Advertisement
| data analysis case study: Case Studies in Neural Data Analysis Mark A. Kramer, Uri T. Eden, 2016-11-04 A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data. As neural data becomes increasingly complex, neuroscientists now require skills in computer programming, statistics, and data analysis. This book teaches practical neural data analysis techniques by presenting example datasets and developing techniques and tools for analyzing them. Each chapter begins with a specific example of neural data, which motivates mathematical and statistical analysis methods that are then applied to the data. This practical, hands-on approach is unique among data analysis textbooks and guides, and equips the reader with the tools necessary for real-world neural data analysis. The book begins with an introduction to MATLAB, the most common programming platform in neuroscience, which is used in the book. (Readers familiar with MATLAB can skip this chapter and might decide to focus on data type or method type.) The book goes on to cover neural field data and spike train data, spectral analysis, generalized linear models, coherence, and cross-frequency coupling. Each chapter offers a stand-alone case study that can be used separately as part of a targeted investigation. The book includes some mathematical discussion but does not focus on mathematical or statistical theory, emphasizing the practical instead. References are included for readers who want to explore the theoretical more deeply. The data and accompanying MATLAB code are freely available on the authors' website. The book can be used for upper-level undergraduate or graduate courses or as a professional reference. A version of this textbook with all of the examples in Python is available on the MIT Press website. |
| data analysis case study: Humanities Data Analysis Folgert Karsdorp, Mike Kestemont, Allen Riddell, 2021-01-12 A practical guide to data-intensive humanities research using the Python programming language The use of quantitative methods in the humanities and related social sciences has increased considerably in recent years, allowing researchers to discover patterns in a vast range of source materials. Despite this growth, there are few resources addressed to students and scholars who wish to take advantage of these powerful tools. Humanities Data Analysis offers the first intermediate-level guide to quantitative data analysis for humanities students and scholars using the Python programming language. This practical textbook, which assumes a basic knowledge of Python, teaches readers the necessary skills for conducting humanities research in the rapidly developing digital environment. The book begins with an overview of the place of data science in the humanities, and proceeds to cover data carpentry: the essential techniques for gathering, cleaning, representing, and transforming textual and tabular data. Then, drawing from real-world, publicly available data sets that cover a variety of scholarly domains, the book delves into detailed case studies. Focusing on textual data analysis, the authors explore such diverse topics as network analysis, genre theory, onomastics, literacy, author attribution, mapping, stylometry, topic modeling, and time series analysis. Exercises and resources for further reading are provided at the end of each chapter. An ideal resource for humanities students and scholars aiming to take their Python skills to the next level, Humanities Data Analysis illustrates the benefits that quantitative methods can bring to complex research questions. Appropriate for advanced undergraduates, graduate students, and scholars with a basic knowledge of Python Applicable to many humanities disciplines, including history, literature, and sociology Offers real-world case studies using publicly available data sets Provides exercises at the end of each chapter for students to test acquired skills Emphasizes visual storytelling via data visualizations |
| data analysis case study: Data Analysis for Business, Economics, and Policy Gábor Békés, Gábor Kézdi, 2021-05-06 A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data. |
| data analysis case study: Case Studies in Data Analysis Jane F. Gentleman, G.A. Whitmore, 2012-12-06 This volume is a collection of eight Case Studies in Data Analysis that appeared in various issues of the Canadian Journal of Statistics (OS) over a twelve year period from 1982 to 1993. One follow-up article to Case Study No.4 is also included in the volume. The OS's Section on Case Studies in Data Analysis was initiated by a former editor who wanted to increase the analytical content of the journal. We were asked to become Section Co-Editors and to develop a format for the case studies. Each case study presents analyses of a real data set by two or more analysts or teams of analysts working independently in a simulated consulting context. The section aimed at demonstrating the process of statistical analysis and the possible diversity of approaches and conclusions. For each case study, the Co-Editors found a set of real Canadian data, posed what they thought was an interesting statistical problem, and recruited analysts working in Canada who were willing to tackle it. The published case studies describe the data and the problem, and present and discuss the analysts' solutions. For some case studies, the providers of the data were invited to contribute their own analysis. |
| data analysis case study: Applied Functional Data Analysis J.O. Ramsay, B.W. Silverman, 2007-11-23 This book contains the ideas of functional data analysis by a number of case studies. The case studies are accessible to research workers in a wide range of disciplines. Every reader should gain not only a specific understanding of the methods of functional data analysis, but more importantly a general insight into the underlying patterns of thought. There is an associated web site with MATLABr and S?PLUSr implementations of the methods discussed. |
| data analysis case study: Fundamentals of Machine Learning for Predictive Data Analytics, second edition John D. Kelleher, Brian Mac Namee, Aoife D'Arcy, 2020-10-20 The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning. |
| data analysis case study: Multiple Case Study Analysis Robert E. Stake, 2013-04-29 Examining situational complexity is a vital part of social and behavioral science research. This engaging text provides an effective process for studying multiple cases--such as sets of teachers, staff development sessions, or clinics operating in different locations--within one complex program. The process also can be used to investigate broadly occurring phenomena without programmatic links, such as leadership or sibling rivalry. Readers learn to design, analyze, and report studies that balance common issues across the group of cases with the unique features and context of each case. Three actual case reports from a transnational early childhood program illustrate the author's approach, and helpful reproducible worksheets facilitate multicase recording and analysis. |
| data analysis case study: Data Analytics for Pandemics Gitanjali Rahul Shinde, Asmita Balasaheb Kalamkar, Parikshit N. Mahalle, Nilanjan Dey, 2020-08-30 Epidemic trend analysis, timeline progression, prediction, and recommendation are critical for initiating effective public health control strategies, and AI and data analytics play an important role in epidemiology, diagnostic, and clinical fronts. The focus of this book is data analytics for COVID-19, which includes an overview of COVID-19 in terms of epidemic/pandemic, data processing and knowledge extraction. Data sources, storage and platforms are discussed along with discussions on data models, their performance, different big data techniques, tools and technologies. This book also addresses the challenges in applying analytics to pandemic scenarios, case studies and control strategies. Aimed at Data Analysts, Epidemiologists and associated researchers, this book: discusses challenges of AI model for big data analytics in pandemic scenarios; explains how different big data analytics techniques can be implemented; provides a set of recommendations to minimize infection rate of COVID-19; summarizes various techniques of data processing and knowledge extraction; enables users to understand big data analytics techniques required for prediction purposes. |
| data analysis case study: Statistical Case Studies Roxy Peck, Larry D. Haugh, Arnold Goodman, 1998-01-01 This book contains 20 case studies that use actual data sets that have not been simplified for classroom use. |
| data analysis case study: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-24 Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder |
| data analysis case study: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| data analysis case study: Handbook of Research on Engineering Innovations and Technology Management in Organizations Gaur, Loveleen, Solanki, Arun, Jain, Vishal, Khazanchi, Deepak, 2020-04-17 As technology weaves itself more tightly into everyday life, socio-economic development has become intricately tied to these ever-evolving innovations. Technology management is now an integral element of sound business practices, and this revolution has opened up many opportunities for global communication. However, such swift change warrants greater research that can foresee and possibly prevent future complications within and between organizations. The Handbook of Research on Engineering Innovations and Technology Management in Organizations is a collection of innovative research that explores global concerns in the applications of technology to business and the explosive growth that resulted. Highlighting a wide range of topics such as cyber security, legal practice, and artificial intelligence, this book is ideally designed for engineers, manufacturers, technology managers, technology developers, IT specialists, productivity consultants, executives, lawyers, programmers, managers, policymakers, academicians, researchers, and students. |
| data analysis case study: Data Science For Dummies Lillian Pierson, 2021-08-20 Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today. |
| data analysis case study: Introduction to Statistical Thinking Benjamin Yakir, 2014-09-19 Introduction to Statistical ThinkingBy Benjamin Yakir |
| data analysis case study: Data Mining with R Luis Torgo, 2016-11-30 Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies, and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies, and they facilitate the do-it-yourself approach followed in the book. Designed for users of data analysis tools, as well as researchers and developers, the book should be useful for anyone interested in entering the world of R and data mining. About the Author Luís Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He teaches Data Mining in R in the NYU Stern School of Business’ MS in Business Analytics program. An active researcher in machine learning and data mining for more than 20 years, Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA. |
| data analysis case study: Data Science Projects with Python Stephen Klosterman, 2021-07-29 Gain hands-on experience of Python programming with industry-standard machine learning techniques using pandas, scikit-learn, and XGBoost Key FeaturesThink critically about data and use it to form and test a hypothesisChoose an appropriate machine learning model and train it on your dataCommunicate data-driven insights with confidence and clarityBook Description If data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable. In this book, you'll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you'll experience in real-world data science projects. You'll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest. Now in its second edition, this book will take you through the end-to-end process of exploring data and delivering machine learning models. Updated for 2021, this edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world. By the end of this data science book, you'll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data. What you will learnLoad, explore, and process data using the pandas Python packageUse Matplotlib to create compelling data visualizationsImplement predictive machine learning models with scikit-learnUse lasso and ridge regression to reduce model overfittingEvaluate random forest and logistic regression model performanceDeliver business insights by presenting clear, convincing conclusionsWho this book is for Data Science Projects with Python – Second Edition is for anyone who wants to get started with data science and machine learning. If you're keen to advance your career by using data analysis and predictive modeling to generate business insights, then this book is the perfect place to begin. To quickly grasp the concepts covered, it is recommended that you have basic experience of programming with Python or another similar language, and a general interest in statistics. |
| data analysis case study: Secondary Analysis of Electronic Health Records MIT Critical Data, 2016-09-09 This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients. |
| data analysis case study: Case Study Methodology in Higher Education Baron, Annette, McNeal, Kelly, 2019-06-28 In higher education, case studies can be utilized to have students put themselves into problems faced by a protagonist and, by doing so, address academic or career-related issues. Working through these issues provides students with an opportunity to gain applied perspective and experiences. Professors in higher education who choose this method of teaching require navigational tools to ensure that students achieve stated learning objectives. Case Study Methodology in Higher Education is an essential research publication that focuses on the history and theories relating to case study methodology including techniques for writing case studies and utilizing them in university settings to prepare students for real-life career-related scenarios. This publication features a wide range of topics such as educational leadership, case writing, and teacher education. It is essential for educators, career professionals, higher education faculty, researchers, and students. |
| data analysis case study: Case Studies in Neural Data Analysis Mark A. Kramer, Uri T. Eden, 2016-10-28 A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data. As neural data becomes increasingly complex, neuroscientists now require skills in computer programming, statistics, and data analysis. This book teaches practical neural data analysis techniques by presenting example datasets and developing techniques and tools for analyzing them. Each chapter begins with a specific example of neural data, which motivates mathematical and statistical analysis methods that are then applied to the data. This practical, hands-on approach is unique among data analysis textbooks and guides, and equips the reader with the tools necessary for real-world neural data analysis. The book begins with an introduction to MATLAB, the most common programming platform in neuroscience, which is used in the book. (Readers familiar with MATLAB can skip this chapter and might decide to focus on data type or method type.) The book goes on to cover neural field data and spike train data, spectral analysis, generalized linear models, coherence, and cross-frequency coupling. Each chapter offers a stand-alone case study that can be used separately as part of a targeted investigation. The book includes some mathematical discussion but does not focus on mathematical or statistical theory, emphasizing the practical instead. References are included for readers who want to explore the theoretical more deeply. The data and accompanying MATLAB code are freely available on the authors' website. The book can be used for upper-level undergraduate or graduate courses or as a professional reference. |
| data analysis case study: Case Study Research in Software Engineering Per Runeson, Martin Host, Austen Rainer, Bjorn Regnell, 2012-03-07 Based on their own experiences of in-depth case studies of software projects in international corporations, in this book the authors present detailed practical guidelines on the preparation, conduct, design and reporting of case studies of software engineering. This is the first software engineering specific book on the case study research method. |
| data analysis case study: Analysis of Neural Data Robert E. Kass, Uri T. Eden, Emery N. Brown, 2014-07-08 Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work. |
| data analysis case study: Using Software in Qualitative Research Ann Lewins, Christina Silver, 2007-05 Using Software in Qualitative Research is an essential introduction to the practice and principles of Computer Assisted Qualitative Data Analysis (CAQDAS), helping the reader choose the most appropriate package for their needs and to get the most out of the software once they are using it. This step-by-step book considers a wide range of tasks and processes, bringing them together to demystify qualitative software and encourage flexible and critical choices and uses of software in supporting analysis. The book can be read as a whole or by chapters, building on one another to provide a holistic sense of the analytic journey without advocating a particular sequential process. Accessible and comprehensive, Using Software in Qualitative Research provides a practical but analytically-grounded guide to thinking about and using software and will be an essential companion for any qualitative researcher. |
| data analysis case study: Conceptual Analyses of Curriculum Inquiry Methodologies Watson, Sandy White, Austin, Stacie, Bell, Jeremy, 2021-12-03 The field of curriculum inquiry has grown rapidly over the last four decades resulting in many new forms of curriculum inquiry to be used as tools to answer unique curriculum-related research questions. There are few texts available that include concise descriptions and elements of curriculum inquiry methodologies and directed at enabling researchers to wisely choose a form of curriculum inquiry most appropriate for their study. Conceptual Analyses of Curriculum Inquiry Methodologies presents chapters that are each devoted to a particular form of inquiry, with a conceptual analysis of the methodology, its purpose(s), its utilization, structure, and organization, all written by scholars with firsthand experience with the form of inquiry. These experts also take the liberty of citing examples of published studies that have utilized the methodology, share the types of relevant data collection instruments and forms of data produced, and also share research questions that can be answered via their form of inquiry. Covering topics such as quantitative methods of inquiry, glocalization, and educational criticism, this is an essential text for curriculum designers, doctoral students, doctoral researchers, university faculty, professors, researchers, and academicians. |
| data analysis case study: Encyclopedia of Case Study Research: L - Z Albert J. Mills, Gabrielle Durepos, Elden Wiebe, 2010 |
| data analysis case study: Case Study Strategies for Architects and Designers Marja Sarvimaki, 2017-06-27 Case Study Strategies for Architects and Designers explains methods in evidence-based design, also called practice-based research, to show you the value of research to your designs. Topics covered pertain to data collection and analysis techniques, including surveys, interviews, fieldwork, participatory design, occupancy evaluations, and memory sketching. Integrative data evaluation, theoretical sampling, triangulation, pattern matching logic, and analytical generalization are also discussed. Global research precedents, exercises, further reading, section summaries, sidebars, more than 30 black and white images and tables will help you conduct empirical inquiries in real-life contexts. |
| data analysis case study: Case Study Research and Applications Robert K. Yin, 2017-09-27 Winner of the 2019 McGuffey Longevity Award from the Textbook & Academic Authors Association (TAA) Recognized as one of the most cited methodology books in the social sciences, the Sixth Edition of Robert K. Yin′s bestselling text provides a complete portal to the world of case study research. With the integration of 11 applications in this edition, the book gives readers access to exemplary case studies drawn from a wide variety of academic and applied fields. Ultimately, Case Study Research and Applications will guide students in the successful use and application of the case study research method. |
| data analysis case study: Case Study Research John Gerring, 2016-12-24 Case Study Research: Principles and Practices provides a general understanding of the case study method as well as specific tools for its successful implementation. These tools are applicable in a variety of fields including anthropology, business and management, communications, economics, education, medicine, political science, psychology, social work, and sociology. Topics include: a survey of case study approaches; a methodologically tractable definition of 'case study'; strategies for case selection, including random sampling and other algorithmic approaches; quantitative and qualitative modes of case study analysis; and problems of internal and external validity. The second edition of this core textbook is designed to be accessible to readers who are new to the subject and is thoroughly revised and updated, incorporating recent research, numerous up-to-date studies and comprehensive lecture slides. |
| data analysis case study: Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance Rana, Dipti P., Mehta, Rupa G., 2021-06-04 Over the last two decades, researchers are looking at imbalanced data learning as a prominent research area. Many critical real-world application areas like finance, health, network, news, online advertisement, social network media, and weather have imbalanced data, which emphasizes the research necessity for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion detection, fake news detection, fraud advertisement detection, cyber bullying identification, disaster events prediction, and more. Machine learning algorithms are based on the heuristic of equally-distributed balanced data and provide the biased result towards the majority data class, which is not acceptable considering imbalanced data is omnipresent in real-life scenarios and is forcing us to learn from imbalanced data for foolproof application design. Imbalanced data is multifaceted and demands a new perception using the novelty at sampling approach of data preprocessing, an active learning approach, and a cost perceptive approach to resolve data imbalance. Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance offers new aspects for imbalanced data learning by providing the advancements of the traditional methods, with respect to big data, through case studies and research from experts in academia, engineering, and industry. The chapters provide theoretical frameworks and the latest empirical research findings that help to improve the understanding of the impact of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches. This book is ideal for data scientists, data analysts, engineers, practitioners, researchers, academicians, and students looking for more information on imbalanced data characteristics and solutions using varied approaches. |
| data analysis case study: Research Basics James V. Spickard, 2016-09-15 Research Basics: Design to Data Analysis in Six Steps offers a fresh and creative approach to the research process based on author James V. Spickard’s decades of teaching experience. Using an intuitive six-step model, readers learn how to craft a research question and then identify a logical process for answering it. Conversational writing and multi-disciplinary examples illuminate the model’s simplicity and power, effectively connecting the “hows” and “whys” behind social science research. Students using this book will learn how to turn their research questions into results. |
| data analysis case study: Using Case Study in Education Research Lorna Hamilton, Connie Corbett-Whittier, 2012-11-19 This book provides an accessible introduction to using case studies. It makes sense of literature in this area, and shows how to generate collaborations and communicate findings. The authors bring together the practical and the theoretical, enabling readers to build expertise on the principles and practice of case study research, as well as engaging with possible theoretical frameworks. They also highlight the place of case study as a key component of educational research. With the help of this book, M-Level students, teacher educators and practitioner researchers will gain the confidence and skills needed to design and conduct a high quality case study. Dr Lorna Hamilton is a Senior Lecturer in Education Research at the University of Edinburgh. Dr Connie Corbett-Whittier is an Associate Professor of English and Humanities at Friends University, Topeka, Kansas. ′Drawing on a wide range of their own and others′ experiences, the authors offer a comprehensive and convincing account of the value of case study in educational research. What comes across - quite passionately - is the way in which a case study approach can bring to life some of the complexities, challenges and contradictions inherent in educational settings. The book is written in a clear and lively manner and should be an invaluable resource for those teachers and students who are incorporating a case study dimension into their research work.′ -Ian Menter, Professor of Teacher Education, University of Oxford ′This book is comprehensive in its coverage, yet detailed in its exposition of case study research. It is a highly interactive text with a critical edge and is a useful tool for teaching. It is of particular relevance to practitioner researchers, providing accessible guidance for reflective practice. It covers key matters such as: purposes, ethics, data analysis, technology, dissemination and communities for research. And it is a good read!′ - Professor Anne Campbell, formerly of Leeds Metropolitan University ′This excellent book is a principled and theoretically informed guide to case study research design and methods for the collection, analysis and presentatin of evidence′ - Professor Andrew Pollard, Institute of Education, University of London Research Methods in Education series: Each book in this series maps the territory of a key research approach or topic in order to help readers progress from beginner to advanced researcher. Each book aims to provide a definitive, market-leading overview and to present a blend of theory and practice with a critical edge. All titles in the series are written for Master′s-level students anywhere and are intended to be useful to the many diverse constituencies interested in research on education and related areas. Other books in the series: - Qualitative Research in Education, Atkins and Wallace - Action Research in Education, McAteer - Ethnography in Education, Mills and Morton |
| data analysis case study: Introduction to Educational Research W. Newton Suter, 2012 W. Newton Suter argues that what is important in a changing education landscape is the ability to think clearly about research methods, reason through complex problems and evaluate published research. He explains how to evaluate data and establish its relevance. |
| data analysis case study: Development Research in Practice Kristoffer Bjärkefur, Luíza Cardoso de Andrade, Benjamin Daniels, Maria Ruth Jones, 2021-07-16 Development Research in Practice leads the reader through a complete empirical research project, providing links to continuously updated resources on the DIME Wiki as well as illustrative examples from the Demand for Safe Spaces study. The handbook is intended to train users of development data how to handle data effectively, efficiently, and ethically. “In the DIME Analytics Data Handbook, the DIME team has produced an extraordinary public good: a detailed, comprehensive, yet easy-to-read manual for how to manage a data-oriented research project from beginning to end. It offers everything from big-picture guidance on the determinants of high-quality empirical research, to specific practical guidance on how to implement specific workflows—and includes computer code! I think it will prove durably useful to a broad range of researchers in international development and beyond, and I learned new practices that I plan on adopting in my own research group.†? —Marshall Burke, Associate Professor, Department of Earth System Science, and Deputy Director, Center on Food Security and the Environment, Stanford University “Data are the essential ingredient in any research or evaluation project, yet there has been too little attention to standardized practices to ensure high-quality data collection, handling, documentation, and exchange. Development Research in Practice: The DIME Analytics Data Handbook seeks to fill that gap with practical guidance and tools, grounded in ethics and efficiency, for data management at every stage in a research project. This excellent resource sets a new standard for the field and is an essential reference for all empirical researchers.†? —Ruth E. Levine, PhD, CEO, IDinsight “Development Research in Practice: The DIME Analytics Data Handbook is an important resource and a must-read for all development economists, empirical social scientists, and public policy analysts. Based on decades of pioneering work at the World Bank on data collection, measurement, and analysis, the handbook provides valuable tools to allow research teams to more efficiently and transparently manage their work flows—yielding more credible analytical conclusions as a result.†? —Edward Miguel, Oxfam Professor in Environmental and Resource Economics and Faculty Director of the Center for Effective Global Action, University of California, Berkeley “The DIME Analytics Data Handbook is a must-read for any data-driven researcher looking to create credible research outcomes and policy advice. By meticulously describing detailed steps, from project planning via ethical and responsible code and data practices to the publication of research papers and associated replication packages, the DIME handbook makes the complexities of transparent and credible research easier.†? —Lars Vilhuber, Data Editor, American Economic Association, and Executive Director, Labor Dynamics Institute, Cornell University |
| data analysis case study: Case Study Method Roger Gomm, Martyn Hammersley, Peter Foster, 2000-10-17 This is the most comprehensive guide to the current uses and importance of case study methods in social research. The editors bring together key contributions from the field which reflect different interpretations of the purpose and capacity of case study research. The address issues such as: the problem of generalizing from study of a small number of cases; and the role of case study in developing and testing theories. The editors offer in-depth assessments of the main arguments. An annotated bibliography of the literature dealing with case study research makes this an exhaustive and indispensable guide. |
| data analysis case study: Public Policy Analytics Ken Steif, 2021-08-18 Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and ‘Planning’ are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government. |
| data analysis case study: The Practice of Reproducible Research Justin Kitzes, Daniel Turek, Fatma Deniz, 2018 The Practice of Reproducible Research presents concrete examples of how researchers in the data-intensive sciences are working to improve the reproducibility of their research projects. In each of the thirty-one case studies in this volume, the author or team describes the workflow that they used to complete a real-world research project. Authors highlight how they utilized particular tools, ideas, and practices to support reproducibility, emphasizing the very practical how, rather than the why or what, of conducting reproducible research. Part 1 provides an accessible introduction to reproducible research, a basic reproducible research project template, and a synthesis of lessons learned from across the thirty-one case studies. Parts 2 and 3 focus on the case studies themselves. The Practice of Reproducible Research is an invaluable resource for students and researchers who wish to better understand the practice of data-intensive sciences and learn how to make their own research more reproducible. |
| data analysis case study: Qualitative Data Analysis with NVivo Patricia Bazeley, 2007-04-12 `In plain language but with very thorough detail, this book guides the researcher who really wants to use the NVivo software (and use it now) into their project. The way is lit with real-project examples, adorned with tricks and tips, but it’s a clear path to a project' - Lyn Richards, Founder and Non-Executive Director, QSR International Doing Qualitative Data Analysis with NVivo is essential reading for anyone thinking of using their computer to help analyze qualitative data. With 15 years experience in computer-assisted analysis of qualitative and mixed-mode data, Patricia Bazeley is one of the leaders in the use and teaching of NVivo software. Through this very practical book, readers are guided on how best to make use of the powerful and flexible tools offered by the latest version of NVivo as they work through each stage of their research projects. Explanations draw on examples from her own and others' projects, and are supported by the methodological literature. Researchers have different requirements and come to their data from different perspectives. This book shows how NVivo software can accommodate and assist analysis across those different perspectives and methodological approaches. It is required reading for both students and experienced researchers alike. |
| data analysis case study: Embedded Case Study Methods Roland W. Scholz, Olaf Tietje, 2002 In an embedded case study, the starting and end point is the comprehension of the case as a whole in its real-world context. This book bridges the gap between quantitative and qualitative approaches to complex problems when using this methodology. |
| data analysis case study: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| data analysis case study: Text Mining and Analysis Dr. Goutam Chakraborty, Murali Pagolu, Satish Garla, 2014-11-22 Big data: It's unstructured, it's coming at you fast, and there's lots of it. In fact, the majority of big data is text-oriented, thanks to the proliferation of online sources such as blogs, emails, and social media. However, having big data means little if you can't leverage it with analytics. Now you can explore the large volumes of unstructured text data that your organization has collected with Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS. This hands-on guide to text analytics using SAS provides detailed, step-by-step instructions and explanations on how to mine your text data for valuable insight. Through its comprehensive approach, you'll learn not just how to analyze your data, but how to collect, cleanse, organize, categorize, explore, and interpret it as well. Text Mining and Analysis also features an extensive set of case studies, so you can see examples of how the applications work with real-world data from a variety of industries. Text analytics enables you to gain insights about your customers' behaviors and sentiments. Leverage your organization's text data, and use those insights for making better business decisions with Text Mining and Analysis. This book is part of the SAS Press program. |
| data analysis case study: R and Data Mining Yanchang Zhao, 2012-12-31 R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. - Presents an introduction into using R for data mining applications, covering most popular data mining techniques - Provides code examples and data so that readers can easily learn the techniques - Features case studies in real-world applications to help readers apply the techniques in their work |
Data Analysis Case Studies - Data Action Lab
In this report, we provide examples of data analysis and quantitative methods applied to “real-life” problems. We emphasize qualitative aspects of the projects as well as significant results and …
Case Study Design Essentials: Definition, Research Questions, …
Definition of the Case Study “An empirical inquiry that investigates a contemporary phenomenon (e.g., a “case”) within its real-life context; when the boundaries between phenomenon and …
Open Case Studies: Statistics and Data Science Education …
develop a case study – namely, to select a motivating question and to create an illustrative data analysis – and the domain expertise needed. As a result, case studies based on realistic …
Accountability Modules Data Analysis: Analyzing Data - Case …
Case studies are of the following general types: C Critical instance case studies gather data to examine a single instance of unique interest and/or to perform a limited test on an assertion …
Data Analytics Case Study
Case Study Industry: Manufacturing Deliverables: • KPI, Dashboards • Reports • Insights Technology: Power BI, SQL Server 2016, Azure, Amazon Cloud, SQL, CSV Files, Excel Files …
A Case Study Analysis of the Equifax Data Breach 1 A Case …
This case study analysis will explore the facts and circumstances surrounding this damaging cyberattack, and critically analyze the factors concerning the case to draw conclusions about …
An Intelligent Approach for Data Analysis and Decision …
In this research, we apply multiple machine learning (ML) models on large dataset present in the e-commerce area by studying several practical cases on online markets. The world has …
Methods of Analysis Case Analysis - University of British …
Case analysis is dependent on reality-record relationships and characterized by representing or reworking the case through what is commonly called a case history or case narrative. “The …
Analyzing Case Study Evidence - SAGE Publications Inc
Tip: How do I start analyzing my case study data? You might start with questions (e.g., the questions in your case study protocol) rather than with the data. Start with a small question …
Using Data Analytics to Derive Business Intelligence: A Case …
Adopting the six data analysis steps, we examined data from a fictitious bike-share company (CYCLISTIC BIKE SHARE) over a period of one year; October 2020 to September 2021.
DESIGNING CASE STUDIES - Simon Fraser University
Plan a systematic approach to case study design. Recognize the strengths and limitations of case studies as a research method. Compose a case study report that is appropriately structured …
A (VERY) BRIEF REFRESHER ON THE CASE STUDY METHOD
As briefly introduced in this chapter, case study research involves systematic data collection and analysis procedures, and case study findings can be generalized to other situations through …
Data Analytics for Smart Manufacturing: A Case Study
To summarize, the main contributions in this paper are as follow: (1) Proposing a data pipeline to handle ingestion, processing and analysis; (2) Provid- ing an in-depth exploratory analysis of …
Data Analysis Case Studies - Data Action Lab
In this report, we provide examples of data analysis and quantitative methods applied to “real-life” problems. We emphasize qualitative aspects of the projects as well as significant results and …
Qualitative Case Study Guidelines - DTIC
Qualitative Case Study Guidelines Saša Baškarada Joint and Operations Analysis Division Defence Science and Technology Organisation DSTO-GD-0773 ABSTRACT This report …
A Case Study on Bank Loan by using MS Excel - IJRPR
i.a. what is data analysis? Data analysis is a process that involves creating dataset, studying it, cleaning it and by removing null values, outliers and converting it to generate a useful result.
The Practice of Qualitative Data Analysis - ResearchGate
This book provides ten case studies with concise real-world examples illustrating how Edited by MAXQDA is used in practice. In each research example, the authors present their way
A CASE STUDY ON BIG DATA ANALYTICS IN MOBILE …
In this paper we review the various methods of analyzing data generated by mobile cellular networks. We aim to introduce the general background of data generated by mobile cellular …
Four Steps to Analyse Data from a Case Study Method
Four steps are proposed to assist the novice researcher analyse their data that has been collected using a case study method. The first step proposes the creation of a data repository …
Data Analysis Case Studies - Data Action Lab
In this report, we provide examples of data analysis and quantitative methods applied to “real-life” problems. We emphasize qualitative aspects of the projects as well as significant results and …
Case Study Design Essentials: Definition, Research Questions, …
Definition of the Case Study “An empirical inquiry that investigates a contemporary phenomenon (e.g., a “case”) within its real-life context; when the boundaries between phenomenon and …
Open Case Studies: Statistics and Data Science Education …
develop a case study – namely, to select a motivating question and to create an illustrative data analysis – and the domain expertise needed. As a result, case studies based on realistic …
Conducting Case Study Research - ASCLS
1. Describe when the case study approach is the most appropriate qualitative research method. 2. Outline the components of a case study research method. 3. Discuss data coding and analysis …
Accountability Modules Data Analysis: Analyzing Data - Case …
Case studies are of the following general types: C Critical instance case studies gather data to examine a single instance of unique interest and/or to perform a limited test on an assertion …
Data Analytics Case Study
Case Study Industry: Manufacturing Deliverables: • KPI, Dashboards • Reports • Insights Technology: Power BI, SQL Server 2016, Azure, Amazon Cloud, SQL, CSV Files, Excel Files …
A Case Study Analysis of the Equifax Data Breach 1 A Case …
This case study analysis will explore the facts and circumstances surrounding this damaging cyberattack, and critically analyze the factors concerning the case to draw conclusions about …
An Intelligent Approach for Data Analysis and Decision …
In this research, we apply multiple machine learning (ML) models on large dataset present in the e-commerce area by studying several practical cases on online markets. The world has …
Methods of Analysis Case Analysis - University of British …
Case analysis is dependent on reality-record relationships and characterized by representing or reworking the case through what is commonly called a case history or case narrative. “The …
Analyzing Case Study Evidence - SAGE Publications Inc
Tip: How do I start analyzing my case study data? You might start with questions (e.g., the questions in your case study protocol) rather than with the data. Start with a small question …
Using Data Analytics to Derive Business Intelligence: A Case …
Adopting the six data analysis steps, we examined data from a fictitious bike-share company (CYCLISTIC BIKE SHARE) over a period of one year; October 2020 to September 2021.
DESIGNING CASE STUDIES - Simon Fraser University
Plan a systematic approach to case study design. Recognize the strengths and limitations of case studies as a research method. Compose a case study report that is appropriately structured …
A (VERY) BRIEF REFRESHER ON THE CASE STUDY METHOD
As briefly introduced in this chapter, case study research involves systematic data collection and analysis procedures, and case study findings can be generalized to other situations through …
Data Analytics for Smart Manufacturing: A Case Study
To summarize, the main contributions in this paper are as follow: (1) Proposing a data pipeline to handle ingestion, processing and analysis; (2) Provid- ing an in-depth exploratory analysis of …
Data Analysis Case Studies - Data Action Lab
In this report, we provide examples of data analysis and quantitative methods applied to “real-life” problems. We emphasize qualitative aspects of the projects as well as significant results and …
Qualitative Case Study Guidelines - DTIC
Qualitative Case Study Guidelines Saša Baškarada Joint and Operations Analysis Division Defence Science and Technology Organisation DSTO-GD-0773 ABSTRACT This report …
A Case Study on Bank Loan by using MS Excel - IJRPR
i.a. what is data analysis? Data analysis is a process that involves creating dataset, studying it, cleaning it and by removing null values, outliers and converting it to generate a useful result.
The Practice of Qualitative Data Analysis - ResearchGate
This book provides ten case studies with concise real-world examples illustrating how Edited by MAXQDA is used in practice. In each research example, the authors present their way
A CASE STUDY ON BIG DATA ANALYTICS IN MOBILE …
In this paper we review the various methods of analyzing data generated by mobile cellular networks. We aim to introduce the general background of data generated by mobile cellular …
Four Steps to Analyse Data from a Case Study Method
Four steps are proposed to assist the novice researcher analyse their data that has been collected using a case study method. The first step proposes the creation of a data repository …
