contend that the scientific approach entails more than simply transforming unprocessed data into information that can be understood. This is what I refer to when I make a declaration like this. |
| data science accelerator program: Data Accelerator for AI and Analytics Simon Lorenz, Gero Schmidt, TJ Harris, Mike Knieriemen, Nils Haustein, Abhishek Dave, Venkateswara Puvvada, Christof Westhues, IBM Redbooks, 2021-01-20 This IBM® Redpaper publication focuses on data orchestration in enterprise data pipelines. It provides details about data orchestration and how to address typical challenges that customers face when dealing with large and ever-growing amounts of data for data analytics. While the amount of data increases steadily, artificial intelligence (AI) workloads must speed up to deliver insights and business value in a timely manner. This paper provides a solution that addresses these needs: Data Accelerator for AI and Analytics (DAAA). A proof of concept (PoC) is described in detail. This paper focuses on the functions that are provided by the Data Accelerator for AI and Analytics solution, which simplifies the daily work of data scientists and system administrators. This solution helps increase the efficiency of storage systems and data processing to obtain results faster while eliminating unnecessary data copies and associated data management. |
| data science accelerator program: Data Science on AWS Chris Fregly, Antje Barth, 2021-04-07 With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more |
| data science accelerator program: Data Science and Innovations for Intelligent Systems Kavita Taneja, Harmunish Taneja, Kuldeep Kumar, Arvind Selwal, Eng Lieh Ouh, 2021-09-30 Data science is an emerging field and innovations in it need to be explored for the success of society 5.0. This book not only focuses on the practical applications of data science to achieve computational excellence, but also digs deep into the issues and implications of intelligent systems. This book highlights innovations in data science to achieve computational excellence that can optimize performance of smart applications. The book focuses on methodologies, framework, design issues, tools, architectures, and technologies necessary to develop and understand data science and its emerging applications in the present era. Data Science and Innovations for Intelligent Systems: Computational Excellence and Society 5.0 is useful for the research community, start-up entrepreneurs, academicians, data-centered industries, and professeurs who are interested in exploring innovations in varied applications and the areas of data science. |
| data science accelerator program: DATA SCIENCE: FOUNDATION & FUNDAMENTALS Mr. Ramkumar A, Dr. Haewon Byeon, Mohit Tiwari, Dr. Santosh Kumar Sahu, 2023-08-21 The academic field of computer science did not develop as a separate subject of study until the 1960s after it had been in existence since the 1950s. The mathematical theory that underpinned the fields of computer programming, compilers, and operating systems was one of the primary focuses of this class. Other important topics were the various programming languages and operating systems. Context-free languages, finite automata, regular expressions, and computability were a few of the topics that were discussed in theoretical computer science lectures. The area of study known as algorithmic analysis became an essential component of theory in the 1970s, after having been mostly overlooked for the majority of its existence up to that point in time. The purpose of this initiative was to investigate and identify practical applications for computer technology. At the time, a significant change is taking place, and a greater amount of attention is being paid to the vast number of different applications that may be utilized. This shift is the cumulative effect of several separate variables coming together at the same time. The convergence of computing and communication technology has been a major motivator, and as a result, this change may be primarily attributed to that convergence. Our current knowledge of data and the most effective approach to work with it in the modern world has to be revised in light of recent advancements in the capability to monitor, collect, and store data in a variety of fields, including the natural sciences, business, and other fields. This is necessary because of the recent breakthroughs in these capabilities. This is as a result of recent advancements that have been made in these capacities. The widespread adoption of the internet and other forms of social networking as indispensable components of people's lives brings with it a variety of opportunities for theoretical development as well as difficulties in actual use. Traditional subfields of computer science continue to hold a significant amount of weight in the field as a whole; however, researchers of the future will focus more on how to use computers to comprehend and extract usable information from massive amounts of data arising from applications rather than how to make computers useful for solving particular problems in a well-defined manner. This shift in emphasis is due to the fact that researchers of 1 | P a ge the future will be more concerned with how to use computers to comprehend and extract usable information from massive amounts of data arising from applications. This shift in emphasis is because researchers of the future will be more concerned with how to use the information they find. As a result of this, we felt it necessary to compile this book, which discusses a theory that would, according to our projections, play an important role within the next 40 years. We think that having a grasp of this issue will provide students with an advantage in the next 40 years, in the same way that having an understanding of automata theory, algorithms, and other topics of a similar sort provided students an advantage in the 40 years prior to this one, and in the 40 years after this one. A movement toward placing a larger emphasis on probabilities, statistical approaches, and numerical processes is one of the most significant shifts that has taken place as a result of the developments that have taken place. Early drafts of the book have been assigned reading at a broad variety of academic levels, ranging all the way from the undergraduate level to the graduate level. The information that is expected to have been learned before for a class that is taken at the undergraduate level may be found in the appendix. As a result of this, the appendix will provide you with some activities to do as a component of your project. |
| data science accelerator program: Harnessing the Potential of Big Data in Post-Pandemic Southeast Asia Asian Development Bank, 2022-05-01 This report illustrates why Southeast Asian countries need big data for pandemic recovery to radically transform the delivery of key services such as health care, social welfare and protection, and education. The final of a four-part series, it looks at the impact of COVID-19 on Cambodia, Indonesia, Myanmar, the Philippines, and Thailand to determine how big data could be an invaluable tool to help governments analyze the challenges they face. It outlines policy reforms and recommendations to help capture the benefits of big data. These include drawing up digital road maps, improving technical infrastructure, increasing data quality, and ramping up training programs to create a skilled workforce to lead the digital transformation. |
| data science accelerator program: Applications of Machine Learning Prashant Johri, Jitendra Kumar Verma, Sudip Paul, 2020-05-04 This book covers applications of machine learning in artificial intelligence. The specific topics covered include human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and the social sciences, bioinformatics and robotics, etc. It also provides a broad range of techniques that can be successfully applied and adopted in different areas. Accordingly, the book offers an interesting and insightful read for scholars in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics. |
| data science accelerator program: Lean Analytics Alistair Croll, Benjamin Yoskovitz, 2024-02-23 Whether you're a startup founder trying to disrupt an industry or an entrepreneur trying to provoke change from within, your biggest challenge is creating a product people actually want. Lean Analytics steers you in the right direction. This book shows you how to validate your initial idea, find the right customers, decide what to build, how to monetize your business, and how to spread the word. Packed with more than thirty case studies and insights from over a hundred business experts, Lean Analytics provides you with hard-won, real-world information no entrepreneur can afford to go without. Understand Lean Startup, analytics fundamentals, and the data-driven mindset Look at six sample business models and how they map to new ventures of all sizes Find the One Metric That Matters to you Learn how to draw a line in the sand, so you'll know it's time to move forward Apply Lean Analytics principles to large enterprises and established products |
| data science accelerator program: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-10-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data science accelerator program: Pivoting Government through Digital Transformation Jay Liebowitz, 2023-08-16 Affecting every sector and country in the world, digital technology is changing the way citizens engage in society, companies conduct business, and governments deliver public services. The COVID-19 pandemic accelerated the pace of digitalization and exposed such vulnerabilities as inadequate infrastructure, weak regulations, and a scarcity of skilled professionals capable of digitally transforming government. Not immune to the digital revolution, governments are slowly adapting to a digital world. Governments are implementing digital solutions to deliver services to their citizens, make payments, and engage the public. Focusing on how government can transition more effectively through digital transformation, Pivoting Government Through Digital Transformation covers the following key components: Setting the stage during the Great Resignation period Filling the digital talent pipeline Best practices and vignettes for applying digital transformation in government Looking ahead towards the future Key chapter contributors from U.S. and foreign governments, as well as state and local governments, discuss how they are coping with today’s environment and how they are using digital transformation efforts to enhance their organization’s effectiveness and digital talent pipeline. With chapters on theory and practice, this groundbreaking book offers an in-depth analysis of the most innovative approaches to e-government and discusses case studies from local, state, and federal government perspectives. This is an essential guide for government employees, scholars, and regular citizens who want to make government work more effectively and democratically in the digital age. |
| data science accelerator program: Data Science Xiaohui Cheng, Weipeng Jing, Xianhua Song, Zeguang Lu, 2019-09-13 This two volume set (CCIS 1058 and 1059) constitutes the refereed proceedings of the 5th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2019 held in Guilin, China, in September 2019. The 104 revised full papers presented in these two volumes were carefully reviewed and selected from 395 submissions. The papers cover a wide range of topics related to basic theory and techniques for data science including data mining; data base; net work; security; machine learning; bioinformatics; natural language processing; software engineering; graphic images; system; education; application. |
| data science accelerator program: Hardware Accelerators in Data Centers Christoforos Kachris, Babak Falsafi, Dimitrios Soudris, 2018-08-21 This book provides readers with an overview of the architectures, programming frameworks, and hardware accelerators for typical cloud computing applications in data centers. The authors present the most recent and promising solutions, using hardware accelerators to provide high throughput, reduced latency and higher energy efficiency compared to current servers based on commodity processors. Readers will benefit from state-of-the-art information regarding application requirements in contemporary data centers, computational complexity of typical tasks in cloud computing, and a programming framework for the efficient utilization of the hardware accelerators. |
| data science accelerator program: Concise Survey of Computer Methods Peter Naur, 1974 |
| data science accelerator program: IBM Integrated Synchronization: Incremental Updates Unleashed Christian Michel, Cüneyt Göksu, Günter Schöllmann, IBM Redbooks, 2021-01-27 The IBM® Db2® Analytics Accelerator (Accelerator) is a logical extension of Db2 for IBM z/OS® that provides a high-speed query engine that efficiently and cost-effectively runs analytics workloads. The Accelerator is an integrated back-end component of Db2 for z/OS. Together, they provide a hybrid workload-optimized database management system that seamlessly manages queries that are found in transactional workloads to Db2 for z/OS and queries that are found in analytics applications to Accelerator. Each query runs in its optimal environment for maximum speed and cost efficiency. The incremental update function of Db2 Analytics Accelerator for z/OS updates Accelerator-shadow tables continually. Changes to the data in original Db2 for z/OS tables are propagated to the corresponding target tables with a high frequency and a brief delay. Query results from Accelerator are always extracted from recent, close-to-real-time data. An incremental update capability that is called IBM InfoSphere® Change Data Capture (InfoSphere CDC) is provided by IBM InfoSphere Data Replication for z/OS up to Db2 Analytics Accelerator V7.5. Since then, an extra new replication protocol between Db2 for z/OS and Accelerator that is called IBM Integrated Synchronization was introduced. With Db2 Analytics Accelerator V7.5, customers can choose which one to use. IBM Integrated Synchronization is a built-in product feature that you use to set up incremental updates. It does not require InfoSphere CDC, which is bundled with IBM Db2 Analytics Accelerator. In addition, IBM Integrated Synchronization has more advantages: Simplified administration, packaging, upgrades, and support. These items are managed as part of the Db2 for z/OS maintenance stream. Updates are processed quickly. Reduced CPU consumption on the mainframe due to a streamlined, optimized design where most of the processing is done on the Accelerator. This situation provides reduced latency. Uses IBM Z® Integrated Information Processor (zIIP) on Db2 for z/OS, which leads to reduced CPU costs on IBM Z and better overall performance data, such as throughput and synchronized rows per second. On z/OS, the workload to capture the table changes was reduced, and the remainder can be handled by zIIPs. With the introduction of an enterprise-grade Hybrid Transactional Analytics Processing (HTAP) enabler that is also known as the Wait for Data protocol, the integrated low latency protocol is now enabled to support more analytical queries running against the latest committed data. IBM Db2 for z/OS Data Gate simplifies delivering data from IBM Db2 for z/OS to IBM Cloud® Pak® for Data for direct access by new applications. It uses the special-purpose integrated synchronization protocol to maintain data currency with low latency between Db2 for z/OS and dedicated target databases on IBM Cloud Pak for Data. |
| data science accelerator program: Accelerator Health Physics H. Wade Patterson, 2012-12-02 Accelerator Health Physics tackles the importance of health physics in the field of nuclear physics, especially to those involved with the use of particle accelerators. The book first explores concepts in nuclear physics, such as fundamental particles, radiation fields, and the responses of the human body to radiation exposure. The book then shifts to its intended purpose and discusses the uses of particle accelerators and the radiation they emit; the measurement of the radiation fields - radiation detectors, the history, design, and application of accelerator shielding; and measures in the implementation of a health physics program. The text is recommended for health physicists who want to learn more about particle accelerators, their effects, and how these effects can be prevented. The book is also beneficial to physicists whose work involves particle accelerators, as the book aims to educate them about the hazards they face in the workplace. |
| data science accelerator program: Big Data Analytics for Cyber-Physical Systems Guido Dartmann, Houbing Herbert Song, Anke Schmeink, 2019-07-16 Approx.374 pages |
| data science accelerator program: An Introduction to Data Francesco Corea, 2018-11-27 This book reflects the author’s years of hands-on experience as an academic and practitioner. It is primarily intended for executives, managers and practitioners who want to redefine the way they think about artificial intelligence (AI) and other exponential technologies. Accordingly the book, which is structured as a collection of largely self-contained articles, includes both general strategic reflections and detailed sector-specific information. More concretely, it shares insights into what it means to work with AI and how to do it more efficiently; what it means to hire a data scientist and what new roles there are in the field; how to use AI in specific industries such as finance or insurance; how AI interacts with other technologies such as blockchain; and, in closing, a review of the use of AI in venture capital, as well as a snapshot of acceleration programs for AI companies. |
| data science accelerator program: The Large Hadron Collider Lyndon R. Evans, 2009-01-01 Describes the technology and engineering of the Large Hadron collider (LHC), one of the greatest scientific marvels of this young 21st century. This book traces the feat of its construction, written by the head scientists involved, placed into the context of the scientific goals and principles. |
| data science accelerator program: The Dynamical Ionosphere Massimo Materassi, Biagio Forte, Anthea J. Coster, Susan Skone, 2019-11-28 The Dynamical Ionosphere: A Systems Approach to Ionospheric Irregularity examines the Earth's ionosphere as a dynamical system with signatures of complexity. The system is robust in its overall configuration, with smooth space-time patterns of daily, seasonal and Solar Cycle variability, but shows a hierarchy of interactions among its sub-systems, yielding apparent unpredictability, space-time irregularity, and turbulence. This interplay leads to the need for constructing realistic models of the average ionosphere, incorporating the increasing knowledge and predictability of high variability components, and for addressing the difficulty of dealing with the worst cases of ionospheric disturbances, all of which are addressed in this interdisciplinary book. Borrowing tools and techniques from classical and stochastic dynamics, information theory, signal processing, fluid dynamics and turbulence science, The Dynamical Ionosphere presents the state-of-the-art in dealing with irregularity, forecasting ionospheric threats, and theoretical interpretation of various ionospheric configurations. - Presents studies addressing Earth's ionosphere as a complex dynamical system, including irregularities and radio scintillation, ionospheric turbulence, nonlinear time series analysis, space-ionosphere connection, and space-time structures - Utilizes interdisciplinary tools and techniques, such as those associated with stochastic dynamics, information theory, signal processing, fluid dynamics and turbulence science - Offers new data-driven models for different ionospheric variability phenomena - Provides a synoptic view of the state-of-the-art and most updated theoretical interpretation, results and data analysis tools of the worst case behavior in ionospheric configurations |
| data science accelerator program: Frontiers in Data Science Matthias Dehmer, Frank Emmert-Streib, 2017-10-16 Frontiers in Data Science deals with philosophical and practical results in Data Science. A broad definition of Data Science describes the process of analyzing data to transform data into insights. This also involves asking philosophical, legal and social questions in the context of data generation and analysis. In fact, Big Data also belongs to this universe as it comprises data gathering, data fusion and analysis when it comes to manage big data sets. A major goal of this book is to understand data science as a new scientific discipline rather than the practical aspects of data analysis alone. |
| data science accelerator program: Data Science Jing He, Philip S. Yu, Yong Shi, Xingsen Li, Zhijun Xie, Guangyan Huang, Jie Cao, Fu Xiao, 2020-02-01 This book constitutes the refereed proceedings of the 6th International Conference on Data Science, ICDS 2019, held in Ningbo, China, during May 2019. The 64 revised full papers presented were carefully reviewed and selected from 210 submissions. The research papers cover the areas of Advancement of Data Science and Smart City Applications, Theory of Data Science, Data Science of People and Health, Web of Data, Data Science of Trust and Internet of Things. |
| data science accelerator program: Big Data in Education Ben Williamson, 2017-07-24 Big data has the power to transform education and educational research. Governments, researchers and commercial companies are only beginning to understand the potential that big data offers in informing policy ideas, contributing to the development of new educational tools and innovative ways of conducting research. This cutting-edge overview explores the current state-of-play, looking at big data and the related topic of computer code to examine the implications for education and schooling for today and the near future. Key topics include: · The role of learning analytics and educational data science in schools · A critical appreciation of code, algorithms and infrastructures · The rise of ‘cognitive classrooms’, and the practical application of computational algorithms to learning environments · Important digital research methods issues for researchers This is essential reading for anyone studying or working in today′s education environment! |
| data science accelerator program: Keeping Track Jeannie Oakes, 2005-05-10 Selected by the American School Board Journal as a “Must Read” book when it was first published and named one of 60 “Books of the Century” by the University of South Carolina Museum of Education for its influence on American education, this provocative, carefully documented work shows how tracking—the system of grouping students for instruction on the basis of ability—reflects the class and racial inequalities of American society and helps to perpetuate them. For this new edition, Jeannie Oakes has added a new Preface and a new final chapter in which she discusses the “tracking wars” of the last twenty years, wars in which Keeping Track has played a central role. From reviews of the first edition:“Should be read by anyone who wishes to improve schools.”—M. Donald Thomas, American School Board Journal“[This] engaging [book] . . . has had an influence on educational thought and policy that few works of social science ever achieve.”—Tom Loveless in The Tracking Wars“Should be read by teachers, administrators, school board members, and parents.”—Georgia Lewis, Childhood Education“Valuable. . . . No one interested in the topic can afford not to attend to it.”—Kenneth A. Strike, Teachers College Record |
| data science accelerator program: A Curious Moon Rob Conery, 2020-12-13 Starting an application is simple enough, whether you use migrations, a model-synchronizer or good old-fashioned hand-rolled SQL. A year from now, however, when your app has grown and you're trying to measure what's happened... the story can quickly change when data is overwhelming you and you need to make sense of what's been accumulating. Learning how PostgreSQL works is just one aspect of working with data. PostgreSQL is there to enable, enhance and extend what you do as a developer/DBA. And just like any tool in your toolbox, it can help you create crap, slice off some fingers, or help you be the superstar that you are.That's the perspective of A Curious Moon - data is the truth, data is your friend, data is your business. The tools you use (namely PostgreSQL) are simply there to safeguard your treasure and help you understand what it's telling you.But what does it mean to be data-minded? How do you even get started? These are good questions and ones I struggled with when outlining this book. I quickly realized that the only way you could truly understand the power and necessity of solid databsae design was to live the life of a new DBA... thrown into the fire like we all were at some point...Meet Dee Yan, our fictional intern at Red:4 Aerospace. She's just been handed the keys to a massive set of data, straight from Saturn, and she has to load it up, evaluate it and then analyze it for a critical project. She knows that PostgreSQL exists... but that's about it.Much more than a tutorial, this book has a narrative element to it a bit like The Martian, where you get to know Dee and the problems she faces as a new developer/DBA... and how she solves them.The truth is in the data... |
| data science accelerator program: Learning Deep Learning Magnus Ekman, 2021-07-19 NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals. -- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA Ekman uses a learning technique that in our experience has proven pivotal to success—asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us. -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how DL frameworks make it easier to develop more complicated and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences Master NLP with sequence-to-sequence networks and the Transformer architecture Build applications for natural language translation and image captioning NVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| data science accelerator program: Medicaid Eligibility Quality Control United States. Social and Rehabilitation Service, 1975 |
| data science accelerator program: Advanced Analytics in Mining Engineering Ali Soofastaei, 2022-02-23 In this book, Dr. Soofastaei and his colleagues reveal how all mining managers can effectively deploy advanced analytics in their day-to-day operations- one business decision at a time. Most mining companies have a massive amount of data at their disposal. However, they cannot use the stored data in any meaningful way. The powerful new business tool-advanced analytics enables many mining companies to aggressively leverage their data in key business decisions and processes with impressive results. From statistical analysis to machine learning and artificial intelligence, the authors show how many analytical tools can improve decisions about everything in the mine value chain, from exploration to marketing. Combining the science of advanced analytics with the mining industrial business solutions, introduce the “Advanced Analytics in Mining Engineering Book” as a practical road map and tools for unleashing the potential buried in your company’s data. The book is aimed at providing mining executives, managers, and research and development teams with an understanding of the business value and applicability of different analytic approaches and helping data analytics leads by giving them a business framework in which to assess the value, cost, and risk of potential analytical solutions. In addition, the book will provide the next generation of miners – undergraduate and graduate IT and mining engineering students – with an understanding of data analytics applied to the mining industry. By providing a book with chapters structured in line with the mining value chain, we will provide a clear, enterprise-level view of where and how advanced data analytics can best be applied. This book highlights the potential to interconnect activities in the mining enterprise better. Furthermore, the book explores the opportunities for optimization and increased productivity offered by better interoperability along the mining value chain – in line with the emerging vision of creating a digital mine with much-enhanced capabilities for modeling, simulation, and the use of digital twins – in line with leading “digital” industries. |
| data science accelerator program: Accelerating Data Transformation with IBM DB2 Analytics Accelerator for z/OS Ute Baumbach, Patric Becker, Uwe Denneler, Eberhard Hechler, Wolfgang Hengstler, Steffen Knoll, Frank Neumann, Guenter Georg Schoellmann, Khadija Souissi, Timm Zimmermann, IBM Redbooks, 2015-12-11 Transforming data from operational data models to purpose-oriented data structures has been commonplace for the last decades. Data transformations are heavily used in all types of industries to provide information to various users at different levels. Depending on individual needs, the transformed data is stored in various different systems. Sending operational data to other systems for further processing is then required, and introduces much complexity to an existing information technology (IT) infrastructure. Although maintenance of additional hardware and software is one component, potential inconsistencies and individually managed refresh cycles are others. For decades, there was no simple and efficient way to perform data transformations on the source system of operational data. With IBM® DB2® Analytics Accelerator, DB2 for z/OS is now in a unique position to complete these transformations in an efficient and well-performing way. DB2 for z/OS completes these while connecting to the same platform as for operational transactions, helping you to minimize your efforts to manage existing IT infrastructure. Real-time analytics on incoming operational transactions is another demand. Creating a comprehensive scoring model to detect specific patterns inside your data can easily require multiple iterations and multiple hours to complete. By enabling a first set of analytical functionality in DB2 Analytics Accelerator, those dedicated mining algorithms can now be run on an accelerator to efficiently perform these modeling tasks. Given the speed of query processing on an accelerator, these modeling tasks can now be performed much quicker compared to traditional relational database management systems. This speed enables you to keep your scoring algorithms more up-to-date, and ultimately adapt more quickly to constantly changing customer behaviors. This IBM Redbooks® publication describes the new table type that is introduced with DB2 Analytics Accelerator V4.1 PTF5 that enables more efficient data transformations. These tables are called accelerator-only tables, and can exist on an accelerator only. The tables benefit from the accelerator performance characteristics, while maintaining access through existing DB2 for z/OS application programming interfaces (APIs). Additionally, we describe the newly introduced analytical capabilities with DB2 Analytics Accelerator V5.1, putting you in the position to efficiently perform data modeling for online analytical requirements in your DB2 for z/OS environment. This book is intended for technical decision-makers who want to get a broad understanding about the analytical capabilities and accelerator-only tables of DB2 Analytics Accelerator. In addition, you learn about how these capabilities can be used to accelerate in-database transformations and in-database analytics in various environments and scenarios, including the following scenarios: Multi-step processing and reporting in IBM DB2 Query Management FacilityTM, IBM Campaign, or Microstrategy environments In-database transformations using IBM InfoSphere® DataStage® Ad hoc data analysis for data scientists In-database analytics using IBM SPSS® Modeler |
| data science accelerator program: Grow to Your Fullest Ling Qin Zhang, 2014-03-06 You are a seed planted by God, within it is a sleeping giant, your fullest in life. However, you usually do not know until you earnestly make a calling to Him and constantly send twitters to Him. He will answer you at his time and reveal the secret of your life. Once you get the secret, you gain a vision; once you get the vision, life is not aimless any more, it becomes exciting and adventurous. A seed has to break out its shell in order to release the life in it. Its a process of self-brokenness, full of pains and risks; a process that requires courage, determination and endurance; a process that is long, lonely but indispensible. Once you succeed in breaking the shell, you grow out to a world that is full of light and darkness, good and evil, opportunity and problem. They may build you up or tear you down. You have to tackle through all the barriers before you are to bloom and bear fruits. The book shows you a roadmap to grow to your fullest and gives you both wisdom and strength to conquer the growing pains from both within and outside you. The book leads you to a new dimension of life that you never imagine and helps you win the crown of life and reach your fullest. |
| data science accelerator program: Data-Intensive Text Processing with MapReduce Jimmy Lin, Chris Dyer, 2022-05-31 Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader think in MapReduce, but also discusses limitations of the programming model as well. Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks |
| data science accelerator program: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| data science accelerator program: Voices of Innovation Edward W. Marx, 2019-01-14 We can all point to random examples of innovation inside of healthcare information technology, but few repeatable processes exist that make innovation more routine than happenstance. How do you create and sustain a culture of innovation? What are the best practices you can refine and embed as part of your organization's DNA? What are the potential outcomes for robust healthcare transformation when we get this innovation mystery solved? Loaded with numerous case studies and stories of successful innovation projects, this book helps the reader understand how to leverage innovation to help fulfill the promise of healthcare information technology in enabling superior business and clinical outcomes. |
| data science accelerator program: I Bytes Technology Industry ITShades.com, 2021-01-14 This document brings together a set of latest data points and publicly available information relevant for Technology Industry. We are very excited to share this content and believe that readers will benefit from this periodic publication immensely. |
| data science accelerator program: AI and Big Data on IBM Power Systems Servers Scott Vetter, Ivaylo B. Bozhinov, Anto A John, Rafael Freitas de Lima, Ahmed.(Mash) Mashhour, James Van Oosten, Fernando Vermelho, Allison White, IBM Redbooks, 2019-04-10 As big data becomes more ubiquitous, businesses are wondering how they can best leverage it to gain insight into their most important business questions. Using machine learning (ML) and deep learning (DL) in big data environments can identify historical patterns and build artificial intelligence (AI) models that can help businesses to improve customer experience, add services and offerings, identify new revenue streams or lines of business (LOBs), and optimize business or manufacturing operations. The power of AI for predictive analytics is being harnessed across all industries, so it is important that businesses familiarize themselves with all of the tools and techniques that are available for integration with their data lake environments. In this IBM® Redbooks® publication, we cover the best practices for deploying and integrating some of the best AI solutions on the market, including: IBM Watson Machine Learning Accelerator (see note for product naming) IBM Watson Studio Local IBM Power SystemsTM IBM SpectrumTM Scale IBM Data Science Experience (IBM DSX) IBM Elastic StorageTM Server Hortonworks Data Platform (HDP) Hortonworks DataFlow (HDF) H2O Driverless AI We map out all the integrations that are possible with our different AI solutions and how they can integrate with your existing or new data lake. We also walk you through some of our client use cases and show you how some of the industry leaders are using Hortonworks, IBM PowerAI, and IBM Watson Studio Local to drive decision making. We also advise you on your deployment options, when to use a GPU, and why you should use the IBM Elastic Storage Server (IBM ESS) to improve storage management. Lastly, we describe how to integrate IBM Watson Machine Learning Accelerator and Hortonworks with or without IBM Watson Studio Local, how to access real-time data, and security. Note: IBM Watson Machine Learning Accelerator is the new product name for IBM PowerAI Enterprise. Note: Hortonworks merged with Cloudera in January 2019. The new company is called Cloudera. References to Hortonworks as a business entity in this publication are now referring to the merged company. Product names beginning with Hortonworks continue to be marketed and sold under their original names. |
| data science accelerator program: Work Disrupted Jeff Schwartz, 2021-01-07 If you only read one book on the future of work, Work Disrupted: Opportunity, Resilience, and Growth in the Accelerated Future of Work should be that book. The future of work swept in sooner than expected, accelerated by Covid-19, creating an urgent need for new maps, new mindsets, new strategies-- and most importantly, a trusted guide to take us on this journey. That guide is Jeff Schwartz. A founding partner of Deloitte Consulting’s Future of Work practice, Schwartz brings clarity, humor, wisdom, and practical advice to the future of work, a topic surrounded by misinformation, fear, and confusion. With a fundamental belief in the power of human innovation and creativity, Schwartz presents the key issues, critical choices, and potential pitfalls that must be on everyone’s radar. If you're anxious about robots taking away your job in the future, you will take comfort in the realistic perspective, fact-based insights, and practical steps Schwartz offers. If you're not sure where to even begin to prepare, follow his level-headed advice and easy-to-follow action plans. If you're a business leader caught between keeping up, while also being thoughtful about the next moves, you will appreciate the playbook directed at you. If you're wondering how Covid-19 will change how and where you will work, Work Disrupted has you covered. Written in a conversational style by Schwartz, with Suzanne Riss, an award-winning journalist and book author, Work Disrupted offers a welcome alternative to books on the topic that lack a broad perspective or dwell on the problems rather than offer solutions. Timely and insightful, the book includes the impact of Covid-19 on our present and future work. Interviews with leading thinkers on the future of work offer additional perspectives and guidance.Cartoons created for the book by leading business illustrator Tom Fishburne bring to life the reader’s journey and the complex issues surrounding the topic. Told from the perspective of an economist, management advisor, and social commentator, Work Disrupted offers hope--and practical advice--exploring such topics as: How we frame what lies ahead is a critical navigational tool. Discover the signposts that can serve as practical guides for individuals who have families to support, mortgages to pay, and want to stay gainfully employed no matter what the future holds. The importance of recognizing the rapidly evolving opportunities in front of us. Learn how to build resilience—in careers, organizations, and leaders—for what lies ahead. Why exploring new mental models helps us discover the steps we need to take to thrive. Individuals can decide how to protect their livelihood while businesses and public institutions can consider how they can lead and support workforces to thrive in twenty-first-century careers and work. Jeff's marvelous book is a roadmap for the new world of work with clear signposts. His insights will help readers discover opportunities, take action, and find hope in uncertain times. The ideas are fresh, beautifully crafted, and immediately applicable. This is not only a book to be read, but savored and used. —Dave Ulrich, Rensis Likert Professor, Ross School of Business, University of Michigan; Partner, the RBL Group; Co-author Reinventing the Organization |
| data science accelerator program: The Analytics Edge Dimitris Bertsimas, Allison K. O'Hair, William R. Pulleyblank, 2016 Provides a unified, insightful, modern, and entertaining treatment of analytics. The book covers the science of using data to build models, improve decisions, and ultimately add value to institutions and individuals--Back cover. |
| data science accelerator program: Accelerate Nicole Forsgren, PhD, Jez Humble, Gene Kim, 2018-03-27 Winner of the Shingo Publication Award Accelerate your organization to win in the marketplace. How can we apply technology to drive business value? For years, we've been told that the performance of software delivery teams doesn't matter―that it can't provide a competitive advantage to our companies. Through four years of groundbreaking research to include data collected from the State of DevOps reports conducted with Puppet, Dr. Nicole Forsgren, Jez Humble, and Gene Kim set out to find a way to measure software delivery performance―and what drives it―using rigorous statistical methods. This book presents both the findings and the science behind that research, making the information accessible for readers to apply in their own organizations. Readers will discover how to measure the performance of their teams, and what capabilities they should invest in to drive higher performance. This book is ideal for management at every level. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
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