| data analysis capstone project: Python for Everybody Charles R. Severance, 2016-04-09 Python for Everybody is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.Python is an easy to use and easy to learn programming language that is freely available on Macintosh, Windows, or Linux computers. So once you learn Python you can use it for the rest of your career without needing to purchase any software.This book uses the Python 3 language. The earlier Python 2 version of this book is titled Python for Informatics: Exploring Information.There are free downloadable electronic copies of this book in various formats and supporting materials for the book at www.pythonlearn.com. The course materials are available to you under a Creative Commons License so you can adapt them to teach your own Python course. |
| data analysis capstone project: Analytics Phil Simon, 2017-07-03 For years, organizations have struggled to make sense out of their data. IT projects designed to provide employees with dashboards, KPIs, and business-intelligence tools often take a year or more to reach the finish line...if they get there at all. This has always been a problem. Today, though, it's downright unacceptable. The world changes faster than ever. Speed has never been more important. By adhering to antiquated methods, firms lose the ability to see nascent trends—and act upon them until it's too late. But what if the process of turning raw data into meaningful insights didn't have to be so painful, time-consuming, and frustrating? What if there were a better way to do analytics? Fortunately, you're in luck... Analytics: The Agile Way is the eighth book from award-winning author and Arizona State University professor Phil Simon. Analytics: The Agile Way demonstrates how progressive organizations such as Google, Nextdoor, and others approach analytics in a fundamentally different way. They are applying the same Agile techniques that software developers have employed for years. They have replaced large batches in favor of smaller ones...and their results will astonish you. Through a series of case studies and examples, Analytics: The Agile Way demonstrates the benefits of this new analytics mind-set: superior access to information, quicker insights, and the ability to spot trends far ahead of your competitors. |
| data analysis capstone project: Data Analysis Foundations with Python Cuantum Technologies LLC, 2024-06-12 Dive into data analysis with Python, starting from the basics to advanced techniques. This course covers Python programming, data manipulation with Pandas, data visualization, exploratory data analysis, and machine learning. Key Features From Python basics to advanced data analysis techniques. Apply your skills to practical scenarios through real-world case studies. Detailed projects and quizzes to help gain the necessary skills. Book DescriptionEmbark on a comprehensive journey through data analysis with Python. Begin with an introduction to data analysis and Python, setting a strong foundation before delving into Python programming basics. Learn to set up your data analysis environment, ensuring you have the necessary tools and libraries at your fingertips. As you progress, gain proficiency in NumPy for numerical operations and Pandas for data manipulation, mastering the skills to handle and transform data efficiently. Proceed to data visualization with Matplotlib and Seaborn, where you'll create insightful visualizations to uncover patterns and trends. Understand the core principles of exploratory data analysis (EDA) and data preprocessing, preparing your data for robust analysis. Explore probability theory and hypothesis testing to make data-driven conclusions and get introduced to the fundamentals of machine learning. Delve into supervised and unsupervised learning techniques, laying the groundwork for predictive modeling. To solidify your knowledge, engage with two practical case studies: sales data analysis and social media sentiment analysis. These real-world applications will demonstrate best practices and provide valuable tips for your data analysis projects.What you will learn Develop a strong foundation in Python for data analysis. Manipulate and analyze data using NumPy and Pandas. Create insightful data visualizations with Matplotlib and Seaborn. Understand and apply probability theory and hypothesis testing. Implement supervised and unsupervised machine learning algorithms. Execute real-world data analysis projects with confidence. Who this book is for This course adopts a hands-on approach, seamlessly blending theoretical lessons with practical exercises and real-world case studies. Practical exercises are designed to apply theoretical knowledge, providing learners with the opportunity to experiment and learn through doing. Real-world applications and examples are integrated throughout the course to contextualize concepts, making the learning process engaging, relevant, and effective. By the end of the course, students will have a thorough understanding of the subject matter and the ability to apply their knowledge in practical scenarios. |
| data analysis capstone project: Undertaking Capstone Projects in Education Jolanta Burke, Majella Dempsey, 2021-12-30 Undertaking Capstone Projects in Education provides students with all of the information required to successfully design and complete a capstone project. Guiding the reader in a step-by-step process, this book covers how to create a question, select a topic of interest, and apply the best possible design solutions. Structured in a way that will help readers build their skills, chapters explore all aspects of the capstone project from the inception of the idea, to laying the foundations, designing the project, analysing the data, and presenting the findings. Filled with examples and written in a friendly and collaborative style, this key guide uses simple language and easy-to-understand examples to unpack complex research issues. This book is essential reading for students and anyone interested in undertaking a capstone project in the field of education. |
| data analysis capstone project: Social Work Capstone Projects John Poulin, PhD, MSW, Stephen Kauffman, PhD, Travis Sky Ingersoll, MED, MSW, PhD, 2021-05-29 The only practical guide for helping social work students create high-quality applied capstone research projects from start to finish This “mentor-in-a-book” provides social work students with invaluable information on designing, implementing, and presenting first-rate applied research projects focused on improving social work programs and services. Taking students step-by-step through the entire process, the book helps students plan their projects by providing descriptions of the various research methodologies that can be used to improve social work programs and services. It offers extensive instruction on how to write effectively by providing detailed information on all written components of capstone research projects, as well as the dos and don’ts of writing research reports. Covering data collection methods, program evaluation, organization and community needs assessments, practice-effectiveness studies, and quantitative and qualitative data analysis, this brand-new book also addresses best practices for presenting findings upon completion of the applied research project. Additional features include abundant case examples demonstrating the application of theory to practice and an examination of both qualitative and quantitative research approaches, while also helping students demonstrate social work practice competencies within their capstone projects. Practice activities in each chapter help students apply knowledge to their research projects; and technology exercises help students master important digital research techniques. A capstone project checklist and competency log help students monitor progress, and QR codes provide supplementary support and resources. Additional faculty resources include competency rubrics, detailed group exercises for each chapter, and a sample syllabus for faculty. Purchase of the book includes digital access for use on most mobile devices or computers. Key Features: Delivers step-by-step information on creating high-quality social work capstone projects from conception through presentation Includes a detailed summary of the major applied research approaches to improving social work programs and services Explains how to research literature and write a problem statement on a social service issue Contains extensive information on how to write effective capstone research papers along with abundant examples Helps students to demonstrate social work practice competencies Offers case examples throughout to demonstrate the application of theory to practice Presents practice activities and technology exercises in each chapter Provides a capstone project checklist and competency log Includes QR codes providing additional resources for each chapter |
| data analysis capstone project: The DNP Degree & Capstone Project Mary Bemker, Barb Schreiner, 2016-02-23 Practical guide to understanding the DNP degree and to completing a successful capstone projectClinical, education, and policy exemplars of successful DNP Capstone projects illustrate the necessary components and approach. Provides guidance on publicizing results and conducting projects as a DNP This textbook focuses on enhancing understanding, and characterizing the Doctor of Nursing Practice degree, and its place in the current healthcare environment. The book offers guidelines for planning and conducting all phases of a DNP capstone project. Examples of successful projects from varied areas of nursing practice are included along with practical tips for publicizing capstone project results to the wider medical community. |
| data analysis capstone project: Practical Data Analysis Using Jupyter Notebook Marc Wintjen, 2020-06-19 Understand data analysis concepts to make accurate decisions based on data using Python programming and Jupyter Notebook Key FeaturesFind out how to use Python code to extract insights from data using real-world examplesWork with structured data and free text sources to answer questions and add value using dataPerform data analysis from scratch with the help of clear explanations for cleaning, transforming, and visualizing dataBook Description Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data. After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps. Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries. By the end of this book, you'll have gained the practical skills you need to analyze data with confidence. What you will learnUnderstand the importance of data literacy and how to communicate effectively using dataFind out how to use Python packages such as NumPy, pandas, Matplotlib, and the Natural Language Toolkit (NLTK) for data analysisWrangle data and create DataFrames using pandasProduce charts and data visualizations using time-series datasetsDiscover relationships and how to join data together using SQLUse NLP techniques to work with unstructured data to create sentiment analysis modelsDiscover patterns in real-world datasets that provide accurate insightsWho this book is for This book is for aspiring data analysts and data scientists looking for hands-on tutorials and real-world examples to understand data analysis concepts using SQL, Python, and Jupyter Notebook. Anyone looking to evolve their skills to become data-driven personally and professionally will also find this book useful. No prior knowledge of data analysis or programming is required to get started with this book. |
| data analysis capstone project: Big Data Analytics with Applications in Insider Threat Detection Bhavani Thuraisingham, Pallabi Parveen, Mohammad Mehedy Masud, Latifur Khan, 2017-11-22 Today's malware mutates randomly to avoid detection, but reactively adaptive malware is more intelligent, learning and adapting to new computer defenses on the fly. Using the same algorithms that antivirus software uses to detect viruses, reactively adaptive malware deploys those algorithms to outwit antivirus defenses and to go undetected. This book provides details of the tools, the types of malware the tools will detect, implementation of the tools in a cloud computing framework and the applications for insider threat detection. |
| data analysis capstone project: Leadership Competencies Of A Clinical Trial Project Manager John Petrera, 2024-06-17 Leadership competencies of a clinical trial project manager is unique in that this qualitative inquiry research project not only explores the specific top leadership competencies of project managers involved in pharmaceutical clinical trials, but the concepts reviewed in this book are applicable broad spectrum to multiple professional fields. The proposed leadership framework combines elements of leadership competencies, project management competencies, personal competencies, and includes a review of the leadership types from the traits theory of leadership. The leadership concepts described are universal and can be applied to improve any leader's abilities. While this book focuses on clinical trial project managers, the concepts and best practices apply to all PMs within pharmaceuticals or in any other field. The derived PM competency framework is transferrable to PMs in numerous industries and may also provide applicable guidance to others, regardless of their profession. Ultimately, the expansion of the PM triangle is a useful concept that many will find interesting. Additionally, personal competencies can improve personal effectiveness, achievements, and actions. This project identified 5 themes to include (a) CTPM experience and knowledge, (b) leadership competencies, (c) leadership types (styles), (d) personal competencies, and (e) project management competency development (PMCD). The 5 themes identified are all critical to understanding the perspectives obtained from the study participants regarding leadership competencies to maximize efficiencies of research and development. The results of this study can (a) potentially assist new CTPMs, (b) provide a refresher for CTPMs seeking improvement, (c) provide support for project managers in general, and (d) may assist hiring managers in determining the leadership skills to seek. The results from this study may support the project, program, and portfolio managers from various industries to better understand the leadership competencies and the overall framework that support project management. At the same time, these 5 themes, interpreted in the broadest terms, may be helpful to you! |
| data analysis capstone project: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-10-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data analysis capstone project: Assembly West Point Association of Graduates (Organization)., 2005 |
| data analysis capstone project: Analytics and Knowledge Management Suliman Hawamdeh, Hsia-Ching Chang, 2018-08-06 The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations. The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics techniques. Analytics, on the other hand, is the examination, interpretation, and discovery of meaningful patterns, trends, and knowledge from data and textual information. It provides the basis for knowledge discovery and completes the cycle in which knowledge management and knowledge utilization happen. Organizations should develop knowledge focuses on data quality, application domain, selecting analytics techniques, and on how to take actions based on patterns and insights derived from analytics. Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics. Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships. This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms. |
| data analysis capstone project: Resumes For Dummies Laura DeCarlo, 2019-02-22 Polish up that old resume—and land your dream job We've all been there: it's time to apply for a job or internship and you have to create or revise your resume. Many questions pop in your head. What do employers want? What skills should I highlight? How do I format this? How do I get noticed? But resume writing doesn't have to be a daunting task. The latest edition of Resumes For Dummies answers all of these questions and more—whether you're a resume rookie, looking for new tips, or want to create that eye-catching winning resume. In this trusted guide, Laura DeCarlo decodes the modern culture of resume writing and offers you insider tips on all the best practices that’ll make your skills shine and your resume pop. Let's start writing! Write effective resumes that will stand out in a crowd Understand Applicant Tracking Systems and how to adapt your resume Keep your resume up with the current culture Position a layoff or other career change and challenge with a positive spin Leverage tips and tricks that give your resume visual power In order to put your best foot forward and stand out in a pile of papers, it’s important to have an excellent and effective resume—and now you can. |
| data analysis capstone project: Teaching Data Analytics Susan A Vowels, Katherine Leaming Goldberg, 2019-06-17 The need for analytics skills is a source of the burgeoning growth in the number of analytics and decision science programs in higher education developed to feed the need for capable employees in this area. The very size and continuing growth of this need means that there is still space for new program development. Schools wishing to pursue business analytics programs intentionally assess the maturity level of their programs and take steps to close the gap. Teaching Data Analytics: Pedagogy and Program Design is a reference for faculty and administrators seeking direction about adding or enhancing analytics offerings at their institutions. It provides guidance by examining best practices from the perspectives of faculty and practitioners. By emphasizing the connection of data analytics to organizational success, it reviews the position of analytics and decision science programs in higher education, and to review the critical connection between this area of study and career opportunities. The book features: A variety of perspectives ranging from the scholarly theoretical to the practitioner applied An in-depth look into a wide breadth of skills from closely technology-focused to robustly soft human connection skills Resources for existing faculty to acquire and maintain additional analytics-relevant skills that can enrich their current course offerings. Acknowledging the dichotomy between data analytics and data science, this book emphasizes data analytics rather than data science, although the book does touch upon the data science realm. Starting with industry perspectives, the book covers the applied world of data analytics, covering necessary skills and applications, as well as developing compelling visualizations. It then dives into pedagogical and program design approaches in data analytics education and concludes with ideas for program design tactics. This reference is a launching point for discussions about how to connect industry’s need for skilled data analysts to higher education’s need to design a rigorous curriculum that promotes student critical thinking, communication, and ethical skills. It also provides insight into adding new elements to existing data analytics courses and for taking the next step in adding data analytics offerings, whether it be incorporating additional analytics assignments into existing courses, offering one course designed for undergraduates, or an integrated program designed for graduate students. |
| data analysis capstone project: The Public Productivity and Performance Handbook Marc Holzer, Andrew Ballard, 2021-07-25 A productive society is dependent upon high-performing government. This third edition of The Public Performance and Productivity Handbook includes chapters from leading scholars, consultants, and practitioners to explore all of the core elements of improvement. Completely revised and focused on best practice, the handbook comprehensively explores managing for high performance, measurement and analysis, costs and finances, human resources, and cutting-edge organizational tools. Its coverage of new and systematic management approaches and well-defined measurement systems provides guidance for organizations of all sizes to improve productivity and performance. The contributors discuss such topics as accountability, organizational effectiveness after budget cuts, the complementary roles of human capital and “big data,” and how to teach performance management in the classroom and in public organizations. The handbook is accompanied by an online companion volume providing examples of performance measurement and improvement manuals across a wide variety of public organizations. The Public Performance and Productivity Handbook, Third Edition, is required reading for all public administration practitioners, as well as for students and scholars interested in the state of the public performance and productivity field. |
| data analysis capstone project: Undertaking Capstone and Final Year Projects in Psychology Jolanta Burke, Majella Dempsey, 2022-09-02 Undertaking Capstone and Final Year Projects in Psychology serves a seminal purpose in guiding its readers to create a capstone project. The text employs traditional and emerging methodologies and methods in order to posit an exhaustive approach that the psychology students can adopt to see their project to fruition. The text aims at fortifying the reader’s skills through the structure of its chapters as they begin to work on their capstone or final year project. The chapters collectively explore the varied aspects that are involved in the completion of a final year project, that is, beginning from the inception of the idea to laying the foundation, designing the project, analysing the data, and, finally, presenting the findings. The text guides the reader through each step and provides further guidance on approaching the idea, coming up with the research question, positioning it within the epistemological and ontological context, and constructing the theoretical framework to arrive at the optimal design solutions. The text will be useful for psychology students who are currently completing a capstone or a final year project. It is further aimed at psychology students who will subsequently be working on a project and are looking forward to gaining cognisance regarding the approach and the methodology to be adopted for the same. |
| data analysis capstone project: The Entry Level Occupational Therapy Doctorate Capstone Elizabeth DeIuliis, Julie Bednarski, 2024-06-01 The purpose of The Entry Level Occupational Therapy Doctorate Capstone: A Framework for The Experience and Project is to provide a step-by-step guide for the development, planning, implementation and dissemination of the entry-level occupational therapy doctoral capstone experience and project. The first entry-level occupational therapy doctorate program was established in 1999, but even now there is a scarcity of occupational therapy resources to guide faculty, prepare students and to socialize mentors to the capstone experience and project. The Entry Level Occupational Therapy Doctorate Capstone by Drs. Elizabeth DeIuliis and Julie Bednarski is the first available resource in the field of occupational therapy devoted to the doctoral capstone. Each chapter provides sample resources and useful documents appropriate for use with occupational therapy doctoral students, faculty, capstone coordinators and site mentors. Included Inside: Templates to develop the MOU, individualized doctoral student objectives, and evaluations Examples of how to structure capstone project proposals Learning activities to guide the literature search and development of a problem statement Strategies of how to approach sustainability and program evaluation of the capstone project Recommendations for structure and formatting of the final written document Additional scholarly products derived from the project Other scholarly deliverables including formats for professional presentations and submissible papers The Entry Level Occupational Therapy Doctorate Capstone: A Framework for The Experience and Project will be the first of its kind to serve as a textbook to provide recommendations that will benefit various stakeholders among the capstone team. |
| data analysis capstone project: Future U.S. Workforce for Geospatial Intelligence National Research Council, Policy and Global Affairs, Board on Higher Education and Workforce, Division on Earth and Life Studies, Board on Earth Sciences and Resources, Committee on the Future U.S. Workforce for Geospatial Intelligence, 2013-04-28 We live in a changing world with multiple and evolving threats to national security, including terrorism, asymmetrical warfare (conflicts between agents with different military powers or tactics), and social unrest. Visually depicting and assessing these threats using imagery and other geographically-referenced information is the mission of the National Geospatial-Intelligence Agency (NGA). As the nature of the threat evolves, so do the tools, knowledge, and skills needed to respond. The challenge for NGA is to maintain a workforce that can deal with evolving threats to national security, ongoing scientific and technological advances, and changing skills and expectations of workers. Future U.S. Workforce for Geospatial Intelligence assesses the supply of expertise in 10 geospatial intelligence (GEOINT) fields, including 5 traditional areas (geodesy and geophysics, photogrammetry, remote sensing, cartographic science, and geographic information systems and geospatial analysis) and 5 emerging areas that could improve geospatial intelligence (GEOINT fusion, crowdsourcing, human geography, visual analytics, and forecasting). The report also identifies gaps in expertise relative to NGA's needs and suggests ways to ensure an adequate supply of geospatial intelligence expertise over the next 20 years. |
| data analysis capstone project: MICCAI 2012 Workshop on Multi-Atlas Labeling Bennett Landman, Annemie Ribbens, Blake Lucas, Christos, Christos Davatzikos,, Brian Avants, Christian Ledig, Da Ma, Daniel Rueckert, Dirk Vandermeulen, Frederik Maes, Guray Erus, Jiahui Wang, Holly Holmes, Hongzhi Wang, Jimit Doshi, Joe Kornegay, Jose Manjon, Alexander Hammers, Alireza Akhondi-Asl, Andrew Asman, 2012-08-26 Characterization of anatomical structure through segmentation has become essential for morphological assessment and localizing quantitative measures. Segmentation through registration and atlas label transfer has proven to be a flexible and fruitful approach as efficient, non-rigid image registration methods have become prevalent. Label transfer segmentation using multiple atlases has helped to bring statistical fusion, shape modeling, and meta-analysis techniques to the forefront of segmentation research. Numerous creative approaches have proposed to use atlas information to apply labels to brain anatomy. However, it is difficult to evaluate the relative advantages and limitations of these methods as they have been applied on very different datasets. This workshop provides a snapshot of the current progress in the field through extended discussions and provides researchers an opportunity to characterize their methods on standardized data in a grand challenge. |
| data analysis capstone project: Pedagogies of Biomedical Science Donna Johnson, 2024-05-31 This book confronts the continually evolving nature of biomedical science education by providing a robust account of learning pedagogies and best practice for scholars and researchers in the field. Rather than considering subdisciplines of biomedical science education separately, the volume takes a holistic approach and considers the complexities of teaching biomedical science as a whole, providing a nuanced overview of how a particular practice fits in such a course overall, as well as providing support for development within the reader’s own subdiscipline. Ultimately, this holistic approach allows for expansive discussion of relevant pedagogical approaches that will directly inform innovations in the contemporary teaching of biomedical science education. Novel in approach and underpinned by the latest in research innovations, this book will appeal to scholars, researchers and postgraduate students in the fields of medical education, higher education, and curriculum studies. Policy makers involved with health education and promotion as well as educational research will also benefit from the volume. |
| data analysis capstone project: Proceedings of the International Conference on Advancing and Redesigning Education 2023 Mohd Fakhizan bin Romlie, |
| data analysis capstone project: Analyzing and Securing Social Networks Bhavani Thuraisingham, Satyen Abrol, Raymond Heatherly, Murat Kantarcioglu, Vaibhav Khadilkar, Latifur Khan, 2016-04-06 Analyzing and Securing Social Networks focuses on the two major technologies that have been developed for online social networks (OSNs): (i) data mining technologies for analyzing these networks and extracting useful information such as location, demographics, and sentiments of the participants of the network, and (ii) security and privacy technolo |
| data analysis capstone project: DNP Education, Practice, and Policy Stephanie W. Ahmed, DNP, FNP-BC, DPNAP, Linda C. Andrist, PhD, RN, WHNP, Sheila M. Davis, DNP, ANP-BC, FAAN, Valerie J. Fuller, PhD, DNP, AGACNP-BC, FNP-BC, FNAP, FAANP, 2012-07-11 Named a 2013 Doody's Core Title! This is an excellent book for both students and current DNPs. The primary areas it addresses--leadership, healthcare policy, and information technology---are essential for the advanced practice nurse to function as a change agent in today's healthcare environment. The book challenges DNPs to engage in clinical practice to the full scope of their capabilities.--Score: 100, 5 Stars. Doody's Medical Reviews This is the only professional issues-oriented Doctor of Nursing Practice (DNP) text to fully integrate all eight American Association of Colleges of Nursing DNP competencies into one volume. It defines practice scholarship for the DNP role and facilitates the sound development of key leadership skills that enable DNP graduates to effectively influence politics and health care policy in order to improve patient and population health care outcomes. The text focuses on the educational requirements of DNPs engaged in the arenas of leadership, health care policy, and information technology. It covers the growth and development of the DNP role, particularly in the context of contemporary health care challenges. With a focus on the Capstone Project, the text addresses the relationship of the DNP role to ongoing scholarship. It covers three important essentials of the DNP curriculumóevidence-based practice, health information technology, and outcomes measurementóand how they can be used to transform health care in the 21st century. The textís challenging and thought-provoking content is of particular value not only to students, but also to professors who will welcome the clarity it offers to the highly complex DNP curriculum. Key Features: Simplifies the highly complex DNP curriculum and integrates DNP core competencies throughout Demonstrates the application of core competencies to practice and aggregate care Provides a well-organized supplement to all courses across the DNP curriculum Uses exemplars of students and practicing DNPs to illustrate effective implementation Offers concrete guidance for achieving a thorough understanding of how DNP graduates utilize core competencies |
| data analysis capstone project: Research Handbook on Project Performance Vittal S. Anantatmula, Chakradhar Iyyunni, 2023-03-02 This engaging Research Handbook presents a fresh look at how to improve project performance for the project sponsor, client and end user using a number of empirical research studies. Focusing on project performance concepts and methods, the Handbook provides a fresh look at successful project completions, achieving project objectives, on-time or ahead of time project completion or delivering within budget. |
| data analysis capstone project: Handbook of Citizen Science in Ecology and Conservation Christopher Andrew Lepczyk, Owen D. Boyle, Timothy L. V. Vargo, 2020 Handbook of Citizen Science in Ecology and Conservation is the first practical and comprehensive manual that provides step-by-step instructions for creating natural science research projects that involve collaboration between scientists and the general public. As citizen-science projects become increasingly common, there is a growing need for concrete best practices around planning and implementing successful projects that can allow project leaders to guide and gauge success of projects while ensuring the collection of high-quality data. Based on a variety of case studies from several citizen-science projects, this is the definitive reference guide for all potential citizen-science practitioners, ranging from professors and graduate students to staff at agencies and nongovernmental organizations-- |
| data analysis capstone project: Teaching and Learning Mathematics Online James P. Howard, II, John F. Beyers, 2020-05-10 Online education has become a major component of higher education worldwide. In mathematics and statistics courses, there exists a number of challenges that are unique to the teaching and learning of mathematics and statistics in an online environment. These challenges are deeply connected to already existing difficulties related to math anxiety, conceptual understanding of mathematical ideas, communicating mathematically, and the appropriate use of technology. Teaching and Learning Mathematics Online bridges these issues by presenting meaningful and practical solutions for teaching mathematics and statistics online. It focuses on the problems observed by mathematics instructors currently working in the field who strive to hone their craft and share best practices with our professional community. The book provides a set of standard practices, improving the quality of online teaching and the learning of mathematics. Instructors will benefit from learning new techniques and approaches to delivering content. Features Based on the experiences of working educators in the field Assimilates the latest technology developments for interactive distance education Focuses on mathematical education for developing early mathematics courses |
| data analysis capstone project: The Palgrave Handbook of Political Research Pedagogy Daniel J. Mallinson, Julia Marin Hellwege, Eric D. Loepp, 2021-09-15 This Handbook addresses why political science programs teach the research process and how instructors come to teach these courses and develop their pedagogy. Contributors offer diverse perspectives on pedagogy, student audience, and the role of research in their curricula. Across four sections—information literacy, research design, research methods, and research writing—authors share personal reflections that showcase the evolution of their pedagogy. Each chapter offers best practices that can serve the wider community of teachers. Ultimately, this text focuses less on the technical substance of the research process and more on the experiences that have guided instructors’ philosophies and practices related to teaching it. |
| data analysis capstone project: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data analysis capstone project: Clinical Analytics and Data Management for the DNP Martha L. Sylvia, PhD, MBA, RN, Mary F. Terhaar, PhD, RN, ANEF, FAAN, 2023-01-18 Praise for the first edition: DNP students may struggle with data management, since their projects are not research but quality improvement, and this book covers the subject well. I recommend it for DNP students for use during their capstone projects. Score: 98, 5 Stars -- Doody's Medical Reviews This unique text and reference—the only book to address the full spectrum of clinical data management for the DNP student—instills a fundamental understanding of how clinical data is gathered, used, and analyzed, and how to incorporate this data into a quality DNP project. The new third edition is updated to reflect changes in national health policy such as quality measurements, bundled payments for specialty care, and Advances to the Affordable Care Act (ACA) and evolving programs through the Centers for Medicare and Medicaid Services (CMS). The third edition reflects the revision of 2021 AACN Essentials and provides data sets and other examples in Excel and SPSS format, along with several new chapters. This resource takes the DNP student step-by-step through the complete process of data management, from planning through presentation, clinical applications of data management that are discipline-specific, and customization of statistical techniques to address clinical data management goals. Chapters are brimming with descriptions, resources, and exemplars that are helpful to both faculty and students. Topics spotlight requisite competencies for DNP clinicians and leaders such as phases of clinical data management, statistics and analytics, assessment of clinical and economic outcomes, value-based care, quality improvement, benchmarking, and data visualization. A progressive case study highlights multiple techniques and methods throughout the text. New to the Third Edition: New Chapter: Using EMR Data for the DNP Project New chapter solidifies link between EBP and Analytics for the DNP project New chapter highlights use of workflow mapping to transition between current and future state, while simultaneously visualizing process measures needed to ensure success of the DNP project Includes more examples to provide practical application exercises for students Key Features: Disseminates robust strategies for using available data from everyday practice to support trustworthy evaluation of outcomes Uses multiple tools to meet data management objectives [SPSS, Excel®, Tableau] Presents case studies to illustrate multiple techniques and methods throughout chapters Includes specific examples of the application and utility of these techniques using software that is familiar to graduate nursing students Offers real world examples of completed DNP projects Provides Instructor’s Manual, PowerPoint slides, data sets in SPSS and Excel, and forms for completion of data management and evaluation plan |
| data analysis capstone project: Closing the Analytics Talent Gap Jennifer Priestley, Robert McGrath, 2021-05-03 How can we recruit out of your program? We have a project – how do we reach out to your students? If we do research together who owns it? We have employees who need to upskill in analytics – can you help me with that? How much does all of this cost? Managers and executives are increasingly asking university professors such questions as they deal with a critical shortage of skilled data analysts. At the same time, academics are asking such questions as: How can I bring a real analytical project in the classroom? How can I get real data to help my students develop the skills necessary to be a data scientist? Is what I am teaching in the classroom aligned with the demands of the market for analytical talent? After spending several years answering almost daily e-mails and telephone calls from business managers asking for staffing help and aiding fellow academics with their analytics teaching needs, Dr. Jennifer Priestley of Kennesaw State University and Dr. Robert McGrath of the University of New Hampshire wrote Closing the Analytics Talent Gap: An Executive’s Guide to Working with Universities. The book builds a bridge between university analytics programs and business organizations. It promotes a dialog that enables executives to learn how universities can help them find strategically important personnel and universities to learn how they can develop and educate this personnel. Organizations are facing previously unforeseen challenges related to the translation of massive amounts of data – structured and unstructured, static and in-motion, voice, text, and image – into information to solve current challenges and anticipate new ones. The advent of analytics and data science also presents universities with unforeseen challenges of providing learning through application. This book helps both organizations with finding data natives and universities with educating students to develop the facility to work in a multi-faceted and complex data environment. . |
| data analysis capstone project: Accelerating Digital Transformation Of Smes Clarence Goh, Yuanto Kusnadi, Benjamin Lee, Gary S C Pan, Poh-sun Seow, 2023-02-10 Digital transformation is happening across all industries worldwide, including in Singapore. This book seeks to provide insights on how small and medium enterprises (SMEs) can embark on their own journeys of digital transformation, while at the same time remaining agile in responding to industry and consumer needs. It will outline how firms can foster a culture of business transformation to improve efficiency and productivity; elaborate on how the COVID-19 pandemic has accelerated the digital transformation process, and how it has provided new opportunities; present a roadmap on how SMEs can navigate through the artificial and data analytics revolution; and provide recommendations on how SMEs can partner with institutes of higher learning. It concludes by elaborating on the skillsets and capabilities needed to drive digitalisation. |
| data analysis capstone project: Advanced Methodologies and Technologies in Network Architecture, Mobile Computing, and Data Analytics Khosrow-Pour, D.B.A., Mehdi, 2018-10-19 From cloud computing to data analytics, society stores vast supplies of information through wireless networks and mobile computing. As organizations are becoming increasingly more wireless, ensuring the security and seamless function of electronic gadgets while creating a strong network is imperative. Advanced Methodologies and Technologies in Network Architecture, Mobile Computing, and Data Analytics highlights the challenges associated with creating a strong network architecture in a perpetually online society. Readers will learn various methods in building a seamless mobile computing option and the most effective means of analyzing big data. This book is an important resource for information technology professionals, software developers, data analysts, graduate-level students, researchers, computer engineers, and IT specialists seeking modern information on emerging methods in data mining, information technology, and wireless networks. |
| data analysis capstone project: Army Sustainment , 2015 The Department of the Army's official professional bulletin on sustainment, publishing timely, authoritative information on Army and Defense sustainment plans, programs, policies, operations, procedures, and doctrine for the benefit of all sustainment personnel. |
| data analysis capstone project: Applied Research Methods in Urban and Regional Planning Yanmei Li, Sumei Zhang, 2022-04-12 This book introduces the fundamentals of research methods and how they apply to the discipline of urban and regional planning. Written at a level appropriate for upper-level undergraduate and beginning master’s level students, the text fills a gap in the literature for textbooks on urban planning. Additionally, the book can be used as a reference for planning practitioners and researchers when analyzing quantitative and qualitative data in urban and regional planning and related fields. The volume does not assume advanced knowledge of mathematical formulas. Rather, it begins with the essentials of research methods, such as the identification of the research problems in planning, the literature review, data collection and presentation, descriptive data analysis, and report of findings. Its discipline-specific topics include field research methods, qualitative data analysis, economic and demographic analysis, evaluation research, and methods in sub-disciplines such as land use planning, transportation planning, environmental planning, and housing analysis. Designed with instruction in mind, this book features downloadable materials, including learning outcomes, chapter highlights, chapter review questions, datasets, and certain Excel models. Students will be able to download review questions to enhance the learning process and datasets to practice methods. |
| data analysis capstone project: Clinical Research for the Doctor of Nursing Practice Allison J. Terry, 2017-06-19 Clinical Research for the Doctor of Nursing Practice, Third Edition is a must-have text focused on teaching students how to conduct research needed for their capstone project. |
| data analysis capstone project: Business Intelligence Career Master Plan Eduardo Chavez, Danny Moncada, 2023-08-31 Learn the foundations of business intelligence, sector trade-offs, organizational structures, and technology stacks while mastering coursework, certifications, and interview success strategies Purchase of the print or Kindle book includes a free PDF eBook Key Features Identify promising job opportunities and ideal entry point into BI Build, design, implement, and maintain BI systems successfully Ace your BI interview with author's expert guidance on certifications, trainings, and courses Book DescriptionNavigating the challenging path of a business intelligence career requires you to consider your expertise, interests, and skills. Business Intelligence Career Master Plan explores key skills like stacks, coursework, certifications, and interview advice, enabling you to make informed decisions about your BI journey. You’ll start by assessing the different roles in BI and matching your skills and career with the tech stack. You’ll then learn to build taxonomy and a data story using visualization types. Additionally, you’ll explore the fundamentals of programming, frontend development, backend development, software development lifecycle, and project management, giving you a broad view of the end-to-end BI process. With the help of the author’s expert advice, you’ll be able to identify what subjects and areas of study are crucial and would add significant value to your skill set. By the end of this book, you’ll be well-equipped to make an informed decision on which of the myriad paths to choose in your business intelligence journey based on your skill set and interests.What you will learn Understand BI roles, roadmap, and technology stack Accelerate your career and land your first job in the BI industry Build the taxonomy of various data sources for your organization Use the AdventureWorks database and PowerBI to build a robust data model Create compelling data stories using data visualization Automate, templatize, standardize, and monitor systems for productivity Who this book is for This book is for BI developers and business analysts who are passionate about data and are looking to advance their proficiency and career in business intelligence. While foundational knowledge of tools like Microsoft Excel is required, having a working knowledge of SQL, Python, Tableau, and major cloud providers such as AWS or GCP will be beneficial. |
| data analysis capstone project: Research Methods Kirsty Williamson, Graeme Johanson, 2017-11-27 Research Methods: Information, Systems, and Contexts, Second Edition, presents up-to-date guidance on how to teach research methods to graduate students and professionals working in information management, information science, librarianship, archives, and records and information systems. It provides a coherent and precise account of current research themes and structures, giving students guidance, appreciation of the scope of research paradigms, and the consequences of specific courses of action. Each of these valuable sections will help users determine the relevance of particular approaches to their own questions. The book presents academics who teach research and information professionals who carry out research with new resources and guidance on lesser-known research paradigms. - Provides up-to-date knowledge of research methods and their applications - Provides a coherent and precise account of current research themes and structures through chapters written by authors who are experts in their fields - Helps students and researchers understand the range of quantitative and qualitative approaches available for research, as well as how to make practical use of them - Provides many illustrations from projects in which authors have been involved, to enhance understanding - Emphasises the nexus between formulation of research question and choice of research methodology - Enables new researchers to understand the implications of their planning decisions |
| data analysis capstone project: Directory of Awards National Science Foundation (U.S.). Directorate for Science and Engineering Education, 1987 |
| data analysis capstone project: Handbook of Research on Big Data Storage and Visualization Techniques Segall, Richard S., Cook, Jeffrey S., 2018-01-05 The digital age has presented an exponential growth in the amount of data available to individuals looking to draw conclusions based on given or collected information across industries. Challenges associated with the analysis, security, sharing, storage, and visualization of large and complex data sets continue to plague data scientists and analysts alike as traditional data processing applications struggle to adequately manage big data. The Handbook of Research on Big Data Storage and Visualization Techniques is a critical scholarly resource that explores big data analytics and technologies and their role in developing a broad understanding of issues pertaining to the use of big data in multidisciplinary fields. Featuring coverage on a broad range of topics, such as architecture patterns, programing systems, and computational energy, this publication is geared towards professionals, researchers, and students seeking current research and application topics on the subject. |
| data analysis capstone project: Principles and Theories of Data Mining With RapidMiner Ramjan, Sarawut, Sunkpho, Jirapon, 2023-05-09 The demand for skilled data scientists is rapidly increasing as more organizations recognize the value of data-driven decision- making. Data science, data management, and data mining are all critical components for various types of organizations, including large and small corporations, academic institutions, and government entities. For companies, these components serve to extract insights and value from their data, empowering them to make evidence-driven decisions and gain a competitive advantage by discovering patterns and trends and avoiding costly mistakes. Academic institutions utilize these tools to analyze large datasets and gain insights into various scientific fields of study, including genetic data, climate data, financial data, and in the social sciences they are used to analyze survey data, behavioral data, and public opinion data. Governments use data science to analyze data that can inform policy decisions, such as identifying areas with high crime rates, determining which regions need infrastructure development, and predicting disease outbreaks. However, individuals who are not data science experts, but are experts within their own fields, may need to apply their experience to the data they must manage, but still struggle to expand their knowledge of how to use data mining tools such as RapidMiner software. Principles and Theories of Data Mining With RapidMiner is a comprehensive guide for students and individuals interested in experimenting with data mining using RapidMiner software. This book takes a practical approach to learning through the RapidMiner tool, with exercises and case studies that demonstrate how to apply data mining techniques to real-world scenarios. Readers will learn essential concepts related to data mining, such as supervised learning, unsupervised learning, association rule mining, categorical data, continuous data, and data quality. Additionally, readers will learn how to apply data mining techniques to popular algorithms, including k-nearest neighbor (K-NN), decision tree, naïve bayes, artificial neural network (ANN), k-means clustering, and probabilistic methods. By the end of the book, readers will have the skills and confidence to use RapidMiner software effectively and efficiently, making it an ideal resource for anyone, whether a student or a professional, who needs to expand their knowledge of data mining with RapidMiner software. |
Data 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 minimum time …
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, released in …
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 from …
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 barriers …
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 collected, …
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 …
