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Essentials of Nursing Practice introduces the core topics and essential information that nursing students, in all four fields, will need to master during the first year of a nursing degree. It expertly brings together insight from over fifty experienced lecturers, nurses and healthcare professionals, along with contributions from student nurses, to deliver the most complete guide to successfully becoming a registered nurse. Key features: A clear, full-colour, effective learning design aimed to help students understand the core theory, skills and knowledge, and how this can be applied in practice through holistic, person-centred nursing. Covers professional issues such as ethics, law, accountability, core academic skills like writing and completing assignments, and fundamental clinical skills such as pain management and medicines administration. Includes interactive activities such as critical thinking, reflection and ‘what’s the evidence’ boxes. Real-life ‘voices’ and experiences from patients, students and practitioners are integrated throughout. Addresses the transition to the new NMC Standards of Proficiency with a new tool developed for educators mapping the content of the book to both the existing and new standards. Readers get free 24/7 access to videos, case studies, journal articles, quizzes and multiple choice questions at the click of a button, by downloading the interactive eBook version of the text. (Redemption code and instructions inside the book)
The Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data. Building on over thirty years’ experience in teaching and practising data science, the author encourages a programming-by-example approach to ensure students and practitioners attune to the practise of data science while building their data skills. Proven frameworks are provided as reusable templates. Real world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets. The book begins by introducing data science. It then reviews R’s capabilities for analysing data by writing computer programs. These programs are developed and explained step by step. From analysing and visualising data, the framework moves on to tried and tested machine learning techniques for predictive modelling and knowledge discovery. Literate programming and a consistent style are a focus throughout the book.
Become an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience Who This Book Is For If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills. What You Will Learn Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux Get data ready for your data science project Manipulate, fix, and explore data in order to solve data science problems Set up an experimental pipeline to test your data science hypotheses Choose the most effective and scalable learning algorithm for your data science tasks Optimize your machine learning models to get the best performance Explore and cluster graphs, taking advantage of interconnections and links in your data In Detail Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users. Style and approach The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
Gain useful insights from your data using popular data science tools Key FeaturesA one-stop guide to Python libraries such as pandas and NumPyComprehensive coverage of data science operations such as data cleaning and data manipulationChoose scalable learning algorithms for your data science tasksBook Description Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users What you will learnSet up your data science toolbox on Windows, Mac, and LinuxUse the core machine learning methods offered by the scikit-learn libraryManipulate, fix, and explore data to solve data science problemsLearn advanced explorative and manipulative techniques to solve data operationsOptimize your machine learning models for optimized performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataWho this book is for If you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.
NSCA's Essentials of Sport Science provides the most contemporary and comprehensive overview of the field of sport science and the role of the sport scientist. It is a primary preparation resource for the Certified Performance and Sport Scientist (CPSS) certification exam.
Want to know how to implement authentic STEM teaching and learning into your classroom? STEM Lesson Essentials provides all the tools and strategies you'll need to design integrated, interdisciplinary STEM lessons and units that are relevant and exciting to your students. With clear definitions of both STEM and STEM literacy, the authors argue that STEM in itself is not a curriculum, but rather a way of organizing and delivering instruction by weaving the four disciplines together in intentional ways. Rather than adding two new subjects to the curriculum, the engineering and technology practices can instead be blended into existing math and science lessons in ways that engage students and help them master 21st century skills.
This kit has been devised to help teachers of primary science in schools of all sizes. The two-year age band structure, the compact size of the resources, correlation to the QCA Scheme of Work and recommended teaching times all contribute to making teaching science more effective in the school. The Teacher Resource Books contain a comprehensive series of clearly structured lesson plans that enable you to teach the QCA Scheme of Work, with six units in each year.
Order and Disorder is a non-mathematical introduction to the most important ideas in science for university students not majoring in a scientific area. The objective is to prepare non-science students for making both life and political decisions involving science and helping them to understand the advances in sciences as covered and reported in the mass media. The book is highly comprehensive in physics and chemistry, and provides the essential background to discuss and gain a better appreciation of selected topics in biological and neurological sciences, as well as astronomy and geology. The topics are integrated with the recurring theme of order and disorder, the organization achieved in the face of the never-ending drive towards disorganization. The book also lends an insight into some considerations of the philosophy of science, as well as the applications of science to health and a variety of other professions. The reading experience is enhanced by the provision of illuminating examples, supplementary reading and a summary of each chapter. New terminologies, that appear here for the first time, are set off in bold italics and annotated in the glossary. Where basic principles are introduced and explained, these are highlighted prominently in bold for ease of reference. Stimulating discussion questions are presented at the end of each chapter, giving readers some food for thought./a
The Ninth Edition teaches students the technical skills required of today’s paramedic while emphasizing other important professional attributes, including critical thinking, empathy, teamwork, communication, problem solving, and personal well-being.
Feel confident navigating the fundamentals of data science Data Science Essentials For Dummies is a quick reference on the core concepts of the exploding and in-demand data science field, which involves data collection and working on dataset cleaning, processing, and visualization. This direct and accessible resource helps you brush up on key topics and is right to the point—eliminating review material, wordy explanations, and fluff—so you get what you need, fast. Strengthen your understanding of data science basics Review what you've already learned or pick up key skills Effectively work with data and provide accessible materials to others Jog your memory on the essentials as you work and get clear answers to your questions Perfect for supplementing classroom learning, reviewing for a certification, or staying knowledgeable on the job, Data Science Essentials For Dummies is a reliable reference that's great to keep on hand as an everyday desk reference.