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The New Science Encyclopedia : Chemistry • Physics • Biology
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Algorithms for Data Science
This textbook on practical data analytics unites fundamental principles, algorithms, and data.Algorithms are the keystone of data analytics and the focal point of this textbook.Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent.But practical data analytics requires more than just the foundations.Problems and data are enormously variable and only the most elementary of algorithms can be used without modification.Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis.By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction.The second chapter introduces associative statistics, themathematical foundation of scalable algorithms and distributed computing.Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II.The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics.The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail.A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science.The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty.The core material of every chapter is accessible to all with these prerequisites.The chapters often expand at the close with innovations of interest to practitioners of data science.Each chapter includes exercises of varying levels of difficulty.The text is eminently suitable for self-study and an exceptional resource for practitioners.
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Simulation Algorithms for Computational Systems Biology
This book explains the state-of-the-art algorithms used to simulate biological dynamics.Each technique is theoretically introduced and applied to a set of modeling cases.Starting from basic simulation algorithms, the book also introduces more advanced techniques that support delays, diffusion in space, or that are based on hybrid simulation strategies. This is a valuable self-contained resource for graduate students and practitioners in computer science, biology and bioinformatics.An appendix covers the mathematical background, and the authors include further reading sections in each chapter.
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Discover Biology, Chemistry & Physics
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What are algorithms in computer science?
Algorithms in computer science are step-by-step procedures or formulas for solving a problem or accomplishing a task. They are a set of rules or instructions that are followed to achieve a particular goal. Algorithms are used in various computer science applications, such as sorting data, searching for information, and performing calculations. They are essential in programming and software development as they provide a systematic way to solve problems and process data efficiently.
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How do sorting algorithms work in computer science?
Sorting algorithms in computer science work by rearranging a collection of items into a specified order. They achieve this by comparing elements and swapping them based on a specific criteria, such as numerical value or alphabetical order. There are various sorting algorithms, each with its own approach and efficiency, such as bubble sort, merge sort, quick sort, and insertion sort. The choice of sorting algorithm depends on the size of the data set, the nature of the data, and the desired time and space complexity.
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Do Muslims have algorithms, Arabic numerals, and advancements in medicine?
Yes, Muslims have made significant contributions to the development of algorithms, Arabic numerals, and advancements in medicine. The concept of algorithms was developed by Muslim mathematicians such as Al-Khwarizmi, whose work laid the foundation for modern algorithms. Arabic numerals, including the concept of zero, were also developed by Muslim mathematicians and are widely used in mathematics and science today. In the field of medicine, Muslim scholars made significant advancements in areas such as surgery, pharmacology, and medical ethics, which have had a lasting impact on the practice of medicine.
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For which problems can algorithms be used in computer science and for which problems can algorithms not be used?
Algorithms can be used in computer science to solve a wide range of problems, including sorting data, searching for specific items, optimizing routes, and processing large amounts of information. However, there are certain problems for which algorithms may not be suitable, such as those that are inherently unpredictable or require human intuition and creativity. Additionally, algorithms may not be effective for problems with extremely large or complex datasets that exceed the capabilities of current computing technology.
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Graph Algorithms for Data Science
Graphs are the natural way to understand connected data.This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment. In Graph Algorithms for Data Science you will learn: Labeled-property graph modelingConstructing a graph from structured data such as CSV or SQLNLP techniques to construct a graph from unstructured dataCypher query language syntax to manipulate data and extract insightsSocial network analysis algorithms like PageRank and community detectionHow to translate graph structure to a ML model input with node embedding modelsUsing graph features in node classification and link prediction workflows Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis.It's filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs.You'll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more.You don't need any graph experience to start benefiting from this insightful guide.These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects. about the technology Graphs reveal the relationships in your data.Tracking these interlinking connections reveals new insights and influences and lets you analyze each data point as part of a larger whole.This interconnected data is perfect for machine learning, as well as analyzing social networks, communities, and even product recommendations. about the book Graph Algorithms for Data Science teaches you how to construct graphs from both structured and unstructured data.You'll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights.The book explores common and useful graph algorithms like PageRank and community detection/clustering algorithms.Each new algorithm you learn is instantly put into action to complete a hands-on data project, including modeling a social network!Finally, you'll learn how to utilize graphs to upgrade your machine learning, including utilizing node embedding models and graph neural networks.
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Plasma Science and Technology : Lectures in Physics, Chemistry, Biology, and Engineering
Plasma Science and Technology An accessible introduction to the fundamentals of plasma science and its applications In Plasma Science and Technology: Lectures in Physics, Chemistry, Biology, and Engineering, distinguished researcher Dr. Alexander Fridman delivers a comprehensive introduction to plasma technology, including fulsome descriptions of the fundamentals of plasmas and discharges.The author discusses a wide variety of practical applications of the technology to medicine, energy, catalysis, coatings, and more, emphasizing engineering and science fundamentals.Offering readers illuminating problems and concept questions to support understanding and self-study, the book also details organic and inorganic applications of plasma technologies, demonstrating its use in nature, in the lab, and in both novel and well-known applications.Readers will also find: A thorough introduction to the kinetics of excited atoms and molecules Comprehensive explorations of non-equilibrium atmospheric pressure cold discharges Practical discussions of plasma processing in microelectronics and other micro-technologies Expert treatments of plasma in environmental control technologies, including the cleaning of air, exhaust gases, water, and soil Perfect for students of chemical engineering, physics, and chemistry, Plasma Science and Technology will also benefit professionals working in these fields who seek a contemporary refresher in the fundamentals of plasma science and its applications.
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Physics in Biology and Medicine
**Selected for Doody’s Core Titles® 2024 in Medical Physics** Physics in Biology and Medicine, Sixth Edition includes new, revised material, and corresponding exercises on many exciting developments in the field.New sections cover biomechanics, biotribology, frictional properties of biological materials, 3-D printing and its use in medicine, new materials in dentistry, microfluidics, bioelectronic medicine, microsensors, and microscopy.This revised edition delivers helpful and engaging additions to the role and importance of physics in biology and medicine, including new coverage on metamaterials, metabolism, and environmental science.It is ideal for courses in biophysics, medical physics, and related subjects.
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Physics in Biology and Medicine
A best-selling resource now in its fifth edition, Paul Davidovits’ Physics in Biology and Medicine provides a high-quality and highly relevant physics grounding for students working toward careers in the medical and related professions.The text does not assume a prior background in physics, but provides it as required.It discusses biological systems that can be analyzed quantitatively and demonstrates how advances in the life sciences have been aided by the knowledge of physical or engineering analysis techniques, with applications, practice, and illustrations throughout. Physics in Biology and Medicine, Fifth Edition, includes new material and corresponding exercises on many exciting developments in the field since the prior edition, including biomechanics of joint replacement; biotribology and frictional properties of biological materials such as saliva, hair, and skin; 3-D printing and its use in medicine; new materials in dentistry; microfluidics and its applications to medicine; health, fractals, and the second law of thermodynamics; bioelectronic medicine; microsensors in medicine; role of myelin in learning, cryoelectron microscopy; clinical uses of sound; health impact of nanoparticle in polluted air. This revised edition delivers a concise and engaging introduction to the role and importance of physics in biology and medicine.It is ideal for courses in biophysics, medical physics, and related subjects.
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What do algorithms achieve?
Algorithms achieve the ability to process and analyze large amounts of data quickly and efficiently. They help in making predictions, identifying patterns, and solving complex problems. Algorithms are used in various fields such as finance, healthcare, and technology to optimize processes and improve decision-making. Overall, algorithms play a crucial role in automating tasks, improving productivity, and driving innovation.
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What do algorithms calculate?
Algorithms are designed to calculate specific tasks or operations based on a set of instructions. They can be used to perform mathematical calculations, process data, analyze patterns, make decisions, and solve problems. In essence, algorithms are used to automate and streamline various processes by following a predefined sequence of steps to produce a desired outcome.
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What are the Instagram algorithms?
The Instagram algorithms are a set of complex calculations used by the platform to determine what content users see on their feed. These algorithms analyze user behavior, such as likes, comments, and shares, to prioritize content from accounts that users engage with the most. The algorithms also take into account the timeliness of posts, the relationship between users, and the type of content being shared. By using these algorithms, Instagram aims to show users the most relevant and engaging content on their feed.
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Which sorting algorithms are there?
There are several common sorting algorithms, including bubble sort, selection sort, insertion sort, merge sort, quick sort, and heap sort. Each algorithm has its own advantages and disadvantages in terms of time complexity, space complexity, and stability. The choice of sorting algorithm depends on the specific requirements of the problem at hand.
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