graph algorithms for data science bratanic tomaz

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 modeling Constructing a graph from structured data such as CSV or SQL NLP techniques to construct a graph from unstructured data Cypher query language syntax to manipulate data and extract insights Social network analysis algorithms like PageRank and community detection How to translate graph structure to a ML model input with node embedding models Using 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 ... Unknown localization key: "more"

Objev podobné jako Graph Algorithms for Data Science - Bratanic Tomaz

A single-volume reference on data science techniques for evaluating and solving business problems using Applied Machine Learning (ML). Applied Machine Learning for Data Science Practitioners offers a practical, step-by-step guide to building end-to-end ML solutions for real-world business challenges, empowering data science practitioners to make informed decisions and select the right techniques for any use case. Unlike many data science books that focus on popular algorithms and coding, this book takes a holistic approach. It equips you with the knowledge to evaluate a range of techniques and algorithms. The book balances theoretical concepts with practical examples to illustrate key concepts, derive insights, and demonstrate applications. In addition to code snippets and reviewing output, the book provides guidance on interpreting results. This book is an essential resource if you are looking to elevate your understanding of ML and your technical capabilities, combining theoretical and practical coding examples. A basic understanding of using data to solve business problems, high school-level math and statistics, and basic Python coding skills are assumed. Written by a recognized data science expert, Applied Machine Learning for Data Science Practitioners covers essential topics, including: Data Science Fundamentals that provide you with an overview of core concepts, laying the ... Unknown localization key: "more"

Objev podobné jako Applied Machine Learning for Data Science Practitioners - Vidya Subramanian

Data science and machine learning—two of the world''s hottest fields—are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world''s #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help students with widely diverse backgrounds learn foundational Python so they can use it for data science and machine learning. This book is catered to introductory-level college courses on data science. Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once students have learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving. Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and more—all created with Colab (Jupyter compatible) notebooks, so students can execute all coding examples interactively without installing or configuring any software.

Objev podobné jako Foundational Python for Data Science - Kennedy Behrman

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Learn how to use R to turn data into insight, knowledge, and understanding. Ideal for current and aspiring data scientists, this book introduces you to doing data science with R and RStudio, as well as the tidyverse-a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly.You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Each section in this edition includes exercises to help you practice what you've learned along the way.Updated for the latest tidyverse best practices, new chapters dive deeper into visualization and data wrangling, show you how to get data from spreadsheets, databases, and websites, and help you make the most of new programming tools. You'll learn how to:Visualize-create plots for data exploration and communication of resultsTransform-discover types of variables and the tools you can use to work with themImport-get data into R and in a form convenient for analysisProgram-learn R tools for solving data problems with ... Unknown localization key: "more"

Objev podobné jako R for Data Science - Grolemund Garrett, Hadley Wickham, Mine Cetinkaya-Rundel

Fast Python for Data Science is a hands-on guide to writing Python code that can process more data, faster, and with less resources. It takes a holistic approach to Python performance, showing you how your code, libraries, and computing architecture interact and can be optimized together. Written for experienced practitioners, Fast Python for Data Science dives right into practical solutions for improving computation and storage efficiency. You''ll experiment with fun and interesting examples such as rewriting games in lower-level Cython and implementing a MapReduce framework from scratch. Finally, you''ll go deep into Python GPU computing and learn how modern hardware has rehabilitated some former antipatterns and made counterintuitive ideas the most efficient way of working. About the technologyFast, accurate systems are vital for handling the huge datasets and complex analytical algorithms that are common in modern data science. Python programmers need to boost performance by writing faster pure-Python programs, optimizing the use of libraries, and utilizing modern multi-processor hardware; Fast Python for Data Science shows you how.

Objev podobné jako Fast Python for Data Science - Tiago Antao

Let Python do the heavy lifting for you as you analyze large datasets Python for Data Science For Dummies lets you get your hands dirty with data using one of the top programming languages. This beginner’s guide takes you step by step through getting started, performing data analysis, understanding datasets and example code, working with Google Colab, sampling data, and beyond. Coding your data analysis tasks will make your life easier, make you more in-demand as an employee, and open the door to valuable knowledge and insights. This new edition is updated for the latest version of Python and includes current, relevant data examples. Get a firm background in the basics of Python coding for data analysisLearn about data science careers you can pursue with Python coding skillsIntegrate data analysis with multimedia and graphics Manage and organize data with cloud-based relational databasesPython careers are on the rise. Grab this user-friendly Dummies guide and gain the programming skills you need to become a data pro.

Objev podobné jako Python for Data Science For Dummies - John Paul Mueller, Luca Massaron

When you know the cause of an event, you can affect its outcome. This accessible introduction to causal inference shows you how to determine causality and estimate effects using statistics and machine learning.In Causal Inference for Data Science you will learn how to: Model reality using causal graphs Estimate causal effects using statistical and machine learning techniques Determine when to use A/B tests, causal inference, and machine learning Explain and assess objectives, assumptions, risks, and limitations Determine if you have enough variables for your analysis It''s possible to predict events without knowing what causes them. Understanding causality allows you both to make data-driven predictions and also intervene to affect the outcomes. Causal Inference for Data Science shows you how to build data science tools that can identify the root cause of trends and events. You''ll learn how to interpret historical data, understand customer behaviors, and empower management to apply optimal decisions.

Objev podobné jako Causal Inference for Data Science - Aleix de Villa

Learn how to use Python and associated data-science libraries to work with and visualize vectors and matrices and their operations, as well import data to apply these techniques. Learn basics of performing vector and matrix operations by hand, how to use several different Python libraries for performing operations.

Objev podobné jako Linear Algebra for Data Science with Python - John M. Shea

Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package. Students are introduced to matrices in two preliminary chapters, before progressing to advanced topics such as the nuclear norm, proximal operators and convex optimization. Highlighted applications include low-rank approximation, matrix completion, subspace learning, logistic regression for binary classification, robust PCA, dimensionality reduction and Procrustes problems. Extensively classroom-tested, the book includes over 200 multiple-choice questions suitable for in-class interactive learning or quizzes, as well as homework exercises (with solutions available for instructors). It encourages active learning with engaging ''explore'' questions, with answers at the back of each chapter, and Julia code examples to demonstrate how the mathematics is actually used in practice. A suite of computational notebooks offers a hands-on learning experience for students. This is a perfect textbook for upper-level undergraduates and first-year graduate students who have taken a prior course in linear algebra basics.

Objev podobné jako Linear Algebra for Data Science, Machine Learning, and Signal Processing - Jeffrey A. Fessler, Raj Rao Nadakuditi

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book''s web site.

Objev podobné jako Mathematics for Machine Learning - A. Aldo Faisal, Marc Peter Deisenroth, Cheng Soon Ong

Upgrade your RAG applications with the power of knowledge graphs.Retrieval Augmented Generation (RAG) is a great way to harness the power of generative AI for information not contained in a LLM''s training data and to avoid depending on LLM for factual information. However, RAG only works when you can quickly identify and supply the most relevant context to your LLM. Essential GraphRAG shows you how to use knowledge graphs to model your RAG data and deliver better performance, accuracy, traceability, and completeness.Inside Essential GraphRAG you''ll learn: The benefits of using Knowledge Graphs in a RAG system How to implement a GraphRAG system from scratch The process of building a fully working production RAG system Constructing knowledge graphs using LLMs Evaluating performance of a RAG pipeline Essential GraphRAG is a practical guide to empowering LLMs with RAG. You''ll learn to deliver vector similarity-based approaches to find relevant information, as well as work with semantic layers, and generate Cypher statements to retrieve data from a knowledge graph.

Objev podobné jako Essential GraphRAG - Bratanic Tomaz

As a software engineer, you’ll encounter countless programming challenges that initially seem confusing, difficult, or even impossible. Don’t despair! Many of these “new” problems already have well-established solutions. Advanced Algorithms and Data Structures teaches you powerful approaches to a wide range of tricky coding challenges that you can adapt and apply to your own applications. Providing a balanced blend of classic, advanced, and new algorithms, this practical guide upgrades your programming toolbox with new perspectives and hands-on techniques. about the technology Data structures and algorithms are the foundations for how programs store and process information. Choosing the optimal algorithms ensures that your programs are fast, efficient, and reliable. about the book Algorithms and Data Structures in Action expands on the basic algorithms you already know to give you a better selection of solutions to different programming problems. In it, you’ll discover techniques for improving priority queues, efficient caching, clustering data, and more. Each example is fully illustrated with graphics, language agnostic pseudo-code, and code samples in various languages. When you’re done, you will be able to implement advanced and little-known algorithms to deliver better performance from your code. what''s inside Improving on basic data structures Efficient caching Nearest neighbour search, ... Unknown localization key: "more"

Objev podobné jako Algorithms and Data Structures in Action - Marcello La Rocca

If you thought data structures and algorithms were all just theory, you''re missing out on what they can do for your JavaScript code. Learn to use Big O notation to make your code run faster by orders of magnitude. Choose from data structures such as hash tables, trees, and graphs to increase your code''s efficiency exponentially. With simple language and clear diagrams, this book makes this complex topic accessible, no matter your background. Every chapter features practice exercises to give you the hands-on information you need to master data structures and algorithms for your day-to-day work.Algorithms and data structures are much more than abstract concepts. Mastering them enables you to write code that runs faster and more efficiently, which is particularly important for today''s web and mobile apps. Take a practical approach to data structures and algorithms, with techniques and real-world scenarios that you can use in your daily production code. The JavaScript edition uses JavaScript exclusively for all code examples, exercises, and solutions.Use Big O notation to measure and articulate the efficiency of your code, and modify your algorithm to make it faster. Find out how your choice of arrays, linked lists, and hash tables can dramatically affect the ... Unknown localization key: "more"

Objev podobné jako A Common-Sense Guide to Data Structures and Algorithms in Javascript, Volume 1 - Jay Wengrow

Data Science for Business with R, written by Jeffrey S. Saltz and Jeffrey M. Stanton, focuses on the concepts foundational for students starting a business analytics or data science degree program. To keep the book practical and applied, the authors feature a running case using a global airline business’s customer survey dataset to illustrate how to turn data in business decisions, in addition to numerous examples throughout. To aid in usability beyond the classroom, the text features full integration of freely-available R and RStudio software, one of the most popular data science tools available. Designed for students with little to no experience in related areas like computer science, the book chapters follow a logical order from introduction and installation of R and RStudio, working with data architecture, undertaking data collection, performing data analysis, and transitioning to data archiving and presentation. Each chapter follows a familiar structure, starting with learning objectives and background, following the basic steps of functions alongside simple examples, applying these functions to the case study, and ending with chapter challenge questions, sources, and a list of R functions so students know what to expect in each step of their data science course. Data Science for Business with ... Unknown localization key: "more"

Objev podobné jako Data Science for Business With R - Jeffrey Morgan Stanton, Jeffrey S. Saltz

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 basicsReview what you've already learned or pick up key skillsEffectively work with data and provide accessible materials to othersJog 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.

Objev podobné jako Data Science Essentials For Dummies - Lillian Pierson

To succeed in AI and data science, you must first master APIs. API skills are essential for AI and data science success. With this book, data scientists and software developers will gain hands-on experience developing and using APIs with the Python programming language and popular frameworks like FastAPI and StreamLit.

Objev podobné jako Hands-On APIs for AI and Data Science - Ryan Day

Learn data science with Python by building five real-world projects! In Data Science Bookcamp you’ll test and build your knowledge of Python and learn to handle the kind of open-ended problems that professional data scientists work on daily. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career. about the technologyIn real-world practice, data scientists create innovative solutions to novel open ended problems. Easy to learn and use, the Python language has become the de facto language for data science amongst researchers, developers, and business users. But knowing a few basic algorithms is not enough to tackle a vague and thorny problem. It takes relentless practice at cracking difficult data tasks to achieve mastery in the field. That’s just what this book delivers. about the book Data Science Bookcamp is a comprehensive set of challenging projects carefully designed to grow your data science skills from novice to master. Veteran data scientist Leonard Apeltsin sets five increasingly difficult exercises that test your abilities against the kind of problems you’d encounter in the real world. As you solve each challenge, you’ll acquire and expand the ... Unknown localization key: "more"

Objev podobné jako Data Science Bookcamp - Leonard Apeltsin

As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize machine learning effectively.Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders.Designed as a guiding companion for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers, and industry professionals from various backgrounds.

Objev podobné jako Data Science and Machine Learning for Non-Programmers - Dothang Truong

Unlock the Power of Data: Transform Your Marketing Strategies with Data Science In the digital age, understanding the symbiosis between marketing and data science is not just an advantage; it's a necessity. In Mastering Marketing Data Science: A Comprehensive Guide for Today's Marketers, Dr. Iain Brown, a leading expert in data science and marketing analytics, offers a comprehensive journey through the cutting-edge methodologies and applications that are defining the future of marketing. This book bridges the gap between theoretical data science concepts and their practical applications in marketing, providing readers with the tools and insights needed to elevate their strategies in a data-driven world. Whether you're a master's student, a marketing professional, or a data scientist keen on applying your skills in a marketing context, this guide will empower you with a deep understanding of marketing data science principles and the competence to apply these principles effectively. Comprehensive Coverage: From data collection to predictive analytics, NLP, and beyond, explore every facet of marketing data science. Practical Applications: Engage with real-world examples, hands-on exercises in both Python & SAS, and actionable insights to apply in your marketing campaigns. Expert Guidance: Benefit from Dr. Iain Brown's decade of experience as he shares ... Unknown localization key: "more"

Objev podobné jako Mastering Marketing Data Science - Iain Brown

A friendly, fully-illustrated introduction to the most important computer programming algorithms. The algorithms you''ll use most often as a programmer have already been discovered, tested, and proven. This book will prepare you for those pesky algorithms questions in every programming job interview and help you apply them in your day-to-day work. And if you want to understand them without slogging through dense multipage proofs, this is the book for you. In Grokking Algorithms, Second Edition you will discover: Search, sort, and graph algorithms Data structures such as arrays, lists, hash tables, trees, and graphs NP complete and greedy algorithms Performance trade-offs between algorithms Exercises and code samples in every chapter Over 400 illustrations with detailed walkthroughs The first edition of Grokking Algorithms proved to over 100,000 readers that learning algorithms doesn''t have to be complicated or boring! This new edition now includes fresh coverage of trees, NP complete problems, and code updates to Python 3. With easy-to-read, friendly explanations, clever examples, and exercises to sharpen your skills as you learn, you’ll actually enjoy learning these important algorithms.

Objev podobné jako Grokking Algorithms - Aditya Bhargava

AI is primed to revolutionize the way we build applications, offering exciting new ways to solve problems, uncover insights, innovate new products, and provide better user experiences. Successful AI is based on a set of core algorithms that form a base of knowledge shared by all data scientists. Grokking Artificial Intelligence Algorithms is a fully-illustrated and interactive tutorial guide to the different approaches and algorithms that underpin AI. Written in simple language and with lots of visual references and hands-on examples, readers learn the concepts, terminology, and theory they need to effectively incorporate AI algorithms into their applications. Grokking Artificial Intelligence Algorithms uses simple language, jargon-busting explanations, and hand-drawn diagrams to open up complex algorithms. Don’t worry if you aren’t a calculus wunderkind; you’ll need only the algebra you picked up in math class. • Use cases for different AI algorithms • How to encode problems and solutions using data structures • Intelligent search for game playing • Ant colony algorithms for path finding • Evolutionary algorithms for optimization problems For software developers with high school-level algebra and calculus skills.

Objev podobné jako Grokking Artificial Intelligence Algorithms - Rishal Hurbans

Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming. It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated using a diverse collection of data sets to conduct statistical tests related to contemporary topics, from the effects of socioeconomic factors on the spread of the COVID-19 virus to the impact of state laws on firearms mortality.This book can be used as an undergraduate textbook for an Introduction to Data Science course or to provide a ... Unknown localization key: "more"

Objev podobné jako Foundations of Data Science with Python - John M. Shea

This book is the result of several decades of teaching experience in data structures and algorithms. It is self-contained but does assume some prior knowledge of data structures, and a grasp of basic programming and mathematics tools. Basic Concepts in Algorithms focuses on more advanced paradigms and methods combining basic programming constructs as building blocks and their usefulness in the derivation of algorithms. Its coverage includes the algorithms'' design process and an analysis of their performance. It is primarily intended as a textbook for the teaching of Algorithms for second year undergraduate students in study fields related to computers and programming.Klein reproduces his oral teaching style in writing, with one topic leading to another, related one. Most of the classical and some more advanced subjects in the theory of algorithms are covered, though not in a comprehensive manner. The topics include Divide and Conquer, Dynamic Programming, Graph algorithms, probabilistic algorithms, data compression, numerical algorithms and intractability. Each chapter comes with its own set of exercises, and solutions to most of them are appended.Related Link(s)

Objev podobné jako Basic Concepts In Algorithms - Shmuel Tomi Klein

Practical, accessible guide to becoming a data scientist, updated to include the latest advances in data science and related fields. Becoming a data scientist is hard. The job focuses on mathematical tools, but also demands fluency with software engineering, understanding of a business situation, and deep understanding of the data itself. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. The focus of The Data Science Handbook is on practical applications and the ability to solve real problems, rather than theoretical formalisms that are rarely needed in practice. Among its key points are: An emphasis on software engineering and coding skills, which play a significant role in most real data science problems. Extensive sample code, detailed discussions of important libraries, and a solid grounding in core concepts from computer science (computer architecture, runtime complexity, and programming paradigms). A broad overview of important mathematical tools, including classical techniques in statistics, stochastic modeling, regression, numerical optimization, and more. Extensive tips about the practical realities of working as a data scientist, including understanding related jobs functions, project life cycles, and the varying roles of data science in an organization. Exactly the right amount of ... Unknown localization key: "more"

Objev podobné jako The Data Science Handbook - Field Cady

This broad, deep, but not-too-technical guide introduces you to the fundamental principles of data science and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect.

Objev podobné jako Data Science for Business - Foster Provost

With this hands-on guide, applied data scientists, machine learning engineers, and practitioners will learn how to build an E2E graph learning pipeline. You'll explore core challenges at each pipeline stage, from data acquisition and representation to real-time inference and feedback loop retraining.

Objev podobné jako Scaling Graph Learning for the Enterprise - Ahmed Menshawy, Sameh Mohamed, Maraim Rizk Masoud

Data science is the foundation of our modern world. It underlies applications used by billions of people every day, providing new tools, forms of entertainment, economic growth, and potential solutions to difficult, complex problems. These opportunities come with significant societal consequences, raising fundamental questions about issues such as data quality, fairness, privacy, and causation. In this book, four leading experts convey the excitement and promise of data science and examine the major challenges in gaining its benefits and mitigating its harms. They offer frameworks for critically evaluating the ingredients and the ethical considerations needed to apply data science productively, illustrated by extensive application examples. The authors'' far-ranging exploration of these complex issues will stimulate data science practitioners and students, as well as humanists, social scientists, scientists, and policy makers, to study and debate how data science can be used more effectively and more ethically to better our world.

Objev podobné jako Data Science in Context - Peter Norvig, Alfred Z. Spector, Jeannette M. Wing, Chris Wiggins

The leading introduction to computer algorithms in use today, including fifty algorithms every programmer should know Princeton Computer Science professors, Robert Sedgewick and Kevin Wayne, survey the most important computer algorithms in use and of interest to anyone working in science, mathematics, and engineering, and those who use computation in the liberal arts. They provide a full treatment of data structures and algorithms for key areas that enable you to confidently implement, debug, and put them to work in any computational environment. Fundamentals: Basic programming models Data abstraction Bags, queues, and stacks Analysis of algorithms Sorting Elementary sorts Mergesort Quicksort Priority queues Applications Graphs Undirected graphs Directed graphs Minimum spanning trees Shortest paths Strings String sorts Tries Substring search Regular expressions Data compression These algorithms are generally ingenious creations that, remarkably, can each be expressed in just a dozen or two lines of code. As a group, they represent problem-solving power of amazing scope. They have enabled the construction of computational artifacts, the solution of scientific problems, and the development of commercial applications that would not have been feasible without them.

Objev podobné jako Algorithms - Kevin Wayne, Robert Sedgewick

In today''s data-driven world, the ability to extract meaningful insights from vast datasets is crucial for success in various fields. This ultimate book for mastering open-source libraries of data science in Python equips you with the essential tools and techniques to navigate the ever-evolving field of data analysis and visualization. Discover how to use Python libraries like NumPy, Pandas, and Matplotlib for data manipulation, analysis, and visualization. This book also covers scientific computing with SciPy and integrates ChatGPT to boost your data science workflow. Designed for data scientists, analysts, and beginners, it offers a practical, hands-on approach to mastering data science fundamentals. With real-world applications and exercises, you will turn raw data into actionable insights, gaining a competitive edge. This book covers everything you need, including open-source libraries, Visual Explorer tools, and ChatGPT, making it a one-stop resource for Python-based data science.

Objev podobné jako Ultimate Data Science Programming in Python - Saurabh Chandrakar

Science, engineering, and technology permeate nearly every facet of modern life and hold the key to solving many of humanity's most pressing current and future challenges. The United States' position in the global economy is declining, in part because U.S. workers lack fundamental knowledge in these fields. To address the critical issues of U.S. competitiveness and to better prepare the workforce, A Framework for K-12 Science Education proposes a new approach to K-12 science education that will capture students' interest and provide them with the necessary foundational knowledge in the field. A Framework for K-12 Science Education outlines a broad set of expectations for students in science and engineering in grades K-12. These expectations will inform the development of new standards for K-12 science education and, subsequently, revisions to curriculum, instruction, assessment, and professional development for educators. This book identifies three dimensions that convey the core ideas and practices around which science and engineering education in these grades should be built. These three dimensions are: crosscutting concepts that unify the study of science through their common application across science and engineering; scientific and engineering practices; and disciplinary core ideas in the physical sciences, life sciences, and earth and space sciences ... Unknown localization key: "more"

Objev podobné jako A Framework for K-12 Science Education - National Research Council, Board on Science Education, Division of Behavioral and Social Sciences and Educati

This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to understand and analyze the role of geography in everyday life. Geographic Data Science with Python introduces a new way of thinking about analysis, by using geographical and computational reasoning, it shows the reader how to unlock new insights hidden within data.Key Features:● Showcases the excellent data science environment in Python.● Provides examples for readers to replicate, adapt, extend, and improve.● Covers the crucial knowledge needed by geographic data scientists.It presents concepts in a far more geographic way than competing textbooks, covering spatial data, mapping, and spatial statistics whilst covering concepts, such as clusters and outliers, as geographic concepts.Intended for data scientists, GIScientists, and geographers, the material provided in this book is of interest due to the manner in which it presents geospatial data, methods, tools, and practices in this new field.

Objev podobné jako Geographic Data Science with Python - Dani Arribas-Bel, Sergio Rey, Levi John Wolf

From a review of the first edition: "Modern Data Science with R… is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician).Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions.The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.

Objev podobné jako Modern Data Science with R - Benjamin S. Baumer, Nicholas J. Horton, Daniel T. Kaplan

Learn Data Science from Scratch equips you with the essential tools and techniques, from Python libraries to machine learning algorithms, to tackle real-world problems and make informed decisions. This book provides a thorough exploration of essential data science concepts, tools, and techniques. Starting with the fundamentals of data science, you will progress through data collection, web scraping, data exploration and visualization, and data cleaning and pre-processing. You will build the required foundation in statistics and probability before diving into machine learning algorithms, deep learning, natural language processing, recommender systems, and data storage systems. With hands-on examples and practical advice, each chapter offers valuable insights and key takeaways, empowering you to master the art of data-driven decision making.

Objev podobné jako Learn Data Science from Scratch - Pratheerth Padman

Statistical Analytics for Health Data Science with SAS and R Set compiles fundamental statistical principles with advanced analytical techniques and covers a wide range of statistical methodologies. With an emphasis on real-world applications, it integrates publicly available health datasets and provides case studies.

Objev podobné jako Statistical Analytics for Health Data Science with SAS and R Set - Jeffrey Wilson, Ding-Geng Chen, Karl E. Peace

While data science is powering most modern organisations its technical intricacies often remain confined to a select few.This book is for those starting to work within this field to give them the foundations on which to build their confidence and understanding. Allowing them to contribute to projects through making decisions and acting on them. Written in an accessible manner, Data Science Foundations provides explanations of complex concepts and methods in layman’s terms supported by examples. It also includes a holistic view of data science covering the technical, ethical and delivery challenges illustrated by case studies.Aligned with industry-recognised qualifications, this is the definitive guide for aspiring data scientists, providing a solid foundation in data analysis techniques.

Objev podobné jako Data Science Foundations - Ian Huke, Stephen Mariadas

A computationally oriented comparison of solution algorithms for two stage and jointly chance constrained stochastic linear programming problems, this is the first book to present comparative computational results with several major stochastic programming solution approaches. The following methods are considered: regularized decomposition, stochastic decomposition and successive discrete approximation methods for two stage problems; cutting plane methods, and a reduced gradient method for jointly chance constrained problems. The first part of the book introduces the algorithms, including a unified approach to decomposition methods and their regularized counterparts. The second part addresses computer implementation of the methods, describes a testing environment based on a model management system, and presents comparative computational results with the various algorithms. Emphasis is on the computational behavior of the algorithms.

Objev podobné jako Stochastic Linear Programming Algorithms - Janos Mayer

A practical field guide for the unique challenges of data science leadership, filled with transformative insights, personal experiences, and industry examples.In How to Lead in Data Science you''ll master techniques for leading data science at every seniority level, from heading up a single project to overseeing a whole company''s data strategy. You''ll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away.

Objev podobné jako How to Lead in Data Science - Jike Chong, Yue Chang

This beginner''s book will teach you how to apply the principles of data science to improve your business strategies - no math proficiency required! Easy-to-follow chapters take the reader through concepts like A/B testing, supervised and unsupervised machine learning, web scraping, and more. Each concept is illustrated using real-world business applications, real-world data, and useful Python code examples. The tone is conversational, and the author avoids the dense mathematical theories associated with data science in favour of simple explanations and practical applications. By the end of the book, readers should be comfortable working with data, applying data to business problems, and using best practices to analyse data using Python.

Objev podobné jako Dive Into Data Science - Bradford Tuckfield

This book attempts to advance Donald Griffin''s vision of the "final, crowning chapter of the Darwinian revolution" by developing a philosophy for the science of animal consciousness. It advocates a Darwinian bottom-up approach that treats consciousness as a complex, evolved, and multidimensional phenomenon in nature rather than a mysterious all-or-nothing property immune to the tools of science and restricted to a single species.The so-called emergence of a science of consciousness in the 1990s has at best been a science of human consciousness. This book aims to advance a true Darwinian science of consciousness in which its evolutionary origin, function, and phylogenetic diversity are moved from the fieldÂ’s periphery to its very centre, thus enabling us to integrate consciousness into an evolutionary view of life. Accordingly, this book has two objectives: (i) to argue for the need and possibility of an evolutionary bottom-up approach that addresses the problem of consciousness in terms of the evolutionary origins of a new ecological lifestyle that made consciousness worth having and (ii) to articulate a thesis and beginnings of a theory of the place of consciousness as a complex evolved phenomenon in nature that can help us to answer the question of what it is ... Unknown localization key: "more"

Objev podobné jako A Philosophy for the Science of Animal Consciousness - Walter Veith

Lead your organization to become evidence-drivenData. It’s the benchmark that informs corporate projections, decision-making, and analysis. But, why do many organizations that see themselves as data-driven fail to thrive? In Leading with AI and Analytics, two renowned experts from the Kellogg School of Management show business leaders how to transform their organization to become evidence-driven, which leads to real, measurable changes that can help propel their companies to the top of their industries.The availability of unprecedented technology-enabled tools has made AI (Artificial Intelligence) an essential component of business analytics. But what’s often lacking are the leadership skills to integrate these technologies to achieve maximum value. Here, the authors provide a comprehensive game plan for developing that all-important human factor to get at the heart of data science: the ability to apply analytical thinking to real-world problems. Each of these tools and techniques comes to powerful life through a wealth of powerful case studies and real-world success stories.Inside, you’ll find the essential tools to help you:Develop a strong data science intuition quotientLead and scale AI and analytics throughout your organizationMove from “best-guess” decision making to evidence-based decisionsCraft strategies and tactics to create real impactWritten for anyone in a leadership or management ... Unknown localization key: "more"

Objev podobné jako Leading with AI and Analytics: Build Your Data Science IQ to Drive Business Value - Eric Anderson, Florian Zettelmeyer

Covid-19 has shown us the importance of mathematical and statistical models to interpret reality, provide forecasts, and explore future scenarios.Algorithms, artificial neural networks, and machine learning help us discover the opportunities and pitfalls of a world governed by mathematics and artificial intelligence.

Objev podobné jako Algorithms for a New World - Alfio Quarteroni

Please note this title is suitable for any student studying:Exam Board: AQA Level: GCSE Subject: Biology for Combined Science (Trilogy)First teaching: September 2016 First exams: June 2018The UK''s bestselling GCSE Science series is specifically tailored for the current AQA GCSE Science (9-1) specifications. This workbook is the perfect companion for use throughout the course, supporting students on their journey from KS3 to success at GCSE. Stretches and challenges students sitting the higher tier for AQA Combined Science: Trilogy GCSETopics follow the order of the Student Book, with carefully designed questions to help capture important notes as students work through the courseActivities help develop higher-order skills and deepen understanding of key conceptsIncludes questions and activities specifically focused on the required maths and practical skillsIncludes hints to help students answer difficult questionsChecklists help students to monitor their own progress

Objev podobné jako AQA GCSE Biology for Combined Science (Trilogy) Workbook: Higher - Gemma Young

Please note this title is suitable for any student studying:Exam Board: AQA Level: GCSE Subject: Chemistry for Combined Science (Trilogy)First teaching: September 2016 First exams: June 2018The UK''s bestselling GCSE Science series is specifically tailored for the current AQA GCSE Science (9-1) specifications. This workbook is the perfect companion for use throughout the course, supporting students on their journey from KS3 to success at GCSE. Stretches and challenges students sitting the higher tier in AQA GCSE Combined Science: TrilogyTopics follow the order of the Student Book, with carefully designed questions to help capture important notes as students work through the courseActivities help develop higher-order skills and deepen understanding of key conceptsIncludes questions and activities specifically focused on the required maths and practical skillsIncludes hints to help students answer difficult questionsChecklists help students to monitor their own progress

Objev podobné jako AQA GCSE Chemistry for Combined Science (Trilogy) Workbook: Higher - Philippa Gardom Hulme

This book provides insights into the true nature of financial and economic data, and is a practical guide on how to analyze a variety of data sources. The focus of the book is on finance and economics, but it also illustrates the use of quantitative analysis and data science in many different areas. Lastly, the book includes practical information on how to store and process data and provides a framework for data driven reasoning about the world.The book begins with entertaining tales from Graham Giller''s career in finance, starting with speculating in UK government bonds at the Oxford Post Office, accidentally creating a global instant messaging system that went ''viral'' before anybody knew what that meant, on being the person who forgot to hit ''enter'' to run a hundred-million dollar statistical arbitrage system, what he decoded from his brief time spent with Jim Simons, and giving Michael Bloomberg a tutorial on Granger Causality.The majority of the content is a narrative of analytic work done on financial, economics, and alternative data, structured around both Dr Giller''s professional career and some of the things that just interested him. The goal is to stimulate interest in predictive methods, to give accurate characterizations of ... Unknown localization key: "more"

Objev podobné jako Adventures In Financial Data Science: The Empirical Properties Of Financial And Economic Data - Graham L Giller

Develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with an exclusive focus on the popular data science tool Python. The book includes many examples of real-life applications, with practice ranging from small to big data.

Objev podobné jako A Hands-On Introduction to Data Science with Python - Chirag Shah

Exam Board: AQALevel & Subject: GCSE Combined Science: SynergyFirst teaching: September 2016 Next exams: June 2026AQA approvedThis book covers Unit 2 of the new 2016 GCSE Combined Science: Synergy course. The grouping of content follows that in the specification. Topic openers set the scene by providing a short overview of the links between the different chapters in a Topic. Linking questions between spreads and from chapter to chapter also help explain the narrative thread that links a Synergy topic together. Working Scientifically objectives linked to the AQA Combined Science: Synergy specification are identified, and questions addressing Working Scientifically skills embedded throughout.* Each spread is divided into three sections – starting with language, ideas and questions that are accessible to all and increasing in complexity to develop and practise ideas further.* Key concept spreads highlight core ideas that students must grasp before they can move on, and which will develop their understanding of the whole topic.* A dedicated spread for every required practical helps students analyse the practical, reflect on the science skills and knowledge they have developed and apply these skills to different contexts.* Maths skills spreads focus on the maths requirements of the AQA Combined Science specification, explaining concepts ... Unknown localization key: "more"

Objev podobné jako AQA GCSE Physical Sciences for Combined Science: Synergy 9-1 Student Book - Katy Bloom

Exam Board: AQALevel & Subject: GCSE Combined Science: SynergyFirst teaching: September 2016 Next exams: June 2026AQA approvedThis book covers Unit 1 of the new 2016 GCSE Combined Science: Synergy course. The grouping of content follows that in the specification. Topic openers set the scene by providing a short overview of the links between the different chapters in a Topic. Linking questions between spreads and from chapter to chapter also help explain the narrative thread that links a Synergy topic together. Working Scientifically objectives linked to the AQA Combined Science: Synergy specification are identified, and questions addressing Working Scientifically skills embedded throughout.* Each spread is divided into three sections – starting with language, ideas and questions that are accessible to all and increasing in complexity to develop and practise ideas further.* Key concept spreads highlight core ideas that students must grasp before they can move on, and which will develop their understanding of the whole topic.* A dedicated spread for every required practical helps students analyse the practical, reflect on the science skills and knowledge they have developed and apply these skills to different contexts.* Maths skills spreads focus on the maths requirements of the AQA Combined Science specification, explaining concepts ... Unknown localization key: "more"

Objev podobné jako AQA GCSE Life and Environmental Sciences for Combined Science: Synergy 9-1 Student Book - Katy Bloom, Gina Walker, Shaista Shirazi

Please note this title is suitable for any student studying:Exam Board: AQA Level: GCSE Subject: Chemistry for Combined Science (Trilogy) - Foundation tierFirst teaching: September 2016 First exams: June 2018The UK''s bestselling GCSE Science series is specifically tailored for the current AQA GCSE Science (9-1) specifications. This workbook is the perfect companion for the series and supports students on their journey from KS3 to success at GCSE. Key features: - Additional support for Foundation students to help them succeed at GCSE- Topics follow the order of the Student Book, with carefully designed questions to help capture important notes as students work through the course - Plenty of engaging activities help consolidate learning and reinforce key concepts - Questions and activities specifically focused on the maths and practical skills required for the exam - ''I can'' checklists help students to monitor their own progress - ''What I need to remember'' to help students remember key concepts

Objev podobné jako AQA GCSE Chemistry for Combined Science (Trilogy) Workbook: Foundation - Philippa Gardom Hulme

This textbook thoroughly outlines combinatorial algorithms for generation, enumeration, and search. Topics include backtracking and heuristic search methods applied to various combinatorial structures, such as:CombinationsPermutationsGraphsDesignsMany classical areas are covered as well as new research topics not included in most existing texts, such as:Group algorithmsGraph isomorphismHill-climbingHeuristic search algorithmsThis work serves as an exceptional textbook for a modern course in combinatorial algorithms, providing a unified and focused collection of recent topics of interest in the area. The authors, synthesizing material that can only be found scattered through many different sources, introduce the most important combinatorial algorithmic techniques - thus creating an accessible, comprehensive text that students of mathematics, electrical engineering, and computer science can understand without needing a prior course on combinatorics.

Objev podobné jako Combinatorial Algorithms - Donald L. Kreher, Douglas R. Stinson

An invaluable resource for both exercise science majors and non-majors, ACSM's Nutrition for Exercise Science, 2nd Edition, demystifies the relationship between nutrition and exercise science and prepares you to confidently apply concepts to clinical practice. Drawing on author Dan Benardot's extensive experience as an instructor, scientist, this engaging, authoritative text delivers an evidence-based yet accessible exploration of how nutrition impacts various aspects of active populations, from general health to muscle development, exercise recovery, injury prevention, and psychological well-being. Real-world examples and case studies bring difficult concepts to life, equipping you with the knowledge and confidence to support the nutritional needs of active populations throughout your healthcare career. ● NEW! Comparative Case Studies at the end of each chapter challenge students to apply what they’ve learned to varying active populations from endurance athletes to power-training athletes to team sports participants. ● Practical Application Activity boxes provide engaging practice assessing diets, analyzing energy balances, and other professional tasks. ● Chapter-opening Case Studies with discussion questions foster the critical thinking skills essential to real-world application. ● Important Factors to Consider boxes throughout chapters reinforce critical chapter points.

Objev podobné jako ACSM's Nutrition for Exercise Science - ACSM, Dan, PhD, DHC, RD, LD, FACSM Benardot