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| data analysis case studies: 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 studies: 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 studies: 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 studies: 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 studies: Practical Data Analysis Peter G. Bryant, Marlene A. Smith, 1998-11 Practical Data Analysis: Case Studies in Business Statistics is a collection of 75 class tested case studies for use in introductory business statistics and general statistics. All cases are drawn from real situations in a broad range of business, economic, and social science settings and include small and large data sets for analysis by students. The philosophy behind the package is to let the cases and data drive or supplement the course. Doing so provides three important opportunities for students and instructors: useful computing experience, hands-on activity, which is more motivating than the traditional course format, and a sense of realism about the use of statistics. |
| data analysis case studies: 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 studies: 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 studies: 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 studies: 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 studies: 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: Applying Data Science Gerhard Svolba, 2017-03-29 See how data science can answer the questions your business faces! Applying Data Science: Business Case Studies Using SAS, by Gerhard Svolba, shows you the benefits of analytics, how to gain more insight into your data, and how to make better decisions. In eight entertaining and real-world case studies, Svolba combines data science and advanced analytics with business questions, illustrating them with data and SAS code. The case studies range from a variety of fields, including performing headcount survival analysis for employee retention, forecasting the demand for new projects, using Monte Carlo simulation to understand outcome distribution, among other topics. The data science methods covered include Kaplan-Meier estimates, Cox Proportional Hazard Regression, ARIMA models, Poisson regression, imputation of missing values, variable clustering, and much more! Written for business analysts, statisticians, data miners, data scientists, and SAS programmers, Applying Data Science bridges the gap between high-level, business-focused books that skimp on the details and technical books that only show SAS code with no business context. |
| data analysis case studies: Case Studies in Applied Bayesian Data Science Kerrie L. Mengersen, Pierre Pudlo, Christian P. Robert, 2020-05-28 Presenting a range of substantive applied problems within Bayesian Statistics along with their Bayesian solutions, this book arises from a research program at CIRM in France in the second semester of 2018, which supported Kerrie Mengersen as a visiting Jean-Morlet Chair and Pierre Pudlo as the local Research Professor. The field of Bayesian statistics has exploded over the past thirty years and is now an established field of research in mathematical statistics and computer science, a key component of data science, and an underpinning methodology in many domains of science, business and social science. Moreover, while remaining naturally entwined, the three arms of Bayesian statistics, namely modelling, computation and inference, have grown into independent research fields. While the research arms of Bayesian statistics continue to grow in many directions, they are harnessed when attention turns to solving substantive applied problems. Each such problem set has its own challenges and hence draws from the suite of research a bespoke solution. The book will be useful for both theoretical and applied statisticians, as well as practitioners, to inspect these solutions in the context of the problems, in order to draw further understanding, awareness and inspiration. |
| data analysis case studies: 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 studies: Data Science in R Deborah Nolan, Duncan Temple Lang, 2015-04-21 Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and ComputationData Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts |
| data analysis case studies: 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 studies: 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 studies: Data Management and Analysis Reda Alhajj, Mohammad Moshirpour, Behrouz Far, 2019-12-20 Data management and analysis is one of the fastest growing and most challenging areas of research and development in both academia and industry. Numerous types of applications and services have been studied and re-examined in this field resulting in this edited volume which includes chapters on effective approaches for dealing with the inherent complexity within data management and analysis. This edited volume contains practical case studies, and will appeal to students, researchers and professionals working in data management and analysis in the business, education, healthcare, and bioinformatics areas. |
| data analysis case studies: Applied Qualitative Research Design Margaret R. Roller, Paul J. Lavrakas, 2015-02-23 This unique text provides a comprehensive framework for creating, managing, and interpreting qualitative research studies that yield valid and useful information. Examples of studies from a wide range of disciplines illustrate the strengths, limitations, and applications of the primary qualitative methods: in-depth interviews, focus group discussions, ethnography, content analysis, and case study and narrative research. Following a consistent format, chapters show students and researchers how to implement each method within a paradigm-neutral and flexible Total Quality Framework (TQF) comprising four interrelated components: Credibility, Analyzability, Transparency, and Usefulness. Unlike other texts that relegate quality issues to one or two chapters, detailed discussions of such crucial topics as construct validity, interresearcher reliability, researcher bias, and verification strategies are featured throughout. The book also addresses applications of the TQF to the writing, review, and evaluation of qualitative research proposals and manuscripts. Pedagogical Features *Summary tables that highlight important content, such as the application of a method to vulnerable or hard-to-reach populations. *Case studies that illustrate TQF standards in practice for each method. *Guidelines for effective documentation (via thick descriptions) of each type of study. *End-of-chapter discussion topics, exercises, and suggested further reading and Web resources. *Chapters open with a preview and close with a bulleted summary of key ideas. *Extensive glossary. 2021 Winner--American Association for Public Opinion Research (AAPOR) Book Award |
| data analysis case studies: Data Science and Complex Networks Guido Caldarelli, Alessandro Chessa, 2016-11-10 This book provides a comprehensive yet short description of the basic concepts of Complex Network theory. In contrast to other books the authors present these concepts through real case studies. The application topics span from Foodwebs, to the Internet, the World Wide Web and the Social Networks, passing through the International Trade Web and Financial time series. The final part is devoted to definition and implementation of the most important network models. The text provides information on the structure of the data and on the quality of available datasets. Furthermore it provides a series of codes to allow immediate implementation of what is theoretically described in the book. Readers already used to the concepts introduced in this book can learn the art of coding in Python by using the online material. To this purpose the authors have set up a dedicated web site where readers can download and test the codes. The whole project is aimed as a learning tool for scientists and practitioners, enabling them to begin working instantly in the field of Complex Networks. |
| data analysis case studies: 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 studies: Thinking with Data Max Shron, 2014-01-20 Many analysts are too concerned with tools and techniques for cleansing, modeling, and visualizing datasets and not concerned enough with asking the right questions. In this practical guide, data strategy consultant Max Shron shows you how to put the why before the how, through an often-overlooked set of analytical skills. Thinking with Data helps you learn techniques for turning data into knowledge you can use. You’ll learn a framework for defining your project, including the data you want to collect, and how you intend to approach, organize, and analyze the results. You’ll also learn patterns of reasoning that will help you unveil the real problem that needs to be solved. Learn a framework for scoping data projects Understand how to pin down the details of an idea, receive feedback, and begin prototyping Use the tools of arguments to ask good questions, build projects in stages, and communicate results Explore data-specific patterns of reasoning and learn how to build more useful arguments Delve into causal reasoning and learn how it permeates data work Put everything together, using extended examples to see the method of full problem thinking in action |
| data analysis case studies: Qualitative Research Through Case Studies Max Travers, 2001-07-23 Qualitative Research Through Case Studies provides an accessible introduction to a wide range of approaches that deal with the theoretical analysis of qualitative data. |
| data analysis case studies: 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 studies: Mastering pandas Ashish Kumar, 2019-10-25 Perform advanced data manipulation tasks using pandas and become an expert data analyst. Key FeaturesManipulate and analyze your data expertly using the power of pandasWork with missing data and time series data and become a true pandas expertIncludes expert tips and techniques on making your data analysis tasks easierBook Description pandas is a popular Python library used by data scientists and analysts worldwide to manipulate and analyze their data. This book presents useful data manipulation techniques in pandas to perform complex data analysis in various domains. An update to our highly successful previous edition with new features, examples, updated code, and more, this book is an in-depth guide to get the most out of pandas for data analysis. Designed for both intermediate users as well as seasoned practitioners, you will learn advanced data manipulation techniques, such as multi-indexing, modifying data structures, and sampling your data, which allow for powerful analysis and help you gain accurate insights from it. With the help of this book, you will apply pandas to different domains, such as Bayesian statistics, predictive analytics, and time series analysis using an example-based approach. And not just that; you will also learn how to prepare powerful, interactive business reports in pandas using the Jupyter notebook. By the end of this book, you will learn how to perform efficient data analysis using pandas on complex data, and become an expert data analyst or data scientist in the process. What you will learnSpeed up your data analysis by importing data into pandasKeep relevant data points by selecting subsets of your dataCreate a high-quality dataset by cleaning data and fixing missing valuesCompute actionable analytics with grouping and aggregation in pandasMaster time series data analysis in pandasMake powerful reports in pandas using Jupyter notebooksWho this book is for This book is for data scientists, analysts and Python developers who wish to explore advanced data analysis and scientific computing techniques using pandas. Some fundamental understanding of Python programming and familiarity with the basic data analysis concepts is all you need to get started with this book. |
| data analysis case studies: Python Machine Learning Case Studies Danish Haroon, 2017-10-27 Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on minimal time and resources. Python Machine Learning Case Studies takes you through the steps to improve business processes and determine the pivotal points that frame strategies. You’ll see machine learning techniques that you can use to support your products and services. Moreover you’ll learn the pros and cons of each of the machine learning concepts to help you decide which one best suits your needs. By taking a step-by-step approach to coding in Python you’ll be able to understand the rationale behind model selection and decisions within the machine learning process. The book is equipped with practical examples along with code snippets to ensure that you understand the data science approach to solving real-world problems. What You Will Learn Gain insights into machine learning concepts Work on real-world applications of machine learning Learn concepts of model selection and optimization Get a hands-on overview of Python from a machine learning point of view Who This Book Is For Data scientists, data analysts, artificial intelligence engineers, big data enthusiasts, computer scientists, computer sciences students, and capital market analysts. |
| data analysis case studies: 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 studies: Data Management and Analysis Using JMP Jane E Oppenlander, Patricia Schaffer, 2017-10-17 A holistic, step-by-step approach to analyzing health care data! Written for both beginner and intermediate JMP users working in or studying health care, Data Management and Analysis Using JMP: Health Care Case Studies bridges the gap between taking traditional statistics courses and successfully applying statistical analysis in the workplace. Authors Jane Oppenlander and Patricia Schaffer begin by illustrating techniques to prepare data for analysis, followed by presenting effective methods to summarize, visualize, and analyze data. The statistical analysis methods covered in the book are the foundational techniques commonly applied to meet regulatory, operational, budgeting, and research needs in the health care field. This example-driven book shows practitioners how to solve real-world problems by using an approach that includes problem definition, data management, selecting the appropriate analysis methods, step-by-step JMP instructions, and interpreting statistical results in context. Practical strategies for selecting appropriate statistical methods, remediating data anomalies, and interpreting statistical results in the domain context are emphasized. The cases presented in Data Management and Analysis Using JMP use multiple statistical methods. A progression of methods--from univariate to multivariate--is employed, illustrating a logical approach to problem-solving. Much of the data used in these cases is open source and drawn from a variety of health care settings. The book offers a welcome guide to working professionals as well as students studying statistics in health care-related fields. |
| data analysis case studies: 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 studies: 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 studies: Data Monitoring in Clinical Trials David L. DeMets, Curt D. Furberg, Lawrence M. Friedman, 2006-06-22 From the authors of Fundamentals of Clinical Trials which has sold over 15,000 copies world wide since its publication in 1998. No competition yet as the text does not focus on how to do clinical trials but on very specific situations that can be encountered during the process. |
| data analysis case studies: NMR Case Studies Jeffrey H. Simpson, 2017-09-19 NMR Case Studies: Data Analysis of Complicated Molecules provides a detailed discussion of the full logical flow associated with assigning the NMR spectra of complex molecules, also helping readers further develop their NMR spectral assignment skills. The robust case studies present the logic of each assignment, from beginning to end, fully exploring the available range of potential solutions. Readers will gain a better appreciation of various approaches and develop an intuitive sense for when this particular concept should be implemented, thus enhancing their skillsets and providing a host of methodologies potentially amenable to yielding correct assignments. Authored by a scientist with more than 20 years of experience in research and instruction, this book is the ideal reference for anyone in search of application-based content. The book addresses complicated molecules, including corticosteroids, biomolecules, polypeptides, and secondary metabolites. - Features the nuclear magnetic resonance (NMR) spectra of a number of large and interesting molecules, ranging from corticosteroids, to secondary metabolites and large synthetically prepared molecules - Uses case studies to pair the spectral signals from the various sites of each molecule to their molecular counterparts in a process called assignment - Includes complex NMR problems, aiding readers in the development of NMR spectral assignment skills - Features input from a leading scientist with over 20 years of research and instruction experience in the field |
| data analysis case studies: 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 studies: Bioconductor Case Studies Florian Hahne, Wolfgang Huber, Robert Gentleman, Seth Falcon, 2010-06-09 Bioconductor software has become a standard tool for the analysis and comprehension of data from high-throughput genomics experiments. Its application spans a broad field of technologies used in contemporary molecular biology. In this volume, the authors present a collection of cases to apply Bioconductor tools in the analysis of microarray gene expression data. Topics covered include: (1) import and preprocessing of data from various sources; (2) statistical modeling of differential gene expression; (3) biological metadata; (4) application of graphs and graph rendering; (5) machine learning for clustering and classification problems; (6) gene set enrichment analysis. Each chapter of this book describes an analysis of real data using hands-on example driven approaches. Short exercises help in the learning process and invite more advanced considerations of key topics. The book is a dynamic document. All the code shown can be executed on a local computer, and readers are able to reproduce every computation, figure, and table. |
| data analysis case studies: 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 studies: The Case for Case Studies Jennifer Widner, Michael Woolcock, Daniel Ortega Nieto, 2022-05-26 This book seeks to narrow two gaps: first, between the widespread use of case studies and their frequently 'loose' methodological moorings; and second, between the scholarly community advancing methodological frontiers in case study research and the users of case studies in development policy and practice. It draws on the contributors' collective experience at this nexus, but the underlying issues are more broadly relevant to case study researchers and practitioners in all fields. How does one prepare a rigorous case study? When can causal inferences reasonably be drawn from a single case? When and how can policy-makers reasonably presume that a demonstrably successful intervention in one context might generate similarly impressive outcomes elsewhere, or if massively 'scaled up'? No matter their different starting points – disciplinary base, epistemological orientation, sectoral specialization, or practical concerns – readers will find issues of significance for their own field, and others across the social sciences. This title is also available Open Access. |
| data analysis case studies: Case Studies in Bayesian Statistical Modelling and Analysis Clair L. Alston, Kerrie L. Mengersen, Anthony N. Pettitt, 2012-12-17 Provides an accessible foundation to Bayesian analysis using real world models This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the corresponding model, the computational method, results and inferences as well as the issues that arise in the implementation of these approaches. Case Studies in Bayesian Statistical Modelling and Analysis: Illustrates how to do Bayesian analysis in a clear and concise manner using real-world problems. Each chapter focuses on a real-world problem and describes the way in which the problem may be analysed using Bayesian methods. Features approaches that can be used in a wide area of application, such as, health, the environment, genetics, information science, medicine, biology, industry and remote sensing. Case Studies in Bayesian Statistical Modelling and Analysis is aimed at statisticians, researchers and practitioners who have some expertise in statistical modelling and analysis, and some understanding of the basics of Bayesian statistics, but little experience in its application. Graduate students of statistics and biostatistics will also find this book beneficial. |
| data analysis case studies: Land Seismic Case Studies for Near-Surface Modeling and Subsurface Imaging Öz Yilmaz, 2021-06-30 Written for practicing geophysicists, “Land Seismic Case Studies for Near-Surface Modeling and Subsurface Imaging” is a comprehensive guide to understanding and interpreting seismic data. The culmination of land seismic data acquisition and processing projects conducted by the author over the last two decades, this book contains more than nearly 800 figures from worldwide case studies—conducted in both 2D and 3D. Beginning with Chapter 1 on seismic characterization of the near-surface, Chapter 2 presents near-surface modeling by traveltime and full-wave inversion, Chapter 3 presents near-surface modeling by imaging, and then Chapter 4 includes detailed case studies for near-surface modeling. Chapter 5 reviews single- and multichannel signal processing of land seismic data with the key objective of removing surface waves and guided waves that are characterized as coherent linear noise. Uncommon seismic data acquisition methods, including large-offset acquisition in thrust belts to capture the large-amplitude supercritical reflections, swath-line acquisition, and joint PP and SH- SH seismic imaging are highlighted in Chapter 6, and Chapter 7 presents image-based rms velocity estimation and discusses the problem of velocity uncertainty. The final two chapters focus exclusively on case studies: 2D in Chapter 8 and 3D in Chapter 9. An outstanding teaching tool, this book includes analysis workflows containing processing steps designed to solve specific problems. Essential for anyone involved in acquisition, processing, and inversion of seismic data, this volume will become the definitive reference for understanding how the variables in seismic acquisition are directly reflected in the data. |
| data analysis case studies: 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 studies: Designing Case Studies J. Blatter, M. Haverland, 2012-05-30 The authors explore three ways of conducting causal analysis in case studies. They draw on established practices as well as on recent innovations in case study methodology and integrate these insights into coherent approaches. They highlight the core features of each approach and provide advice on each step of the research process. |
| data analysis case studies: 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 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 …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
