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| data science foundations everfi: Data Science Fundamentals and Practical Approaches Nandi Dr. Rupam Dr. Gypsy, Kumar Sharma, 2020-09-03 Learn how to process and analysis data using Python Key Features a- The book has theories explained elaborately along with Python code and corresponding output to support the theoretical explanations. The Python codes are provided with step-by-step comments to explain each instruction of the code. a- The book is quite well balanced with programs and illustrative real-case problems. a- The book not only deals with the background mathematics alone or only the programs but also beautifully correlates the background mathematics to the theory and then finally translating it into the programs. a- A rich set of chapter-end exercises are provided, consisting of both short-answer questions and long-answer questions. Description This book introduces the fundamental concepts of Data Science, which has proved to be a major game-changer in business solving problems. Topics covered in the book include fundamentals of Data Science, data preprocessing, data plotting and visualization, statistical data analysis, machine learning for data analysis, time-series analysis, deep learning for Data Science, social media analytics, business analytics, and Big Data analytics. The content of the book describes the fundamentals of each of the Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of Python programming language. Each chapter contains numerous examples and illustrative output to explain the important basic concepts. An appropriate number of questions is presented at the end of each chapter for self-assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic. What will you learn a- Understand what machine learning is and how learning can be incorporated into a program. a- Perform data processing to make it ready for visual plot to understand the pattern in data over time. a- Know how tools can be used to perform analysis on big data using python a- Perform social media analytics, business analytics, and data analytics on any data of a company or organization. Who this book is for The book is for readers with basic programming and mathematical skills. The book is for any engineering graduates that wish to apply data science in their projects or wish to build a career in this direction. The book can be read by anyone who has an interest in data analysis and would like to explore more out of interest or to apply it to certain real-life problems. Table of Contents 1. Fundamentals of Data Science1 2. Data Preprocessing 3. Data Plotting and Visualization 4. Statistical Data Analysis 5. Machine Learning for Data Science 6. Time-Series Analysis 7. Deep Learning for Data Science 8. Social Media Analytics 9. Business Analytics 10. Big Data Analytics About the Authors Dr. Gypsy Nandi is an Assistant Professor (Sr) in the Department of Computer Applications, Assam Don Bosco University, India. Her areas of interest include Data Science, Social Network Mining, and Machine Learning. She has completed her Ph.D. in the field of 'Social Network Analysis and Mining'. Her research scholars are currently working mainly in the field of Data Science. She has several research publications in reputed journals and book series. Dr. Rupam Kumar Sharma is an Assistant Professor in the Department of Computer Applications, Assam Don Bosco University, India. His area of interest includes Machine Learning, Data Analytics, Network, and Cyber Security. He has several research publications in reputed SCI and Scopus journals. He has also delivered lectures and trained hundreds of trainees and students across different institutes in the field of security and android app development. |
| data science foundations everfi: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks. |
| data science foundations everfi: Fundamentals of Data Science Sanjeev J. Wagh, Manisha S. Bhende, Anuradha D. Thakare, 2021-09-26 Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science. Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes Readers will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue. This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge. Features : Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets. Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools. Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice. Information is presented in an accessible way for students, researchers and academicians and professionals. |
| data science foundations everfi: Foundations of Data Science with Python John M. Shea, 2024-02-20 Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. Intended for engineers and scientists, it can be used by any who know computer programming. |
| data science foundations everfi: Data Science Foundations Fionn Murtagh, 2017-09-22 Data Science Foundations is most welcome and, indeed, a piece of literature that the field is very much in need of...quite different from most data analytics texts which largely ignore foundational concepts and simply present a cookbook of methods...a very useful text and I would certainly use it in my teaching. - Mark Girolami, Warwick University Data Science encompasses the traditional disciplines of mathematics, statistics, data analysis, machine learning, and pattern recognition. This book is designed to provide a new framework for Data Science, based on a solid foundation in mathematics and computational science. It is written in an accessible style, for readers who are engaged with the subject but not necessarily experts in all aspects. It includes a wide range of case studies from diverse fields, and seeks to inspire and motivate the reader with respect to data, associated information, and derived knowledge. |
| data science foundations everfi: Foundations of Data Science for Engineering Problem Solving Parikshit Narendra Mahalle, Gitanjali Rahul Shinde, Priya Dudhale Pise, Jyoti Yogesh Deshmukh, 2021-08-21 This book is one-stop shop which offers essential information one must know and can implement in real-time business expansions to solve engineering problems in various disciplines. It will also help us to make future predictions and decisions using AI algorithms for engineering problems. Machine learning and optimizing techniques provide strong insights into novice users. In the era of big data, there is a need to deal with data science problems in multidisciplinary perspective. In the real world, data comes from various use cases, and there is a need of source specific data science models. Information is drawn from various platforms, channels, and sectors including web-based media, online business locales, medical services studies, and Internet. To understand the trends in the market, data science can take us through various scenarios. It takes help of artificial intelligence and machine learning techniques to design and optimize the algorithms. Big data modelling and visualization techniques of collected data play a vital role in the field of data science. This book targets the researchers from areas of artificial intelligence, machine learning, data science and big data analytics to look for new techniques in business analytics and applications of artificial intelligence in recent businesses. |
| data science foundations everfi: The Constitution of Algorithms Florian Jaton, 2021-04-27 A laboratory study that investigates how algorithms come into existence. Algorithms--often associated with the terms big data, machine learning, or artificial intelligence--underlie the technologies we use every day, and disputes over the consequences, actual or potential, of new algorithms arise regularly. In this book, Florian Jaton offers a new way to study computerized methods, providing an account of where algorithms come from and how they are constituted, investigating the practical activities by which algorithms are progressively assembled rather than what they may suggest or require once they are assembled. |
| data science foundations everfi: Fundamentals of Data Science Jugal K. Kalita, Dhruba K. Bhattacharyya, Swarup Roy, 2023-11-17 Fundamentals of Data Science: Theory and Practice presents basic and advanced concepts in data science along with real-life applications. The book provides students, researchers and professionals at different levels a good understanding of the concepts of data science, machine learning, data mining and analytics. Users will find the authors' research experiences and achievements in data science applications, along with in-depth discussions on topics that are essential for data science projects, including pre-processing, that is carried out before applying predictive and descriptive data analysis tasks and proximity measures for numeric, categorical and mixed-type data. The book's authors include a systematic presentation of many predictive and descriptive learning algorithms, including recent developments that have successfully handled large datasets with high accuracy. In addition, a number of descriptive learning tasks are included. - Presents the foundational concepts of data science along with advanced concepts and real-life applications for applied learning - Includes coverage of a number of key topics such as data quality and pre-processing, proximity and validation, predictive data science, descriptive data science, ensemble learning, association rule mining, Big Data analytics, as well as incremental and distributed learning - Provides updates on key applications of data science techniques in areas such as Computational Biology, Network Intrusion Detection, Natural Language Processing, Software Clone Detection, Financial Data Analysis, and Scientific Time Series Data Analysis - Covers computer program code for implementing descriptive and predictive algorithms |
| data science foundations everfi: The Quants Scott Patterson, 2011-01-25 With the immediacy of today’s NASDAQ close and the timeless power of a Greek tragedy, The Quants is at once a masterpiece of explanatory journalism, a gripping tale of ambition and hubris, and an ominous warning about Wall Street’s future. In March of 2006, four of the world’s richest men sipped champagne in an opulent New York hotel. They were preparing to compete in a poker tournament with million-dollar stakes, but those numbers meant nothing to them. They were accustomed to risking billions. On that night, these four men and their cohorts were the new kings of Wall Street. Muller, Griffin, Asness, and Weinstein were among the best and brightest of a new breed, the quants. Over the prior twenty years, this species of math whiz--technocrats who make billions not with gut calls or fundamental analysis but with formulas and high-speed computers--had usurped the testosterone-fueled, kill-or-be-killed risk-takers who’d long been the alpha males the world’s largest casino. The quants helped create a digitized money-trading machine that could shift billions around the globe with the click of a mouse. Few realized, though, that in creating this unprecedented machine, men like Muller, Griffin, Asness and Weinstein had sowed the seeds for history’s greatest financial disaster. Drawing on unprecedented access to these four number-crunching titans, The Quants tells the inside story of what they thought and felt in the days and weeks when they helplessly watched much of their net worth vaporize--and wondered just how their mind-bending formulas and genius-level IQ’s had led them so wrong, so fast. |
| data science foundations everfi: Bell's Theorem, Quantum Theory and Conceptions of the Universe Menas Kafatos, 2013-03-09 Bell's Theorem and its associated implications for the nature of the physical world remain topics of great interest. For this reason many meetings have been recently held on the interpretation of quantum theory and the implications of Bell's Theorem. Generally these meetings have been held primarily for quantum physicists and philosophers of science who have been or are actively working on the topic. Nevertheless, other philosophers of science, mathematicians, engineers as well as members of the general public have increasingly taken interest in Bell's Theorem and its implications. The Fall Workshop held at George Mason University on October 21 and 22, 1988 and titled Bell's Theorem, Quantum Theory and Conceptions of the Universe was of a more general scope. Not only it attracted experts in the field, it also covered other topics such as the implications of quantum non-locality for the nature of consciousness, cosmology, the anthropic principle, etc. topics usually not covered in previous meetings of this kind. The meeting was attended by more than one hundred ten specialists and other interested people from all over the world. The purpose of the meeting was not to provide a definitive answer to the general questions raised by Bell's Theorem. It is likely that the debate will go on for quite a long time. Rather, it was meant to contribute to the important dialogue between different disciplines. |
| data science foundations everfi: The Data Science Handbook Field Cady, 2017-02-28 A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: • Extensive sample code and tutorials using Python™ along with its technical libraries • Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems • Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity • A wide variety of case studies from industry • Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon. |
| data science foundations everfi: A Hands-On Introduction to Data Science Chirag Shah, 2020-04-02 This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science. |
| data science foundations everfi: Social Inequality and Social Stratification in U.S. Society Christopher Doob, 2015-08-27 Social Inequality – examining our present while understanding our past. Social Inequality and Social Statification in US Society, 1st edition uses a historical and conceptual framework to explain social stratification and social inequality. The historical scope gives context to each issue discussed and allows the reader to understand how each topic has evolved over the course of American history. The authors use qualitative data to help explain socioeconomic issues and connect related topics. Each chapter examines major concepts, so readers can see how an individual’s success in stratified settings often relies heavily on their access to valued resources–types of capital which involve finances, schooling, social networking, and cultural competence. Analyzing the impact of capital types throughout the text helps map out the prospects for individuals, families, and also classes to maintain or alter their position in social-stratification systems. Learning Goals Upon completing this book, readers will be able to: Analyze the four major American classes, as well as how race and gender are linked to inequalities in the United States Understand attempts to reduce social inequality Identify major historical events that have influenced current trends Understand how qualitative sources help reveal the inner workings that accompany people’s struggles with the socioeconomic order Recognize the impact of social-stratification systems on individuals and families |
| data science foundations everfi: Time for the U.S. to Reskill? Organisation for Economic Co-operation and Development, 2013 This study identifies key lessons about the strategic objectives and directions which should form a frame for policy development in the US, including policy on adult learning and schooling. |
| data science foundations everfi: Financial Education and Capability Julie Birkenmaier, Jami Curley, Margaret Sherraden, 2013-02-21 This book introduces the concept of financial capability and assembles the latest evidence from ground-breaking innovations with financially vulnerable families, and links it to education, policy, and practice. It is a key resource for those interested in improving financial education and financial products and services for low-income families. |
| data science foundations everfi: Conceptualising Demand Jenny Rinkinen, Elizabeth Shove, Greg Marsden, 2020-06-01 This book addresses fundamental questions about the very idea of demand: how is it constituted, how does it change and how might it be steered? Conceptualising Demand focuses on five core propositions: that demand is derived from social practices; that it is made and not simply met; that it is materially embedded and temporally unfolding; and that it is modulated through many forms of policy and governance. In working through these claims, the book weaves concepts from the sociology of consumption, science and technology studies, policy analyses and social theories of practice together with empirical cases and new research into such topics as the rise of refrigerated foods, the emergence of online shopping and the transformation of energy demanding services. This innovative book takes a fresh look at the very idea of demand, a concept that is often taken for granted, but that is vital for scholars and students of energy, mobility, climate change and consumption, and anyone interested in the subject. |
| data science foundations everfi: C++ Programming: From Problem Analysis to Program Design D. S. Malik, 2017-05-24 Learn how to program with C++ using today’s definitive choice for your first programming language experience -- C++ PROGRAMMING: FROM PROBLEM ANALYSIS TO PROGRAM DESIGN, 8E. D.S. Malik’s time-tested, user-centered methodology incorporates a strong focus on problem-solving with full-code examples that vividly demonstrate the hows and whys of applying programming concepts and utilizing C++ to work through a problem. Thoroughly updated end-of-chapter exercises, more than 20 extensive new programming exercises, and numerous new examples drawn from Dr. Malik’s experience further strengthen the reader’s understanding of problem solving and program design in this new edition. This book highlights the most important features of C++ 14 Standard with timely discussions that ensure this edition equips you to succeed in your first programming experience and well beyond. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version. |
| data science foundations everfi: Data Science Foundations Tools and Techniques Michael Freeman, Joel Ross, 2018-11-23 The Foundational Hands-On Skills You Need to Dive into Data Science “Freeman and Ross have created the definitive resource for new and aspiring data scientists to learn foundational programming skills.” –From the foreword by Jared Lander, series editor Using data science techniques, you can transform raw data into actionable insights for domains ranging from urban planning to precision medicine. Programming Skills for Data Science brings together all the foundational skills you need to get started, even if you have no programming or data science experience. Leading instructors Michael Freeman and Joel Ross guide you through installing and configuring the tools you need to solve professional-level data science problems, including the widely used R language and Git version-control system. They explain how to wrangle your data into a form where it can be easily used, analyzed, and visualized so others can see the patterns you’ve uncovered. Step by step, you’ll master powerful R programming techniques and troubleshooting skills for probing data in new ways, and at larger scales. Freeman and Ross teach through practical examples and exercises that can be combined into complete data science projects. Everything’s focused on real-world application, so you can quickly start analyzing your own data and getting answers you can act upon. Learn to Install your complete data science environment, including R and RStudio Manage projects efficiently, from version tracking to documentation Host, manage, and collaborate on data science projects with GitHub Master R language fundamentals: syntax, programming concepts, and data structures Load, format, explore, and restructure data for successful analysis Interact with databases and web APIs Master key principles for visualizing data accurately and intuitively Produce engaging, interactive visualizations with ggplot and other R packages Transform analyses into sharable documents and sites with R Markdown Create interactive web data science applications with Shiny Collaborate smoothly as part of a data science team Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| data science foundations everfi: Foundations of Data Science Dr. M. Muthamizh Selvam, Sanjay Srivastava, Dr. R Thiru Murugan, Dr. B Gayathri, 2024-09-05 Foundations of Data Science is a comprehensive guide that introduces key concepts and techniques essential for understanding and analyzing data in the modern world. Foundational topics like statistics, probability, linear algebra, and machine learning, offering practical insights and applications in real-world data science. With a focus on both theory and implementation, it is designed for students and professionals seeking to build a solid grounding in data science principles and develop skills in data-driven problem-solving, analysis, and predictive modeling across diverse domains. |
| data science foundations everfi: The Data Science Workshop Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare, 2020-01-29 Cut through the noise and get real results with a step-by-step approach to data science Key Features Ideal for the data science beginner who is getting started for the first time A data science tutorial with step-by-step exercises and activities that help build key skills Structured to let you progress at your own pace, on your own terms Use your physical print copy to redeem free access to the online interactive edition Book DescriptionYou already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You'll learn from real examples that lead to real results. Throughout The Data Science Workshop, you'll take an engaging step-by-step approach to understanding data science. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your data science book. Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You'll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.What you will learn Find out the key differences between supervised and unsupervised learning Manipulate and analyze data using scikit-learn and pandas libraries Learn about different algorithms such as regression, classification, and clustering Discover advanced techniques to improve model ensembling and accuracy Speed up the process of creating new features with automated feature tool Simplify machine learning using open source Python packages Who this book is forOur goal at Packt is to help you be successful, in whatever it is you choose to do. The Data Science Workshop is an ideal data science tutorial for the data science beginner who is just getting started. Pick up a Workshop today and let Packt help you develop skills that stick with you for life. |
| data science foundations everfi: First Course in Algebra Joseph Antonius Nyberg, 1932 |
| data science foundations everfi: Java how to Program Paul J. Deitel, Harvey M. Deitel, 2015 Java How to Program (Late Objects), Tenth Edition is intended for use in the Java programming course. It also serves as a useful reference and self-study tutorial to Java programming. The Deitels' groundbreaking How to Program series offers unparalleled breadth and depth of object-oriented programming concepts and intermediate-level topics for further study. Java How to Program (Late Objects), Tenth Edition, teaches programming by presenting the concepts in the context of full working programs. The Late Objects Version delays coverage of class development, first presenting control structures, methods and arrays material in a non-object-oriented, procedural programming context. Teaching and Learning Experience This program presents a better teaching and learning experience--for you and your students. Teach Programming with the Deitels' Signature Live Code Approach: Java language features are introduced with thousands of lines of code in hundreds of complete working programs. Use a Late Objects Approach: The Late Objects Version begins with a rich treatment of procedural programming, including two full chapters on control statements and 200+ exercises. Keep Your Course Current: This edition can be used with Java SE 7 or Java SE 8, and is up-to-date with the latest technologies and advancements. Facilitate Learning with Outstanding Applied Pedagogy: Making a Difference exercise sets, projects, and hundreds of valuable programming tips help students apply concepts. Support Instructors and Students: Student and instructor resources are available to expand on the topics presented in the text. |
| data science foundations everfi: Lifetime Health , 2009 |
| data science foundations everfi: The Tech That Comes Next Amy Sample Ward, Afua Bruce, 2022-03-15 Changing the way we use, develop, and fund technology for social change is possible, and it starts with you. The Tech That Comes Next: How Changemakers, Philanthropists, and Technologists Can Build an Equitable World outlines a vision of a more equitable and just world along with practical steps to creating it, appropriately leveraging technology along the way. In the book, you'll find: Strategies for changing culture and investments inside social impact organizations Ways to change technology development so it incorporates more of society Examples of data, security, and privacy laws and policies that need to change to protect vulnerable populations and advance positive change Ideal for nonprofit leaders, social activists, policymakers, technologists, entrepreneurs, founders, managers, and other business leaders, The Tech That Comes Next belongs in the libraries of anyone who envisions a world in which technology helps advance, rather than hinders, positive social change. |
| data science foundations everfi: Energizing Brain Breaks David Sladkey, 2013-01-15 The fastest way to keep your students engaged It′s an all too familiar sight: that glazed look in your students′ eyes. They′ve been sitting or listening for too long. What they need is an Energizing Brain Break--a quick physical and mental challenge that′s like hitting the refresh button on your computer, but for your students. This practical full-color flip book contains 50 highly effective, classroom-tested brain breaks that you can put to immediate use across the grades. No preparation or supplies are required; just one to two minutes of your time when you see a need. You′ll find pictures, directions, and online videos for activities such as: Slap Count Letters: students alternate slapping each other′s hands while spelling a word Rock, Paper, Scissors, Math: partners reveal a certain number of fingers to each other, and the first person to add them together wins Bizz-Buzz: groups of students count from 1 to 40 using a combination of numbers and words There′s no better way to help students remain sharp and alert, reenergized to take on the next task of learning! |
| data science foundations everfi: College Drinking and Drug Use Helene Raskin White, David L. Rabiner, 2011-11-14 Substance use among college students can result in serious academic and safety problems and have long-term negative repercussions. This state-of-the-art volume draws on the latest research on students' alcohol and drug use to provide useful suggestions for how to address this critical issue on college campuses. Leading researchers from multiple disciplines examine the prevalence and nature of substance use by students; biological and neuropsychological considerations; psychological and social aspects; prevention; and policy. Exemplary programs are presented -- including brief interventions, comprehensive prevention programs, and recovery support programs -- enhancing the utility of the book for campus-based clinicians and administrators. This title is part of The Duke Series in Child Development and Public Policy, edited by Kenneth A. Dodge and Martha Putallaz. |
| data science foundations everfi: Data Science for Dummies Lillian Pierson, 2015-07-13 This is the hardcover format of Data Science For Dummies. Discover how data science can help you gain in-depth insight into your business - the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles in organizations. Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organizations massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, youll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. Provides a background in data science fundamentals before moving on to working with relational databases and unstructured data and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark Its a big, big data world out there - let this hardcover format of Data Science For Dummies help you harness its power and gain a competitive edge for your organization. |
| data science foundations everfi: Real Estate Finance and Investments William B. Brueggeman, Jeffrey D. Fisher, 2005 Real Estate Finance & Investments is today's most indispensable, hands-on look at the increasingly vital arena of real estate partnerships, secondary mortgage markets, and fixed- and adjustable- rate mortgages. Updates to this edition include completely revised coverage of REITs, expanded coverage of CMBS, more detail on how underlying economic factors affect property value, and short readings based on current events. |
| data science foundations everfi: Digital Libraries for Open Knowledge Eva Méndez, Fabio Crestani, Cristina Ribeiro, Gabriel David, João Correia Lopes, 2018-09-04 This book constitutes the proceedings of the 22nd International Conference on Theory and Practice of Digital Libraries, TPDL 2018, held in Porto, Portugal, in September 2018. The 51 full papers, 17 short papers, and 13 poster and tutorial papers presented in this volume were carefully reviewed and selected from 81 submissions. The general theme of TPDL 2018 was Digital Libraries for Open Knowledge. The papers present a wide range of the following topics: Metadata, Entity Disambiguation, Data Management, Scholarly Communication, Digital Humanities, User Interaction, Resources, Information Extraction, Information Retrieval, Recommendation. |
| data science foundations everfi: Data Science Programming All-in-One For Dummies John Paul Mueller, Luca Massaron, 2020-01-09 Your logical, linear guide to the fundamentals of data science programming Data science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models. Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Get grounded: the ideal start for new data professionals What lies ahead: learn about specific areas that data is transforming Be meaningful: find out how to tell your data story See clearly: pick up the art of visualization Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s! |
| data science foundations everfi: The Oxford Illustrated Science Dictionary Oxford University Press, 2012 The Oxford Illustrated Science Dictionary explains academic vocabulary at a level appropriate for high-beginning and intermediate students, which accelerates their mastery of content and allows them to be successful in content-area classes and Content reviewed and approved by nationally accredited science and math standards experts |
| data science foundations everfi: Introduction to Data Science Laura Igual, Santi Seguí, 2017-02-22 This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website. |
| data science foundations everfi: Workplace Readiness SADDLEBACK EDUCATIONAL PUBLISHING., 2021-08-31 Theme: Hi-Lo, life skills, career, achieve independence, skills, job success, job skills, There's more to finding a job than simply applying. First, figure out what you'd like to do for a living. Think ahead and set career goals. Understand what training and education you'll need to reach your dream. Then begin your job search, looking for work that aligns with your goals. Grab potential employers' interest with a polished cover letter and resume, then impress them further in an interview. You'll be ready for the workplace in no time. Combining practical content with visual appeal, the Life Skills Handbooks read more like magazines than books. These 120-page handbooks are designed to teach life skills to today's teens in an approachable and non-threatening way. Realistic scenarios help teens grasp the relevance of the information in these books, and tables, graphs, and charts add to students' understanding. Essential vocabulary is featured to help students build real-world literacy. |
| data science foundations everfi: Data Science Fundamentals Part 1 Jonathan Dinu, 2017 20 Hours of Video Instruction Data Science Fundamentals LiveLessons teaches you the foundational concepts, theory, and techniques you need to know to become an effective data scientist. The videos present you with applied, example-driven lessons in Python and its associated ecosystem of libraries, where you get your hands dirty with real datasets and see real results. Description If nothing else, by the end of this video course you will have analyzed a number of datasets from the wild, built a handful of applications, and applied machine learning algorithms in meaningful ways to get real results. And along the way you learn the best practices and computational techniques used by a professional data scientist. More specifically, you learn how to acquire data that is openly accessible on the Internet by working with APIs. You learn how to parse XML and JSON data to load it into a relational database. About the Instructor Jonathan Dinu is an author, researcher, and most importantly, an educator. He is currently pursuing a Ph.D. in Computer Science at Carnegie Mellon's Human Computer Interaction Institute (HCII), where he is working to democratize machine learning and artificial intelligence through interpretable and interactive algorithms. Previously, he founded Zipfian Academy (an immersive data science training program acquired by Galvanize), has taught classes at the University of San Francisco, and has built a Data Visualization MOOC with Udacity. In addition to his professional data science experience, he has run data science trainings for a Fortune 500 company and taught workshops at Strata, PyData, and DataWeek (among others). He first discovered his love of all things data while studying Computer Science and Physics at UC Berkeley, and in a former life he worked for Alpine Data Labs developing distributed machine learning algorithms for predictive analytics on Hadoop. Jonathan has always had a passion for sharing the things he has learned in the most creative ways he can. When he is not working with students, you can find him blogging about data, visualization, and education at hopelessoptimism.com or rambling on Twitter jonathandinu. Skill Level Beginner What You Will Learn How to get up and running with a Python data science environment The essentials of Python 3, including object-oriented programming The basics of the data science process and what each step entails How to build a simple (yet powerful) recommendation engine for Air... |
| data science foundations everfi: Data Science Fundamentals Pocket Primer Oswald Campesato, 2021-05-12 As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to the basic concepts of data science using Python 3 and other computer applications. It is intended to be a fast-paced introduction to some basic features of data analytics and also covers statistics, data visualization, linear algebra, and regular expressions. The book includes numerous code samples using Python, NumPy, R, SQL, NoSQL, and Pandas. Companion files with source code and color figures are available. FEATURES: Includes a concise introduction to Python 3 and linear algebra Provides a thorough introduction to data visualization and regular expressions Covers NumPy, Pandas, R, and SQL Introduces probability and statistical concepts Features numerous code samples throughout Companion files with source code and figures |
| data science foundations everfi: Introduction To Algorithms Thomas H Cormen, Charles E Leiserson, Ronald L Rivest, Clifford Stein, 2001 An extensively revised edition of a mathematically rigorous yet accessible introduction to algorithms. |
| data science foundations everfi: Handbook of Systems and Complexity in Health Joachim P Sturmberg, Carmel Martin, 2013-01-09 This book is an introduction to health care as a complex adaptive system, a system that feeds back on itself. The first section introduces systems and complexity theory from a science, historical, epistemological, and technical perspective, describing the principles and mathematics. Subsequent sections build on the health applications of systems science theory, from human physiology to medical decision making, population health and health services research. The aim of the book is to introduce and expand on important population health issues from a systems and complexity perspective, highlight current research developments and their implications for health care delivery, consider their ethical implications, and to suggest directions for and potential pitfalls in the future. |
| data science foundations everfi: The Comprehensive Textbook of Healthcare Simulation Adam I. Levine, Samuel DeMaria Jr., Andrew D Schwartz, Alan J. Sim, 2013-06-18 The Comprehensive Textbook of Healthcare Simulation is a cohesive, single-source reference on all aspects of simulation in medical education and evaluation. It covers the use of simulation in training in each specialty and is aimed at healthcare educators and administrators who are developing their own simulation centers or programs and professional organizations looking to incorporate the technology into their credentialing process. For those already involved in simulation, the book will serve as a state-of-the-art reference that helps them increase their knowledge base, expand their simulation program’s capabilities, and attract new, additional target learners. Features: • Written and edited by pioneers and experts in healthcare simulation • Personal memoirs from simulation pioneers • Each medical specialty covered • Guidance on teaching in the simulated environment • Up-to-date information on current techniques and technologies • Tips from “insiders” on funding, development, accreditation, and marketing of simulation centers • Floor plans of simulation centers from across the United States • Comprehensive glossary of terminology |
| data science foundations everfi: Statistical Foundations of Data Science Jianqing Fan, Runze Li, Cun-Hui Zhang, Hui Zou, 2020-09-20 Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning. |
| data science foundations everfi: Culture, Learning, and Technology Angela D. Benson, Roberto Joseph, Joi L. Moore, 2017-02-17 Culture, Learning, and Technology: Research and Practice provides readers with an overview of the research on culture, learning, and technology (CLT) and introduces the concept of culture-related theoretical frameworks. In 13 chapters, the book explores the theoretical and philosophical views of CLT, presents research studies that examine various aspects of CLT, and showcases projects that employ best practices in CLT. Written for researchers and students in the fields of Educational Technology, Instructional Design, and the Learning Sciences, this volume represents a broad conceptualization of CLT and encompasses a variety of settings. As the first significant collection of research in this emerging field of study, Culture, Learning, and Technology overflows with new insights into the increasing role of technology use across all levels of education. |
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 enable a …
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 to …
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 …
