| data engineering capstone project: Python for Mechanical and Aerospace Engineering Alex Kenan, 2021-01-01 The traditional computer science courses for engineering focus on the fundamentals of programming without demonstrating the wide array of practical applications for fields outside of computer science. Thus, the mindset of “Java/Python is for computer science people or programmers, and MATLAB is for engineering” develops. MATLAB tends to dominate the engineering space because it is viewed as a batteries-included software kit that is focused on functional programming. Everything in MATLAB is some sort of array, and it lends itself to engineering integration with its toolkits like Simulink and other add-ins. The downside of MATLAB is that it is proprietary software, the license is expensive to purchase, and it is more limited than Python for doing tasks besides calculating or data capturing. This book is about the Python programming language. Specifically, it is about Python in the context of mechanical and aerospace engineering. Did you know that Python can be used to model a satellite orbiting the Earth? You can find the completed programs and a very helpful 595 page NSA Python tutorial at the book’s GitHub page at https://www.github.com/alexkenan/pymae. Read more about the book, including a sample part of Chapter 5, at https://pymae.github.io |
| data engineering capstone project: Practical Concepts for Capstone Design Engineering Frederick Bloetscher, Daniel Meeroff, 2015 Practical Concepts for Capstone Design Engineering is the first and only comprehensive senior-level college textbook that provides the essential information needed to complete a successful capstone project in civil or construction engineering. Students will gain valuable insight and preparation for civil and construction engineering professional practice, and will learn how to smoothly transition from strictly academic work to solving real-world problems in the context of their capstone projects. The authors provide professional quality work examples, case studies, helpful hints, and assignments at the end of each chapter that further enhance comprehension. In addition to providing students with the key skills necessary to successfully enter the profession, they will also be well prepared for the Fundamentals of Engineering Exam upon graduation. Key Features: Replicates the steps used by practicing engineers to complete design projects from site selection, investigation, and site planning, through the preliminary design calculations and drawing preparation. Offers an approach for integrating students, faculty, design professionals, clients, consultants and regulators bridging the gap between the classroom and the profession with astounding results Provides faculty with a framework for developing an effective capstone course, including examples of grading and rubric sheets for student presentations Appropriate for adoption as primary or supplemental reading in other engineering and construction courses as well |
| data engineering capstone project: Practical Engineering Design Maja Bystrom, Bruce Eisenstein, 2005-05-12 Every engineer must eventually face their first daunting design project. Scheduling, organization, budgeting, prototyping: all can be overwhelming in the short time given to complete the project. While there are resources available on project management and the design process, many are focused too narrowly on specific topics or areas of engineering. Practical Engineering Design presents a complete overview of the design project and beyond for any engineering discipline, including sections on how to protect intellectual property rights and suggestions for turning the project into a business. An outgrowth of the editors' broad experience teaching the capstone Engineering Design course, Practical Engineering Design reflects the most pressing and often-repeated questions with a set of guidelines for the entire process. The editors present two sample project reports and presentations in the appendix and refer to them throughout the book, using examples and critiques to demonstrate specific suggestions for improving the quality of writing and presentation. Real-world examples demonstrate how to formulate schedules and budgets, and generous references in each chapter offer direction to more in-depth information. Whether for a co-op assignment or your first project on the job, this is the most comprehensive guide available for deciding where to begin, organizing the team, budgeting time and resources, and, most importantly, completing the project successfully. |
| data engineering capstone project: The Engineering Capstone Course Harvey F. Hoffman, 2014-07-14 This essential book takes students and instructors through steps undertaken in a start-to-finish engineering project as conceived and presented in the engineering capstone course. The learning experience follows an industry model to prepare students to recognize a need for a product or service, create and work in a team; identify competition, patent overlap, and necessary resources, generate a project proposal that accounts for business issues, prepare a design, develop and fabricate the product or service, develop a test plan to evaluate the product or service, and prepare and deliver a final report and presentation. Throughout the book, students are asked to examine the business viability aspects of the project. The Engineering Capstone Course: Fundamentals for Students and Instructors emphasizes that a design must meet a set of realistic technical specifications and constraints including examination of attendant economics, environmental needs, sustainability, manufacturability, health and safety, governmental regulations, industry standards, and social and political constraints. The book is ideal for instructors teaching, or students working through, the capstone course. |
| data engineering capstone project: Undertaking Capstone Projects in Education Jolanta Burke, Majella Dempsey, 2021-12-30 Undertaking Capstone Projects in Education provides students with all of the information required to successfully design and complete a capstone project. Guiding the reader in a step-by-step process, this book covers how to create a question, select a topic of interest, and apply the best possible design solutions. Structured in a way that will help readers build their skills, chapters explore all aspects of the capstone project from the inception of the idea, to laying the foundations, designing the project, analysing the data, and presenting the findings. Filled with examples and written in a friendly and collaborative style, this key guide uses simple language and easy-to-understand examples to unpack complex research issues. This book is essential reading for students and anyone interested in undertaking a capstone project in the field of education. |
| data engineering capstone project: Python for Everybody Charles R. Severance, 2016-04-09 Python for Everybody is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.Python is an easy to use and easy to learn programming language that is freely available on Macintosh, Windows, or Linux computers. So once you learn Python you can use it for the rest of your career without needing to purchase any software.This book uses the Python 3 language. The earlier Python 2 version of this book is titled Python for Informatics: Exploring Information.There are free downloadable electronic copies of this book in various formats and supporting materials for the book at www.pythonlearn.com. The course materials are available to you under a Creative Commons License so you can adapt them to teach your own Python course. |
| data engineering capstone project: Law and Policy for the Quantum Age Chris Jay Hoofnagle, Simson L. Garfinkel, 2022-01-06 The Quantum Age cuts through the hype to demystify quantum technologies, their development paths, and the policy issues they raise. |
| data engineering capstone project: Engineering Design Clive L. Dym, Patrick Little, 2004 Written for introductory courses in engineering design, this text illustrates conceptual design methods and project management tools through descriptions, examples, and case studies. |
| data engineering capstone project: Engineering Grand Challenges in Scholar Programs Ghafour Amouzad Mahdiraji, Edwin C.Y. Chung, Satesh Narayana Namasivayam, Mohammad Hosseini Fouladi, 2019-02-06 This book explains how Taylor’s University implemented a curriculum in their engineering program that prepares students to address challenges facing the world. Aim is to enable Engineers put their knowledge into application to meet the 14 challenges of the century as outlined by the National Academy of Engineering (NAE) of the United States. The research groups are organized around the 14 grand challenges for engineering The structure of their syllabi is organized in a way that they address the 5 core competencies: Research Experience, Entrepreneurship, Service Learning, Interdisciplinary Curriculum, Global Dimension. It uses the CDIO educational framework, a project-based learning approach that provides students with the big picture of engineering. Through this method, students are able to: Master a deeper working knowledge of the fundamentals of engineering Lead in the creation and operation of new products and systems Understand the importance and strategic value of research work As the only programe of its kind outside North America, it offers the brightest minds the opportunity to face real-world issues and places them on the cutting edge of the engineering world. |
| data engineering capstone project: 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 engineering capstone project: Recent Advances in Artificial Intelligence and Data Engineering Pushparaj Shetty D., Surendra Shetty, 2021-10-31 This book presents select proceedings of the International Conference on Artificial Intelligence and Data Engineering (AIDE 2020). Various topics covered in this book include deep learning, neural networks, machine learning, computational intelligence, cognitive computing, fuzzy logic, expert systems, brain-machine interfaces, ant colony optimization, natural language processing, bioinformatics and computational biology, cloud computing, machine vision and robotics, ambient intelligence, intelligent transportation, sensing and sensor networks, big data challenge, data science, high performance computing, data mining and knowledge discovery, and data privacy and security. The book will be a valuable reference for beginners, researchers, and professionals interested in artificial intelligence, robotics and data engineering. |
| data engineering capstone project: Database Internals Alex Petrov, 2019-09-13 When it comes to choosing, using, and maintaining a database, understanding its internals is essential. But with so many distributed databases and tools available today, it’s often difficult to understand what each one offers and how they differ. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals. Throughout the book, you’ll explore relevant material gleaned from numerous books, papers, blog posts, and the source code of several open source databases. These resources are listed at the end of parts one and two. You’ll discover that the most significant distinctions among many modern databases reside in subsystems that determine how storage is organized and how data is distributed. This book examines: Storage engines: Explore storage classification and taxonomy, and dive into B-Tree-based and immutable Log Structured storage engines, with differences and use-cases for each Storage building blocks: Learn how database files are organized to build efficient storage, using auxiliary data structures such as Page Cache, Buffer Pool and Write-Ahead Log Distributed systems: Learn step-by-step how nodes and processes connect and build complex communication patterns Database clusters: Which consistency models are commonly used by modern databases and how distributed storage systems achieve consistency |
| data engineering capstone project: MICCAI 2012 Workshop on Multi-Atlas Labeling Bennett Landman, Annemie Ribbens, Blake Lucas, Christos, Christos Davatzikos,, Brian Avants, Christian Ledig, Da Ma, Daniel Rueckert, Dirk Vandermeulen, Frederik Maes, Guray Erus, Jiahui Wang, Holly Holmes, Hongzhi Wang, Jimit Doshi, Joe Kornegay, Jose Manjon, Alexander Hammers, Alireza Akhondi-Asl, Andrew Asman, 2012-08-26 Characterization of anatomical structure through segmentation has become essential for morphological assessment and localizing quantitative measures. Segmentation through registration and atlas label transfer has proven to be a flexible and fruitful approach as efficient, non-rigid image registration methods have become prevalent. Label transfer segmentation using multiple atlases has helped to bring statistical fusion, shape modeling, and meta-analysis techniques to the forefront of segmentation research. Numerous creative approaches have proposed to use atlas information to apply labels to brain anatomy. However, it is difficult to evaluate the relative advantages and limitations of these methods as they have been applied on very different datasets. This workshop provides a snapshot of the current progress in the field through extended discussions and provides researchers an opportunity to characterize their methods on standardized data in a grand challenge. |
| data engineering capstone project: Embedding Service Learning in European Higher Education Pilar Aramburuzabala, Lorraine McIlrath, Héctor Opazo, 2019-05-07 Service learning brings together students, academics and the community whereby all become teaching resources, problem solvers and partners. In addition to enhancing academic and real-world learning, the overall purpose of service learning is to instil in students a sense of civic engagement and responsibility and work towards positive social change within society. Embedding Service Learning in European Higher Education promotes service learning as a pedagogical approach that develops civic engagement within higher education. It both describes and assesses the most recent developments and contextual positioning of service learning in European higher education and considers if and how the pedagogy is responding to European Union policy and the strategy of higher education institutions and towards engagement with broader societal issues. With case studies from 12 universities across Europe, this book draws on existing practice, shares knowledge and develops best practice to provide conceptual and practical tools for teaching, researching and practising service learning. This book: exposes service learning as a key approach in terms of embedding a culture of political and civic literacy within higher education; considers service learning in Europe, an area of growing research in service learning practice; explores the issue of university social responsibility; presents chapters from leaders in the service learning movement at a national and international level. Practical and engaging, Embedding Service Learning in European Higher Education is a fascinating read for anyone working in service learning as well as those working at universities with an interest in social and civic engagement and institutional reform. |
| data engineering capstone project: Engineering Capstone Design Bahram Nassersharif, 2022-06-27 Structured with a practical approach, Engineering Capstone Design guides engineering students to successfully manage capstone design projects. The book addresses the challenge of open-ended design projects, often in a team-based format, discussing team member roles, communication, and cooperation. It incorporates accreditation requirements and provides a modern framework for working with industry, reinforced by the inclusion of case studies. Offers a structured process for capstone design, responsive to ABET accreditation requirements Explains how to manage design projects under critical timelines and budgets Covers essential topics and steps in a capstone design sequence, including defining, conceiving, presenting, prototyping, building, testing, and redesigning Considers industry perspectives, as well as design competitions Includes case studies for a look into industry experience In addition to guiding engineering students conducting capstone design projects, this book will also interest industry professionals who are engaged in product development or design problem-solving. |
| data engineering capstone project: Mastering Java for Data Science Alexey Grigorev, 2017-04-27 Use Java to create a diverse range of Data Science applications and bring Data Science into production About This Book An overview of modern Data Science and Machine Learning libraries available in Java Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks. Easy-to-follow illustrations and the running example of building a search engine. Who This Book Is For This book is intended for software engineers who are comfortable with developing Java applications and are familiar with the basic concepts of data science. Additionally, it will also be useful for data scientists who do not yet know Java but want or need to learn it. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing the existing stack, this book is for you! What You Will Learn Get a solid understanding of the data processing toolbox available in Java Explore the data science ecosystem available in Java Find out how to approach different machine learning problems with Java Process unstructured information such as natural language text or images Create your own search engine Get state-of-the-art performance with XGBoost Learn how to build deep neural networks with DeepLearning4j Build applications that scale and process large amounts of data Deploy data science models to production and evaluate their performance In Detail Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings. Style and approach This is a practical guide where all the important concepts such as classification, regression, and dimensionality reduction are explained with the help of examples. |
| data engineering capstone project: Data-Centric Business and Applications Aneta Poniszewska-Marańda, Natalia Kryvinska, Stanisław Jarząbek, Lech Madeyski, 2019-12-14 This book explores various aspects of software creation and development as well as data and information processing. It covers relevant topics such as business analysis, business rules, requirements engineering, software development processes, software defect prediction, information management systems, and knowledge management solutions. Lastly, the book presents lessons learned in information and data management processes and procedures. |
| data engineering capstone project: Analytics and Knowledge Management Suliman Hawamdeh, Hsia-Ching Chang, 2018-08-06 The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations. The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics techniques. Analytics, on the other hand, is the examination, interpretation, and discovery of meaningful patterns, trends, and knowledge from data and textual information. It provides the basis for knowledge discovery and completes the cycle in which knowledge management and knowledge utilization happen. Organizations should develop knowledge focuses on data quality, application domain, selecting analytics techniques, and on how to take actions based on patterns and insights derived from analytics. Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics. Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships. This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms. |
| data engineering capstone project: Principles and Theories of Data Mining With RapidMiner Ramjan, Sarawut, Sunkpho, Jirapon, 2023-05-09 The demand for skilled data scientists is rapidly increasing as more organizations recognize the value of data-driven decision- making. Data science, data management, and data mining are all critical components for various types of organizations, including large and small corporations, academic institutions, and government entities. For companies, these components serve to extract insights and value from their data, empowering them to make evidence-driven decisions and gain a competitive advantage by discovering patterns and trends and avoiding costly mistakes. Academic institutions utilize these tools to analyze large datasets and gain insights into various scientific fields of study, including genetic data, climate data, financial data, and in the social sciences they are used to analyze survey data, behavioral data, and public opinion data. Governments use data science to analyze data that can inform policy decisions, such as identifying areas with high crime rates, determining which regions need infrastructure development, and predicting disease outbreaks. However, individuals who are not data science experts, but are experts within their own fields, may need to apply their experience to the data they must manage, but still struggle to expand their knowledge of how to use data mining tools such as RapidMiner software. Principles and Theories of Data Mining With RapidMiner is a comprehensive guide for students and individuals interested in experimenting with data mining using RapidMiner software. This book takes a practical approach to learning through the RapidMiner tool, with exercises and case studies that demonstrate how to apply data mining techniques to real-world scenarios. Readers will learn essential concepts related to data mining, such as supervised learning, unsupervised learning, association rule mining, categorical data, continuous data, and data quality. Additionally, readers will learn how to apply data mining techniques to popular algorithms, including k-nearest neighbor (K-NN), decision tree, naïve bayes, artificial neural network (ANN), k-means clustering, and probabilistic methods. By the end of the book, readers will have the skills and confidence to use RapidMiner software effectively and efficiently, making it an ideal resource for anyone, whether a student or a professional, who needs to expand their knowledge of data mining with RapidMiner software. |
| data engineering capstone project: Recommendation Engines Michael Schrage, 2020-09-01 How companies like Amazon, Netflix, and Spotify know what you might also like: the history, technology, business, and societal impact of online recommendation engines. Increasingly, our technologies are giving us better, faster, smarter, and more personal advice than our own families and best friends. Amazon already knows what kind of books and household goods you like and is more than eager to recommend more; YouTube and TikTok always have another video lined up to show you; Netflix has crunched the numbers of your viewing habits to suggest whole genres that you would enjoy. In this volume in the MIT Press's Essential Knowledge series, innovation expert Michael Schrage explains the origins, technologies, business applications, and increasing societal impact of recommendation engines, the systems that allow companies worldwide to know what products, services, and experiences you might also like. |
| data engineering capstone project: Mechanism Analysis Lyndon O. Barton, 2016-04-19 This updated and enlarged Second Edition provides in-depth, progressive studies of kinematic mechanisms and offers novel, simplified methods of solving typical problems that arise in mechanisms synthesis and analysis - concentrating on the use of algebra and trigonometry and minimizing the need for calculus.;It continues to furnish complete coverag |
| data engineering capstone project: Senior Design Projects in Mechanical Engineering Yongsheng Ma, Yiming Rong, 2021-11-10 This book offers invaluable insights about the full spectrum of core design course contents systematically and in detail. This book is for instructors and students who are involved in teaching and learning of ‘capstone senior design projects’ in mechanical engineering. It consists of 17 chapters, over 300 illustrations with many real-world student project examples. The main project processes are grouped into three phases, i.e., project scoping and specification, conceptual design, and detail design, and each has dedicated two chapters of process description and report content prescription, respectively. The basic principles and engineering process flow are well applicable for professional development of mechanical design engineers. CAD/CAM/CAE technologies are commonly used within many project examples. Thematic chapters also cover student teamwork organization and evaluation, project management, design standards and regulations, and rubrics of course activity grading. Key criteria of successful course accreditation and graduation attributes are discussed in details. In summary, it is a handy textbook for the capstone design project course in mechanical engineering and an insightful teaching guidebook for engineering design instructors. |
| data engineering capstone project: Experimental Statistics and Data Analysis for Mechanical and Aerospace Engineers James A. Middleton, 2021-11-25 This book develops foundational concepts in probability and statistics with primary applications in mechanical and aerospace engineering. It develops the mindset a data analyst must have to interpret an ill-defined problem, operationalize it, collect or interpret data, and use this evidence to make decisions that can improve the quality of engineered products and systems. It was designed utilizing the latest research in statistics learning and in engagement teaching practices The author’s focus is on developing students’ conceptual understanding of statistical theory with the goal of effective design and conduct of experiments. Engineering statistics is primarily a form of data modeling. Emphasis is placed on modelling variation in observations, characterizing its distribution, and making inferences with regards to quality assurance and control. Fitting multivariate models, experimental design and hypothesis testing are all critical skills developed. All topics are developed utilizing real data from engineering projects, simulations, and laboratory experiences. In other words, we begin with data, we end with models. The key features are: Realistic contexts situating the learning of the statistics in actual engineering practice. A balance of rigorous mathematics, conceptual scaffolding, and real, messy data, to ensure that students learn the important concepts and can apply them in practice. The consistency of text, lecture notes, data sets, and simulations yield a coherent set of instructional resources for the instructor and a coherent set of learning experiences for the students. MatLab is used as a computational tool. Other tools are easily substituted. Table of Contents 1. Introduction 2. Dealing with Variation 3. Types of Data 4. Introduction to Probability 5. Sampling Distribution of the Mean 6. The Ten Building Blocks of Experimental Design 7. Sampling Distribution of the Proportion 8. Hypothesis Testing Using the 1-sample Statistics 9. 2-sample Statistics 10. Simple Linear Regression 11. The General Linear Model: Regression with Multiple Predictors 12. The GLM with Categorical Independent Variables: The Analysis of Variance 13. The General Linear Model: Randomized Block Factorial ANOVA 14. Factorial Analysis of Variance 15. The Bootstrap 16. Data Reduction: Principal Components Analysis Index Author Biography James A. Middleton is Professor of Mechanical and Aerospace Engineering and former Director of the Center for Research on Education in Science, Mathematics, Engineering, and Technology at Arizona State University. Previously, he held the Elmhurst Energy Chair in STEM education at the University of Birmingham in the UK. He received his Ph.D. from the University of Wisconsin-Madison. He has been Senior co-Chair of the Special Interest Group for Mathematics Education in the American Educational Research Association, and as Chair of the National Council of Teachers of Mathematics’ Research Committee. He has been a consultant for the College Board, the Rand Corporation, the National Academies, the American Statistical Association, the IEEE, and numerous school systems around the United States, the UK, and Australia. He has garnered over $30 million in grants to study and improve mathematics education in urban schools. |
| data engineering capstone project: Closing the Analytics Talent Gap Jennifer Priestley, Robert McGrath, 2021-05-03 How can we recruit out of your program? We have a project – how do we reach out to your students? If we do research together who owns it? We have employees who need to upskill in analytics – can you help me with that? How much does all of this cost? Managers and executives are increasingly asking university professors such questions as they deal with a critical shortage of skilled data analysts. At the same time, academics are asking such questions as: How can I bring a real analytical project in the classroom? How can I get real data to help my students develop the skills necessary to be a data scientist? Is what I am teaching in the classroom aligned with the demands of the market for analytical talent? After spending several years answering almost daily e-mails and telephone calls from business managers asking for staffing help and aiding fellow academics with their analytics teaching needs, Dr. Jennifer Priestley of Kennesaw State University and Dr. Robert McGrath of the University of New Hampshire wrote Closing the Analytics Talent Gap: An Executive’s Guide to Working with Universities. The book builds a bridge between university analytics programs and business organizations. It promotes a dialog that enables executives to learn how universities can help them find strategically important personnel and universities to learn how they can develop and educate this personnel. Organizations are facing previously unforeseen challenges related to the translation of massive amounts of data – structured and unstructured, static and in-motion, voice, text, and image – into information to solve current challenges and anticipate new ones. The advent of analytics and data science also presents universities with unforeseen challenges of providing learning through application. This book helps both organizations with finding data natives and universities with educating students to develop the facility to work in a multi-faceted and complex data environment. . |
| data engineering capstone project: 10-Minute Science Projects Sarah L. Schuette, 2020 Looking for science-themed makerspace projects that won't take too long? Look no more! From bots and goo to lava and cells, these 10-minute STEM projects will have kids making in no time! |
| data engineering capstone project: Compendium of Civil Engineering Education Strategies Hudson Jackson, Kassim Tarhini, 2022-06-07 This book compiles proven strategies and information on civil engineering education and the skills necessary for successful practice of civil engineering such as critical thinking, design thinking, leadership, and communication skills. It also addresses other relevant topics including professional ethics, global perspectives, assessment, recruitment, retention, and more. It is designed so that each chapter can be used separately or in combination with other chapters to help enhance and foster student learning as well as development of skills required for engineering practice. Features Includes overviews of successful academic approaches for each topic including implementation examples in every chapter Explains how assessment and the resulting data can be used for holistic evaluation and improvement of student learning Addresses the complexities of moral and professional ethics in engineering Highlights the importance of adopting a global perspective and the successful strategies that have been used or considered in educating resilient, globally minded engineers Compendium of Civil Engineering Education Strategies: Case Studies and Examples serves as a useful guide for engineering faculty, practitioners, and graduate students considering a career in academia. Academic faculty and working professionals will find the content helpful as instructional and reference material in developing and assessing career skills. It is also useful for intellectually curious students who want a deeper understanding and appreciation of the need for professional development and life-long learning. |
| data engineering capstone project: Growing Information: Part 2 Eli B. Cohen, 2009 |
| data engineering capstone project: Bulletin - U.S. Coast Guard Academy Alumni Association United States Coast Guard Academy. Alumni Association, 1985 |
| data engineering capstone project: Systems Engineering for the Digital Age Dinesh Verma, 2023-10-24 Systems Engineering for the Digital Age Comprehensive resource presenting methods, processes, and tools relating to the digital and model-based transformation from both technical and management views Systems Engineering for the Digital Age: Practitioner Perspectives covers methods and tools that are made possible by the latest developments in computational modeling, descriptive modeling languages, semantic web technologies, and describes how they can be integrated into existing systems engineering practice, how best to manage their use, and how to help train and educate systems engineers of today and the future. This book explains how digital models can be leveraged for enhancing engineering trades, systems risk and maturity, and the design of safe, secure, and resilient systems, providing an update on the methods, processes, and tools to synthesize, analyze, and make decisions in management, mission engineering, and system of systems. Composed of nine chapters, the book covers digital and model-based methods, digital engineering, agile systems engineering, improving system risk, and more, representing the latest insights from research in topics related to systems engineering for complicated and complex systems and system-of-systems. Based on validated research conducted via the Systems Engineering Research Center (SERC), this book provides the reader a set of pragmatic concepts, methods, models, methodologies, and tools to aid the development of digital engineering capability within their organization. Systems Engineering for the Digital Age: Practitioner Perspectives includes information on: Fundamentals of digital engineering, graphical concept of operations, and mission and systems engineering methods Transforming systems engineering through integrating M&S and digital thread, and interactive model centric systems engineering The OODA loop of value creation, digital engineering measures, and model and data verification and validation Digital engineering testbed, transformation, and implications on decision making processes, and architecting tradespace analysis in a digital engineering environment Expedited systems engineering for rapid capability and learning, and agile systems engineering framework Based on results and insights from a research center and providing highly comprehensive coverage of the subject, Systems Engineering for the Digital Age: Practitioner Perspectives is written specifically for practicing engineers, program managers, and enterprise leadership, along with graduate students in related programs of study. |
| data engineering capstone project: Infusing Real World Experiences into Engineering Education AMD NextGen Engineer, National Academy of Engineering, 2012-11-15 The aim of this report is to encourage enhanced richness and relevance of the undergraduate engineering education experience, and thus produce better-prepared and more globally competitive graduates, by providing practical guidance for incorporating real world experience in US engineering programs. The report, a collaborative effort of the National Academy of Engineering (NAE) and Advanced Micro Devices, Inc. (AMD), builds on two NAE reports on The Engineer of 2020 that cited the importance of grounding engineering education in real world experience. This project also aligns with other NAE efforts in engineering education, such as the Grand Challenges of Engineering, Changing the Conversation, and Frontiers of Engineering Education. This publication presents 29 programs that have successfully infused real world experiences into engineering or engineering technology undergraduate education. The Real World Engineering Education committee acknowledges the vision of AMD in supporting this project, which provides useful exemplars for institutions of higher education who seek model programs for infusing real world experiences in their programs. The NAE selection committee was impressed by the number of institutions committed to grounding their programs in real world experience and by the quality, creativity, and diversity of approaches reflected in the submissions. A call for nominations sent to engineering and engineering technology deans, chairs, and faculty yielded 95 high-quality submissions. Two conditions were required of the nominations: (1) an accredited 4-year undergraduate engineering or engineering technology program was the lead institutions, and (2) the nominated program started operation no later than the fall 2010 semester. Within these broad parameters, nominations ranged from those based on innovations within a single course to enhancements across an entire curriculum or institution. Infusing Real World Experiences into Engineering Education is intended to provide sufficient information to enable engineering and engineering technology faculty and administrators to assess and adapt effective, innovative models of programs to their own institution's objectives. Recognizing that change is rarely trivial, the project included a brief survey of selected engineering deans concern in the adoption of such programs. |
| data engineering capstone project: Assembly West Point Association of Graduates (Organization)., 2005 |
| data engineering capstone project: Roundtable on Data Science Postsecondary Education National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Division on Engineering and Physical Sciences, Board on Science Education, Computer Science and Telecommunications Board, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, 2020-10-02 Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting. |
| data engineering capstone project: Shaping Our World Gretar Tryggvason, 2011-11 Engineering education is currently on the verge of a major transformation. However, while the need has been much discussed and several proposals for change have been put forward, relatively little focus has been put on actual implementation of the proposed changes. This book examines a program that has a long history of experimentation in engineering education. Written by experts on the subject, it describes specific topics with each chapter focusing on a specific innovation that has been carried out and explaining the educational pedagogy the learning benefit, as well as the transferability of the approach-- |
| data engineering capstone project: Data Analysis Foundations with Python Cuantum Technologies LLC, 2024-06-12 Dive into data analysis with Python, starting from the basics to advanced techniques. This course covers Python programming, data manipulation with Pandas, data visualization, exploratory data analysis, and machine learning. Key Features From Python basics to advanced data analysis techniques. Apply your skills to practical scenarios through real-world case studies. Detailed projects and quizzes to help gain the necessary skills. Book DescriptionEmbark on a comprehensive journey through data analysis with Python. Begin with an introduction to data analysis and Python, setting a strong foundation before delving into Python programming basics. Learn to set up your data analysis environment, ensuring you have the necessary tools and libraries at your fingertips. As you progress, gain proficiency in NumPy for numerical operations and Pandas for data manipulation, mastering the skills to handle and transform data efficiently. Proceed to data visualization with Matplotlib and Seaborn, where you'll create insightful visualizations to uncover patterns and trends. Understand the core principles of exploratory data analysis (EDA) and data preprocessing, preparing your data for robust analysis. Explore probability theory and hypothesis testing to make data-driven conclusions and get introduced to the fundamentals of machine learning. Delve into supervised and unsupervised learning techniques, laying the groundwork for predictive modeling. To solidify your knowledge, engage with two practical case studies: sales data analysis and social media sentiment analysis. These real-world applications will demonstrate best practices and provide valuable tips for your data analysis projects.What you will learn Develop a strong foundation in Python for data analysis. Manipulate and analyze data using NumPy and Pandas. Create insightful data visualizations with Matplotlib and Seaborn. Understand and apply probability theory and hypothesis testing. Implement supervised and unsupervised machine learning algorithms. Execute real-world data analysis projects with confidence. Who this book is for This course adopts a hands-on approach, seamlessly blending theoretical lessons with practical exercises and real-world case studies. Practical exercises are designed to apply theoretical knowledge, providing learners with the opportunity to experiment and learn through doing. Real-world applications and examples are integrated throughout the course to contextualize concepts, making the learning process engaging, relevant, and effective. By the end of the course, students will have a thorough understanding of the subject matter and the ability to apply their knowledge in practical scenarios. |
| data engineering capstone project: System Engineering Analysis, Design, and Development Charles S. Wasson, 2015-11-16 Praise for the first edition: This excellent text will be useful to every system engineer (SE) regardless of the domain. It covers ALL relevant SE material and does so in a very clear, methodical fashion. The breadth and depth of the author's presentation of SE principles and practices is outstanding. —Philip Allen This textbook presents a comprehensive, step-by-step guide to System Engineering analysis, design, and development via an integrated set of concepts, principles, practices, and methodologies. The methods presented in this text apply to any type of human system -- small, medium, and large organizational systems and system development projects delivering engineered systems or services across multiple business sectors such as medical, transportation, financial, educational, governmental, aerospace and defense, utilities, political, and charity, among others. Provides a common focal point for “bridging the gap” between and unifying System Users, System Acquirers, multi-discipline System Engineering, and Project, Functional, and Executive Management education, knowledge, and decision-making for developing systems, products, or services Each chapter provides definitions of key terms, guiding principles, examples, author’s notes, real-world examples, and exercises, which highlight and reinforce key SE&D concepts and practices Addresses concepts employed in Model-Based Systems Engineering (MBSE), Model-Driven Design (MDD), Unified Modeling Language (UMLTM) / Systems Modeling Language (SysMLTM), and Agile/Spiral/V-Model Development such as user needs, stories, and use cases analysis; specification development; system architecture development; User-Centric System Design (UCSD); interface definition & control; system integration & test; and Verification & Validation (V&V) Highlights/introduces a new 21st Century Systems Engineering & Development (SE&D) paradigm that is easy to understand and implement. Provides practices that are critical staging points for technical decision making such as Technical Strategy Development; Life Cycle requirements; Phases, Modes, & States; SE Process; Requirements Derivation; System Architecture Development, User-Centric System Design (UCSD); Engineering Standards, Coordinate Systems, and Conventions; et al. Thoroughly illustrated, with end-of-chapter exercises and numerous case studies and examples, Systems Engineering Analysis, Design, and Development, Second Edition is a primary textbook for multi-discipline, engineering, system analysis, and project management undergraduate/graduate level students and a valuable reference for professionals. |
| data engineering capstone project: Deep Learning Applications, Volume 2 M. Arif Wani, Taghi Khoshgoftaar, Vasile Palade, 2020-12-14 This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers. |
| data engineering capstone project: New Horizons in Web Based Learning Yiwei Cao, Terje Väljataga, Jeff K.T. Tang, Howard Leung, Mart Laanpere, 2014-12-05 This book constitutes the revised selected papers of the workshops of the 13th International Conference of Web-based Learning, ICWL 2014, held in Tallinn, Estonia, in August 2014. This volume comprises papers of six workshops: 1. The Seventh International Workshop on Social and Personal Computing for Web-Supported Learning Communities (SPeL 2014) 2. The First International Workshop on Peer-Review, Peer-Assessment, and Self-Assessment in Education (PRASAE 2014) 3. International Workshop on Mobile and Personalized Learning (IWMPL 2014) 4. The First International Workshop on Open Badges in Education (OBIE 2014) 5. The Fourth International Symposium on Knowledge Management & E-Learning (KMEL 2014) 6. The Future of e-Textbooks Workshop (FeT 2014). |
| data engineering capstone project: Data-Driven Innovation for Intelligent Technology Hiram Ponce, |
| data engineering capstone project: iCEER2014-McMaster Digest Mohamed Bakr, Ahmed Elsharabasy, 2014-11-18 International Conference on Engineering Education and Research |
| data engineering capstone project: Advanced Information Technology in Education Khine Soe Thaung, 2012-02-03 The volume includes a set of selected papers extended and revised from the 2011 International Conference on Computers and Advanced Technology in Education. With the development of computers and advanced technology, the human social activities are changing basically. Education, especially the education reforms in different countries, has been experiencing the great help from the computers and advanced technology. Generally speaking, education is a field which needs more information, while the computers, advanced technology and internet are a good information provider. Also, with the aid of the computer and advanced technology, persons can make the education an effective combination. Therefore, computers and advanced technology should be regarded as an important media in the modern education. Volume Advanced Information Technology in Education is to provide a forum for researchers, educators, engineers, and government officials involved in the general areas of computers and advanced technology in education to disseminate their latest research results and exchange views on the future research directions of these fields. |
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 …
