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| data science degree colorado: Data Science Careers, Training, and Hiring Renata Rawlings-Goss, 2019-08-02 This book is an information packed overview of how to structure a data science career, a data science degree program, and how to hire a data science team, including resources and insights from the authors experience with national and international large-scale data projects as well as industry, academic and government partnerships, education, and workforce. Outlined here are tips and insights into navigating the data ecosystem as it currently stands, including career skills, current training programs, as well as practical hiring help and resources. Also, threaded through the book is the outline of a data ecosystem, as it could ultimately emerge, and how career seekers, training programs, and hiring managers can steer their careers, degree programs, and organizations to align with the broader future of data science. Instead of riding the current wave, the author ultimately seeks to help professionals, programs, and organizations alike prepare a sustainable plan for growth in this ever-changing world of data. The book is divided into three sections, the first “Building Data Careers”, is from the perspective of a potential career seeker interested in a career in data, the second “Building Data Programs” is from the perspective of a newly forming data science degree or training program, and the third “Building Data Talent and Workforce” is from the perspective of a Data and Analytics Hiring Manager. Each is a detailed introduction to the topic with practical steps and professional recommendations. The reason for presenting the book from different points of view is that, in the fast-paced data landscape, it is helpful to each group to more thoroughly understand the desires and challenges of the other. It will, for example, help the career seekers to understand best practices for hiring managers to better position themselves for jobs. It will be invaluable for data training programs to gain the perspective of career seekers, who they want to help and attract as students. Also, hiring managers will not only need data talent to hire, but workforce pipelines that can only come from partnerships with universities, data training programs, and educational experts. The interplay gives a broader perspective from which to build. |
| data science degree colorado: 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 degree colorado: Biology Everywhere Melanie Peffer, 2020-02-28 Biology as explained through the lens of how we experience it as part of our daily lives. Written for a trade audience. |
| data science degree colorado: Biostatistical Methods Stephen W. Looney, 2010-11-10 Leading biostatisticians and biomedical researchers describe many of the key techniques used to solve commonly occurring data analytic problems in molecular biology, and demonstrate how these methods can be used in the development of new markers for exposure to a risk factor or for disease outcomes. Major areas of application include microarray analysis, proteomic studies, image quantitation, genetic susceptibility and association, evaluation of new biomarkers, and power analysis and sample size. |
| data science degree colorado: 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 science degree colorado: Numsense! Data Science for the Layman Annalyn Ng, 2017-03-24 Used in Stanford's CS102 Big Data (Spring 2017) course. Want to get started on data science? Our promise: no math added. This book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations, as well as lots of visuals, all of which are colorblind-friendly. Popular concepts covered include: A/B Testing Anomaly Detection Association Rules Clustering Decision Trees and Random Forests Regression Analysis Social Network Analysis Neural Networks Features: Intuitive explanations and visuals Real-world applications to illustrate each algorithm Point summaries at the end of each chapter Reference sheets comparing the pros and cons of algorithms Glossary list of commonly-used terms With this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions. |
| data science degree colorado: Fundamentals of Data Visualization Claus O. Wilke, 2019-03-18 Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options. This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization. Explore the basic concepts of color as a tool to highlight, distinguish, or represent a value Understand the importance of redundant coding to ensure you provide key information in multiple ways Use the book’s visualizations directory, a graphical guide to commonly used types of data visualizations Get extensive examples of good and bad figures Learn how to use figures in a document or report and how employ them effectively to tell a compelling story |
| data science degree colorado: Urban Cascadia and the Pursuit of Environmental Justice Nik Janos, Corina McKendry, 2021-10-26 In Portland’s harbor, environmental justice groups challenge the EPA for a more thorough cleanup of the Willamette River. Near Olympia, the Puyallup assert their tribal sovereignty and treaty rights to fish. Seattle housing activists demand that Amazon pay to address the affordability crisis it helped create. Urban Cascadia, the infrastructure, social networks, built environments, and non-human animals and plants that are interconnected in the increasingly urbanized bioregion that surrounds Portland, Seattle, and Vancouver, enjoys a reputation for progressive ambitions and forward-thinking green urbanism. Yet legacies of settler colonialism and environmental inequalities contradict these ambitions, even as people strive to achieve those progressive ideals. In this edited volume, historians, geographers, urbanists, and other scholars critically examine these contradictions to better understand the capitalist urbanization of nature, the creation of social and environmental inequalities, and the movements to fight for social and environmental justice. Neither a story of green disillusion nor one of green boosterism, Urban Cascadia and the Pursuit of Environmental Justice reveals how the region can address broader issues of environmental justice, Indigenous sovereignty, and the politics of environmental change. |
| data science degree colorado: Applied Managerial Economics Modern Lectures, Incorporated, 2009-10 |
| data science degree colorado: Critical Sports Studies Nicholas Villanueva, Jr., 2019-12-05 Critical Sports Studies: A Document Reader provides students with a selection of essays that examine social problems in sport. Readers are challenged to critically consider various topics to better understand how the global phenomenon of sport can lead to challenges both on and off the field. The opening chapter introduces the study of sport in society as an academic discipline. Later chapters cover amateurism in sport, sports and politics, and the role of media in |
| data science degree colorado: Colleges That Create Futures Princeton Review, 2016-05-10 KICK-START YOUR CAREER WITH THE RIGHT ON-CAMPUS EXPERIENCE! When it comes to getting the most out of college, the experiences you have outside the classroom are just as important as what you study. Colleges That Create Futures looks beyond the usual “best of” college lists to highlight 50 schools that empower students to discover practical, real-world applications for their talents and interests. The schools in this book feature distinctive research, internship, and hands-on learning programs—all the info you need to help find a college where you can parlay your passion into a successful post-college career. Inside, You'll Find: • In-depth profiles covering career services, internship support, student group activity, alumni satisfaction, noteworthy facilities and programs, and more • Candid assessments of each school’s academics from students, current faculty, and alumni • Unique hands-on learning opportunities for students across majors • Testimonials on career prep from alumni in business, education, law, and much more *************************** What makes Colleges That Create Futures important? You've seen the headlines—lately the news has been full of horror stories about how the college educational system has failed many recent grads who leave school with huge debt, no job prospects, and no experience in the working world. Colleges That Create Futures identifies schools that don't fall into this trap but instead prepare students for successful careers! How are the colleges selected? Schools are selected based on survey results on career services, grad school matriculation, internship support, student group and government activity, alumni activity and salaries, and noteworthy facilities and programs. |
| data science degree colorado: Progress in Statistics J. Gani, 1975-02-01 |
| data science degree colorado: Predictive Analytics Eric Siegel, 2016-01-12 Mesmerizing & fascinating... —The Seattle Post-Intelligencer The Freakonomics of big data. —Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive analytics (aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death — including one health insurance company. How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a |
| data science degree colorado: Analytics and Data Science Amit V. Deokar, Ashish Gupta, Lakshmi S. Iyer, Mary C. Jones, 2017-10-05 This book explores emerging research and pedagogy in analytics and data science that have become core to many businesses as they work to derive value from data. The chapters examine the role of analytics and data science to create, spread, develop and utilize analytics applications for practice. Selected chapters provide a good balance between discussing research advances and pedagogical tools in key topic areas in analytics and data science in a systematic manner. This book also focuses on several business applications of these emerging technologies in decision making, i.e., business analytics. The chapters in Analytics and Data Science: Advances in Research and Pedagogy are written by leading academics and practitioners that participated at the Business Analytics Congress 2015. Applications of analytics and data science technologies in various domains are still evolving. For instance, the explosive growth in big data and social media analytics requires examination of the impact of these technologies and applications on business and society. As organizations in various sectors formulate their IT strategies and investments, it is imperative to understand how various analytics and data science approaches contribute to the improvements in organizational information processing and decision making. Recent advances in computational capacities coupled by improvements in areas such as data warehousing, big data, analytics, semantics, predictive and descriptive analytics, visualization, and real-time analytics have particularly strong implications on the growth of analytics and data science. |
| data science degree colorado: Dialogue Across Difference Patricia Gurin, Biren (Ratnesh) A. Nagda, Ximena Zuniga, 2013-03-15 Due to continuing immigration and increasing racial and ethnic inclusiveness, higher education institutions in the United States are likely to grow ever more diverse in the 21st century. This shift holds both promise and peril: Increased inter-ethnic contact could lead to a more fruitful learning environment that encourages collaboration. On the other hand, social identity and on-campus diversity remain hotly contested issues that often raise intergroup tensions and inhibit discussion. How can we help diverse students learn from each other and gain the competencies they will need in an increasingly multicultural America? Dialogue Across Difference synthesizes three years’ worth of research from an innovative field experiment focused on improving intergroup understanding, relationships and collaboration. The result is a fascinating study of the potential of intergroup dialogue to improve relations across race and gender. First developed in the late 1980s, intergroup dialogues bring together an equal number of students from two different groups – such as people of color and white people, or women and men – to share their perspectives and learn from each other. To test the possible impact of such courses and to develop a standard of best practice, the authors of Dialogue Across Difference incorporated various theories of social psychology, higher education, communication studies and social work to design and implement a uniform curriculum in nine universities across the country. Unlike most studies on intergroup dialogue, this project employed random assignment to enroll more than 1,450 students in experimental and control groups, including in 26 dialogue courses and control groups on race and gender each. Students admitted to the dialogue courses learned about racial and gender inequalities through readings, role-play activities and personal reflections. The authors tracked students’ progress using a mixed-method approach, including longitudinal surveys, content analyses of student papers, interviews of students, and videotapes of sessions. The results are heartening: Over the course of a term, students who participated in intergroup dialogues developed more insight into how members of other groups perceive the world. They also became more thoughtful about the structural underpinnings of inequality, increased their motivation to bridge differences and intergroup empathy, and placed a greater value on diversity and collaborative action. The authors also note that the effects of such courses were evident on nearly all measures. While students did report an initial increase in negative emotions – a possible indication of the difficulty of openly addressing race and gender – that effect was no longer present a year after the course. Overall, the results are remarkably consistent and point to an optimistic conclusion: intergroup dialogue is more than mere talk. It fosters productive communication about and across differences in the service of greater collaboration for equity and justice. Ambitious and timely, Dialogue Across Difference presents a persuasive practical, theoretical and empirical account of the benefits of intergroup dialogue. The data and research presented in this volume offer a useful model for improving relations among different groups not just in the college setting but in the United States as well. |
| data science degree colorado: Leadership in Statistics and Data Science Amanda L. Golbeck, 2021-03-22 This edited collection brings together voices of the strongest thought leaders on diversity, equity and inclusion in the field of statistics and data science, with the goal of encouraging and steering the profession into the regular practice of inclusive and humanistic leadership. It provides futuristic ideas for promoting opportunities for equitable leadership, as well as tested approaches that have already been found to make a difference. It speaks to the challenges and opportunities of leading successful research collaborations and making strong connections within research teams. Curated with a vision that leadership takes a myriad of forms, and that diversity has many dimensions, this volume examines the nuances of leadership within a workplace environment and promotes storytelling and other competencies as critical elements of effective leadership. It makes the case for inclusive and humanistic leadership in statistics and data science, where there often remains a dearth of women and members of certain racial communities among the employees. Titled and non-titled leaders will benefit from the planning, evaluation, and structural tools offered within to contribute inclusive excellence in workplace climate, environment, and culture. |
| data science degree colorado: The Visual Imperative Lindy Ryan, 2016-03-14 Data is powerful. It separates leaders from laggards and it drives business disruption, transformation, and reinvention. Today's most progressive companies are using the power of data to propel their industries into new areas of innovation, specialization, and optimization. The horsepower of new tools and technologies have provided more opportunities than ever to harness, integrate, and interact with massive amounts of disparate data for business insights and value – something that will only continue in the era of the Internet of Things. And, as a new breed of tech-savvy and digitally native knowledge workers rise to the ranks of data scientist and visual analyst, the needs and demands of the people working with data are changing, too. The world of data is changing fast. And, it's becoming more visual. Visual insights are becoming increasingly dominant in information management, and with the reinvigorated role of data visualization, this imperative is a driving force to creating a visual culture of data discovery. The traditional standards of data visualizations are making way for richer, more robust and more advanced visualizations and new ways of seeing and interacting with data. However, while data visualization is a critical tool to exploring and understanding bigger and more diverse and dynamic data, by understanding and embracing our human hardwiring for visual communication and storytelling and properly incorporating key design principles and evolving best practices, we take the next step forward to transform data visualizations from tools into unique visual information assets. - Discusses several years of in-depth industry research and presents vendor tools, approaches, and methodologies in discovery, visualization, and visual analytics - Provides practicable and use case-based experience from advisory work with Fortune 100 and 500 companies across multiple verticals - Presents the next-generation of visual discovery, data storytelling, and the Five Steps to Data Storytelling with Visualization - Explains the Convergence of Visual Analytics and Visual discovery, including how to use tools such as R in statistical and analytic modeling - Covers emerging technologies such as streaming visualization in the IOT (Internet of Things) and streaming animation |
| data science degree colorado: Machine Learning and Data Science in the Oil and Gas Industry Patrick Bangert, 2021-03-04 Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. - Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful - Gain practical understanding of machine learning used in oil and gas operations through contributed case studies - Learn change management skills that will help gain confidence in pursuing the technology - Understand the workflow of a full-scale project and where machine learning benefits (and where it does not) |
| data science degree colorado: Managing the Drug Discovery Process Susan Miller, Walter Moos, Barbara Munk, Stephen Munk, Charles Hart, David Spellmeyer, 2023-03-09 Managing the Drug Discovery Process, Second Edition thoroughly examines the current state of pharmaceutical research and development by providing experienced perspectives on biomedical research, drug hunting and innovation, including the requisite educational paths that enable students to chart a career path in this field. The book also considers the interplay of stakeholders, consumers, and drug firms with respect to a myriad of factors. Since drug research can be a high-risk, high-payoff industry, it is important to students and researchers to understand how to effectively and strategically manage both their careers and the drug discovery process. This new edition takes a closer look at the challenges and opportunities for new medicines and examines not only the current research milieu that will deliver novel therapies, but also how the latest discoveries can be deployed to ensure a robust healthcare and pharmacoeconomic future. All chapters have been revised and expanded with new discussions on remarkable advances including CRISPR and the latest gene therapies, RNA-based technologies being deployed as vaccines as well as therapeutics, checkpoint inhibitors and CAR-T approaches that cure cancer, diagnostics and medical devices, entrepreneurship, and AI. Written in an engaging manner and including memorable insights, this book is aimed at anyone interested in helping to save countless more lives through science. A valuable and compelling resource, this is a must-read for all students, educators, practitioners, and researchers at large—indeed, anyone who touches this critical sphere of global impact—in and around academia and the biotechnology/pharmaceutical industry. - Considers drug discovery in multiple R&D venues - big pharma, large biotech, start-up ventures, academia, and nonprofit research institutes - with a clear description of the degrees and training that will prepare students well for a career in this arena - Analyzes the organization of pharmaceutical R&D, taking into account human resources considerations like recruitment and configuration, management of discovery and development processes, and the coordination of internal research within, and beyond, the organization, including outsourced work - Presents a consistent, well-connected, and logical dialogue that readers will find both comprehensive and approachable - Addresses new areas such as CRISPR gene editing technologies and RNA-based drugs and vaccines, personalized medicine and ethical and moral issues, AI/machine learning and other in silico approaches, as well as completely updating all chapters |
| data science degree colorado: Building Gender Equity in the Academy Sandra Laursen, Ann E. Austin, 2020-11-24 Grounded in scholarship but written for busy institutional leaders, Building Gender Equity in the Academy is a handbook of actionable strategies for faculty and administrators working to improve the inclusion and visibility of women and others who are marginalized in the sciences and in academe more broadly. |
| data science degree colorado: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course. |
| data science degree colorado: Automated Machine Learning for Business Kai R. Larsen, Daniel S. Becker, 2021 This book teaches the full process of how to conduct machine learning in an organizational setting. It develops the problem-solving mind-set needed for machine learning and takes the reader through several exercises using an automated machine learning tool. To build experience with machine learning, the book provides access to the industry-leading AutoML tool, DataRobot, and provides several data sets designed to build deep hands-on knowledge of machinelearning. |
| data science degree colorado: Sustainable Statistical and Data Science Methods and Practices O. Olawale Awe, Eric A. Vance, 2024-01-05 This volume gathers papers presented at the LISA 2020 Sustainability Symposium in Kumasi, Ghana, May 2–6, 2022. They focus on sustainable methods and practices of using statistics and data science to address real-world problems. From utilizing social media for statistical collaboration to predicting obesity among rural women, and from analyzing inflation in Nigeria using machine learning to teaching data science in Africa, this book explores the intersection of data, statistics, and sustainability. With practical applications, code snippets, and case studies, this book offers valuable insights for researchers, policymakers, and data enthusiasts alike. The LISA 2020 Global Network aims to enhance statistical and data science capability in developing countries through the creation of a network of collaboration laboratories (also known as “stat labs”). These stat labs are intended to serve as engines for development by training the next generation of collaborative statisticians and data scientists, providing research infrastructure for researchers, data producers, and decision-makers, and enabling evidence-based decision-making that has a positive impact on society. The research conducted at LISA 2020 focuses on practical methods and applications for sustainable growth of statistical capacity in developing nations. |
| data science degree colorado: Computerworld , 1982-01-18 For more than 40 years, Computerworld has been the leading source of technology news and information for IT influencers worldwide. Computerworld's award-winning Web site (Computerworld.com), twice-monthly publication, focused conference series and custom research form the hub of the world's largest global IT media network. |
| data science degree colorado: Media and Religion Stewart M. Hoover, Nabil Echchaibi, 2021-07-05 The series Religion and Society (RS) contributes to the exploration of religions as social systems- both in Western and non-Western societies; in particular, it examines religions in their differentiation from, and intersection with, other cultural systems, such as art, economy, law and politics. Due attention is given to paradigmatic case or comparative studies that exhibit a clear theoretical orientation with the empirical and historical data of religion and such aspects of religion as ritual, the religious imagination, constructions of tradition, iconography, or media. In addition, the formation of religious communities, their construction of identity, and their relation to society and the wider public are key issues of this series. |
| data science degree colorado: Applied Data Science Martin Braschler, Thilo Stadelmann, Kurt Stockinger, 2019-06-13 This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry. |
| data science degree colorado: Resisting War Oliver Kaplan, 2017-07-20 This book explores how local social organization and cohesion enable covert and overt nonviolent strategies. |
| data science degree colorado: Physical Oceanography and Climate Kris Karnauskas, 2020-04-02 An engaging and accessible textbook focusing on climate dynamics from the perspective of the ocean, specifically interactions between the atmosphere and ocean. It describes the fundamental physics and dynamics governing the behaviour of the ocean, and provides numerous end-of-chapter questions and access to online data sets. |
| data science degree colorado: Black–Latino Relations in U.S. National Politics Rodney E. Hero, Robert R. Preuhs, 2013-01-21 Social science research has frequently found conflict between Latinos and African Americans in urban politics and governance, as well as in the groups' attitudes toward one another. Rodney E. Hero and Robert R. Preuhs analyze whether conflict between these two groups is also found in national politics. Based on extensive evidence on the activities of minority advocacy groups in national politics and the behavior of minority members of Congress, the authors find the relationship between the groups is characterized mainly by non-conflict and a considerable degree of independence. The question of why there appears to be little minority intergroup conflict at the national level of government is also addressed. This is the first systematic study of Black–Latino intergroup relations at the national level of United States politics. |
| data science degree colorado: The School of Library Science [Catalogue] Western Reserve University. School of Library Science, 1910 |
| data science degree colorado: Handbook of Research on STEM Education Carla C. Johnson, Margaret J. Mohr-Schroeder, Tamara J. Moore, Lyn D. English, 2020-04-27 The Handbook of Research on STEM Education represents a groundbreaking and comprehensive synthesis of research and presentation of policy within the realm of science, technology, engineering, and mathematics (STEM) education. What distinguishes this Handbook from others is the nature of integration of the disciplines that is the founding premise for the work – all chapters in this book speak directly to the integration of STEM, rather than discussion of research within the individual content areas. The Handbook of Research on STEM Education explores the most pressing areas of STEM within an international context. Divided into six sections, the authors cover topics including: the nature of STEM, STEM learning, STEM pedagogy, curriculum and assessment, critical issues in STEM, STEM teacher education, and STEM policy and reform. The Handbook utilizes the lens of equity and access by focusing on STEM literacy, early childhood STEM, learners with disabilities, informal STEM, socio-scientific issues, race-related factors, gender equity, cultural-relevancy, and parental involvement. Additionally, discussion of STEM education policy in a variety of countries is included, as well as a focus on engaging business/industry and teachers in advocacy for STEM education. The Handbook’s 37 chapters provide a deep and meaningful landscape of the implementation of STEM over the past two decades. As such, the findings that are presented within provide the reader with clear directions for future research into effective practice and supports for integrated STEM, which are grounded in the literature to date. |
| data science degree colorado: Astronomy Jeffrey O. Bennett, Megan Donahue, Nicholas Schneider, Mark Voit, 2008-06-25 |
| data science degree colorado: Modeling Techniques in Predictive Analytics Thomas W. Miller, 2015 Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you're new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you're already a modeler, programmer, or manager, it will teach you crucial skills you don't yet have. This guide illuminates the discipline through realistic vignettes and intuitive data visualizations-not complex math. Thomas W. Miller, leader of Northwestern University's pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today's key applications for predictive analytics, delivering skills and knowledge to put models to work-and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively. |
| data science degree colorado: Mathematical Tools for Applied Multivariate Analysis Paul E. Green, 2014-05-10 Mathematical Tools for Applied Multivariate Analysis provides information pertinent to the aspects of transformational geometry, matrix algebra, and the calculus that are most relevant for the study of multivariate analysis. This book discusses the mathematical foundations of applied multivariate analysis. Organized into six chapters, this book begins with an overview of the three problems in multiple regression, principal components analysis, and multiple discriminant analysis. This text then presents a standard treatment of the mechanics of matrix algebra, including definitions and operations on matrices, vectors, and determinants. Other chapters consider the topics of eigenstructures and linear transformations that are important to the understanding of multivariate techniques. This book discusses as well the eigenstructures and quadratic forms. The final chapter deals with the geometric aspects of linear transformations. This book is a valuable resource for students. |
| data science degree colorado: Machine Learning for Cybersecurity Cookbook Emmanuel Tsukerman, 2019-11-25 Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection Key FeaturesManage data of varying complexity to protect your system using the Python ecosystemApply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and social engineeringAutomate your daily workflow by addressing various security challenges using the recipes covered in the bookBook Description Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers. You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models. By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach. What you will learnLearn how to build malware classifiers to detect suspicious activitiesApply ML to generate custom malware to pentest your securityUse ML algorithms with complex datasets to implement cybersecurity conceptsCreate neural networks to identify fake videos and imagesSecure your organization from one of the most popular threats – insider threatsDefend against zero-day threats by constructing an anomaly detection systemDetect web vulnerabilities effectively by combining Metasploit and MLUnderstand how to train a model without exposing the training dataWho this book is for This book is for cybersecurity professionals and security researchers who are looking to implement the latest machine learning techniques to boost computer security, and gain insights into securing an organization using red and blue team ML. This recipe-based book will also be useful for data scientists and machine learning developers who want to experiment with smart techniques in the cybersecurity domain. Working knowledge of Python programming and familiarity with cybersecurity fundamentals will help you get the most out of this book. |
| data science degree colorado: Scientific and Technical Aerospace Reports , 1993 |
| data science degree colorado: Environmental and Natural Resource Economics Thomas H. Tietenberg, Lynne Lewis, 2018-03-13 Environmental and Natural Resource Economics is the best-selling text for natural resource economics and environmental economics courses, offering a policy-oriented approach and introducing economic theory and empirical work from the field. Students will leave the course with a global perspective of both environmental and natural resource economics and how they interact. Complemented by a number of case studies showing how underlying economic principles provided the foundation for specific environmental and resource policies, this key text highlights what can be learned from the actual experience. This new, 11th edition includes updated data, a number of new studies and brings a more international focus to the subject. Key features include: Extensive coverage of the major issues including climate change, air and water pollution, sustainable development, and environmental justice. Dedicated chapters on a full range of resources including water, land, forests, fisheries, and recyclables. Introductions to the theory and method of environmental economics including externalities, benefit-cost analysis, valuation methods, and ecosystem goods and services. Boxed ‘Examples’ and ‘Debates’ throughout the text which highlight global examples and major talking points. The text is fully supported with end-of-chapter summaries, discussion questions, and self-test exercises in the book and multiple-choice questions, simulations, references, slides, and an instructor’s manual on the Companion Website. |
| data science degree colorado: Cleaning Data for Effective Data Science David Mertz, 2021-03-31 Think about your data intelligently and ask the right questions Key FeaturesMaster data cleaning techniques necessary to perform real-world data science and machine learning tasksSpot common problems with dirty data and develop flexible solutions from first principlesTest and refine your newly acquired skills through detailed exercises at the end of each chapterBook Description Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the hard way. In a light-hearted and engaging exploration of different tools, techniques, and datasets real and fictitious, Python veteran David Mertz teaches you the ins and outs of data preparation and the essential questions you should be asking of every piece of data you work with. Using a mixture of Python, R, and common command-line tools, Cleaning Data for Effective Data Science follows the data cleaning pipeline from start to end, focusing on helping you understand the principles underlying each step of the process. You'll look at data ingestion of a vast range of tabular, hierarchical, and other data formats, impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features. The long-form exercises at the end of each chapter let you get hands-on with the skills you've acquired along the way, also providing a valuable resource for academic courses. What you will learnIngest and work with common data formats like JSON, CSV, SQL and NoSQL databases, PDF, and binary serialized data structuresUnderstand how and why we use tools such as pandas, SciPy, scikit-learn, Tidyverse, and BashApply useful rules and heuristics for assessing data quality and detecting bias, like Benford’s law and the 68-95-99.7 ruleIdentify and handle unreliable data and outliers, examining z-score and other statistical propertiesImpute sensible values into missing data and use sampling to fix imbalancesUse dimensionality reduction, quantization, one-hot encoding, and other feature engineering techniques to draw out patterns in your dataWork carefully with time series data, performing de-trending and interpolationWho this book is for This book is designed to benefit software developers, data scientists, aspiring data scientists, teachers, and students who work with data. If you want to improve your rigor in data hygiene or are looking for a refresher, this book is for you. Basic familiarity with statistics, general concepts in machine learning, knowledge of a programming language (Python or R), and some exposure to data science are helpful. |
| data science degree colorado: Transformative Ethnic Studies in Schools Christine E. Sleeter, Miguel Zavala, 2020 Drawing on Christine Sleeter's review of research on the academic and social impact of ethnic studies commissioned by the National Education Association, this book will examine the value and forms of teaching and researching ethnic studies. The book employs a diverse conceptual framework, including critical pedagogy, anti-racism, Afrocentrism, Indigeneity, youth participatory action research, and critical multicultural education. The book provides cases of classroom teachers to 'illustrate what such conceptual framework look like when enacted in the classroom, as well as tensions that spring from them within school bureaucracies driven by neoliberalism.' Sleeter and Zavala will also outline ways to conduct research for 'investigating both learning and broader impacts of ethnic research used for liberatory ends'-- |
| data science degree colorado: The Knowledge Illusion Steven Sloman, Philip Fernbach, 2017-03-14 “The Knowledge Illusion is filled with insights on how we should deal with our individual ignorance and collective wisdom.” —Steven Pinker We all think we know more than we actually do. Humans have built hugely complex societies and technologies, but most of us don’t even know how a pen or a toilet works. How have we achieved so much despite understanding so little? Cognitive scientists Steven Sloman and Philip Fernbach argue that we survive and thrive despite our mental shortcomings because we live in a rich community of knowledge. The key to our intelligence lies in the people and things around us. We’re constantly drawing on information and expertise stored outside our heads: in our bodies, our environment, our possessions, and the community with which we interact—and usually we don’t even realize we’re doing it. The human mind is both brilliant and pathetic. We have mastered fire, created democratic institutions, stood on the moon, and sequenced our genome. And yet each of us is error prone, sometimes irrational, and often ignorant. The fundamentally communal nature of intelligence and knowledge explains why we often assume we know more than we really do, why political opinions and false beliefs are so hard to change, and why individual-oriented approaches to education and management frequently fail. But our collaborative minds also enable us to do amazing things. The Knowledge Illusion contends that true genius can be found in the ways we create intelligence using the community around us. |
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 Transnationa…
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; …
Belmont Forum Adopts Open Data Principles for Environmental Chan…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e …
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 …
