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Here's an essential revision tool for the MRCP exam. The second book in this three-book series, written by renowned experts, offers fully detailed answers and helps to prepare students in data interpretation of laboratory or other results (such as ECGs) by implementing practical case histories.
Salient features of this book: a unique book for mastering the skills of interpreting various types of data in clinical medicine; useful for postgraduate students to practice and assess their competency in interpreting clinical scenarios; contains 333 case histories and clinical data, 26 spirometry tracings, 14 family trees, 16 data on cardiac catheterization and 171 clinical pictures; relevant clinical information regarding a disease condition have been provided in 'answers' section to help students get a complete idea about the disease without going through voluminous textbooks; helpful for teachers and busy practitioners to quickly refresh their memory; more than enough information for undergraduates and interns.
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
A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data.
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.
Focusing on the interpretation of data commonly available to anesthesiologists, this book presents a data point, followed by discussion in a question and answer format. Covering EKG's, X-Rays, MRI's, graphs, paper recordings, blood gas results, laboratory results, patient case histories and more, Data Interpretation in Anesthesia provides an enhanced and stimulating learning format for residents in training and practitioners alike.
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.
The MRCP Part 2 Examination is increasingly orientated around evidence-based medicine, up-to-date patient management and current guidelines. This is a move away from the esoteric questions seen in the past and it makes a more useful exam. This book combines the three sections of the exam; case histories, data interpretations and photographic material, to give candidates all round practice.
Environmental Geochemistry: Site Characterization, Data Analysis and Case Histories, Second Edition, reviews the role of geochemistry in the environment and details state-of-the-art applications of these principles in the field, specifically in pollution and remediation situations. Chapters cover both philosophy and procedures, as well as applications, in an array of issues in environmental geochemistry including health problems related to environment pollution, waste disposal and data base management. This updated edition also includes illustrations of specific case histories of site characterization and remediation of brownfield sites. - Covers numerous global case studies allowing readers to see principles in action - Explores the environmental impacts on soils, water and air in terms of both inorganic and organic geochemistry - Written by a well-respected author team, with over 100 years of experience combined - Includes updated content on: urban geochemical mapping, chemical speciation, characterizing a brownsfield site and the relationship between heavy metal distributions and cancer mortality
First multi-year cumulation covers six years: 1965-70.