data science bookcamp leonard apeltsin

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

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

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

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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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Discover the fundamentals of data science and develop the skills you need for achieving success in this important sector.

Objev podobné jako Confident Data Science - Adam Ross Nelson

We are in an age of big data where all of our everyday interactions and transactions generate data. Much of this data is spatial – it is collected some-where – and identifying analytical insight from trends and patterns in these increasing rich digital footprints presents a number of challenges.Whilst other books describe different flavours of Data Analytics in R and other programming languages, there are none that consider Spatial Data (i.e. the location attached to data), or that consider issues of inference, linking Big Data, Geography, GIS, Mapping and Spatial Analytics. This is a ‘learning by doing’ textbook, building on the previous book by the same authors, An Introduction to R for Spatial Analysis and Mapping. It details the theoretical issues in analyses of Big Spatial Data and developing practical skills in the reader for addressing these with confidence.

Objev podobné jako Geographical Data Science and Spatial Data Analysis - Chris Brunsdon, Lex Comber

With this updated second edition, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.

Objev podobné jako Data Science from Scratch - Joel Grus

Prepares students for Microsoft Exam 70-774-and helps them demonstrate real-world mastery of performing key data science activities with Azure Machine Learning services. Designed for experienced IT students ready to advance their status, Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the MCSA level.

Objev podobné jako Exam Ref 70-774 Perform Cloud Data Science with Azure Machine Learning - Ginger Grant, Tamanaco Francisquez, Pau Sempere, Paco Gonzalez, Julio Granado

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

Very Short Introductions: Brilliant, Sharp, InspiringSocial science is the study of human behaviour. It offers the tools to understand and explain people''s choices and actions, and how they live together in communities. With insights from social science, organisations and individuals may be persuaded to change their behaviour, making a difference in addressing societal challenges, from climate change to fighting pandemics and alleviating poverty. Social science can offer us the answers to key questions, such as why do some people gamble, eat unhealthy foods, or hold racist beliefs? How do families allocate household tasks, raise children effectively, or manage grief? How do companies weigh profit against environment sustainability, decide to invest in innovation, or adopt policies to address unequal pay between women and men? Why do countries sign international treaties, commit human rights atrocities, or transition from authoritarianism to democracy? Taking an interdisciplinary approach, this Very Short Introduction offers an accessible overview of social science, explaining how methods and theory from different disciplines can be applied and combined to address major global challenges. It aims to equip students, scholars, and practitioners to analyse, interpret, and undertake social science research. Drawing upon inspiring examples, it shows how social scientists can have real-world ... Unknown localization key: "more"

Objev podobné jako Social Science - Alexander Betts

A tidyverse edition of the acclaimed textbook on data analysis and statistics for the social sciences and allied fieldsQuantitative analysis is an essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it. Quantitative Social Science is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, including business, economics, education, political science, psychology, sociology, public policy, and data science. Proven in classrooms around the world, this one-of-a-kind textbook engages directly with empirical analysis, showing students how to analyze and interpret data using the tidyverse family of R packages. Data sets taken directly from leading quantitative social science research illustrate how to use data analysis to answer important questions about society and human behavior. Emphasizes hands-on learning, not paper-and-pencil statisticsIncludes data sets from actual research for students to test their skills onCovers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical toolsFeatures a wealth of supplementary exercises, including additional data analysis exercises and programming exercisesOffers a solid foundation for further studyComes with additional course materials online, including notes, sample code, exercises ... Unknown localization key: "more"

Objev podobné jako Quantitative Social Science - Kosuke Imai, Nora Webb Williams

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

“This text should be required reading for everyone in contemporary business.” --Peter Woodhull, CEO, Modus21 “The one book that clearly describes and links Big Data concepts to business utility.” --Dr. Christopher Starr, PhD “Simply, this is the best Big Data book on the market!” --Sam Rostam, Cascadian IT Group “...one of the most contemporary approaches I’ve seen to Big Data fundamentals...” --Joshua M. Davis, PhD The Definitive Plain-English Guide to Big Data for Business and Technology Professionals Big Data Fundamentals provides a pragmatic, no-nonsense introduction to Big Data. Best-selling IT author Thomas Erl and his team clearly explain key Big Data concepts, theory and terminology, as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams. The authors begin by explaining how Big Data can propel an organization forward by solving a spectrum of previously intractable business problems. Next, they demystify key analysis techniques and technologies and show how a Big Data solution environment can be built and integrated to offer competitive advantages. Discovering Big Data’s fundamental concepts and what makes it different from previous forms of data analysis and data science Understanding the business motivations and drivers behind Big Data adoption, ... Unknown localization key: "more"

Objev podobné jako Big Data Fundamentals - Thomas Erl, Paul Buhler, Wajid Khattak

An Absolute Beginner''s Guide to Learning Data Analysis Using Python, a Demanding Skill for TodayKey FeaturesHands-on learning experience of Python''s fundamentals.Covers various examples of how to code end-to-end data analysis with easy illustrations.An excellent starting point to begin your data analysis journey with Python programming.DescriptionIn an effort to provide content for beginners, the book Data Analysis with Python provides a concrete first step in learning data analysis. Written by a data professional with decades of experience, this book provides a solid foundation in data analysis and numerous data science processes. In doing so, readers become familiar with common Python libraries and straightforward scripting techniques.Python and many of its well-known data analysis libraries, such as Pandas, NumPy, and Matplotlib, are utilized throughout this book to carry out various operations typical of data analysis projects.Following an introduction to Python programming fundamentals, the book combines well-known numerical calculation and statistical libraries to demonstrate the fundamentals of programming, accompanied by many practical examples. This book provides a solid groundwork for data analysis by teaching Python programming as well as Python''s built-in data analysis capabilities.What you will learnLearn the fundamentals of core Python programming for data analysis.Master Python''s most demanding data analysis and visualization libraries, ... Unknown localization key: "more"

Objev podobné jako Data Analysis with Python - Rituraj Dixit

Data science fundamentals, using clear, expert explanations and comic illustrations to spark interest and enthusiasm in the next generation of computer scientists!Data, Big and Small delves into the details behind computer science data and information, which help people and organisations to make decisions. Learn how and why data are created, stored and shared. Find out how we make sense of information through graphs, maps and pictures, and understand how machines learn from the data that we feed them. The end of the book fast-forwards to the future of computer science, data and robotics, and considers what it might mean to live in a ''smarter'' world.Contents: What are data? / Data, information and knowledge / Data are vital / Where do data come from? / Getting the format right / Storing all those data / The value is in the processing / Picture it! / Big data / Getting the most out of data / Keeping data secure / A trip into the future of data ... / Pioneer portraits / Further information / Glossary / Quiz yourself! / IndexThe Click, Connect, Compute series untangles the computer science web and teaches children about the essentials of computer software, hardware and digital ... Unknown localization key: "more"

Objev podobné jako Click, Connect, Compute: Data, Big and Small - Dr Dharini Balasubramaniam

An engaging introduction to data science that emphasizes critical thinking over statistical techniquesAn introduction to data science or statistics shouldn’t involve proving complex theorems or memorizing obscure terms and formulas, but that is exactly what most introductory quantitative textbooks emphasize. In contrast, Thinking Clearly with Data focuses, first and foremost, on critical thinking and conceptual understanding in order to teach students how to be better consumers and analysts of the kinds of quantitative information and arguments that they will encounter throughout their lives. Among much else, the book teaches how to assess whether an observed relationship in data reflects a genuine relationship in the world and, if so, whether it is causal; how to make the most informative comparisons for answering questions; what questions to ask others who are making arguments using quantitative evidence; which statistics are particularly informative or misleading; how quantitative evidence should and shouldn’t influence decision-making; and how to make better decisions by using moral values as well as data. Filled with real-world examples, the book shows how its thinking tools apply to problems in a wide variety of subjects, including elections, civil conflict, crime, terrorism, financial crises, health care, sports, music, and space travel. Above all ... Unknown localization key: "more"

Objev podobné jako Thinking Clearly with Data - Anthony Fowler, Ethan Bueno de Mesquita

This is the first end-to-end, full-color guide to telling powerful, actionable data stories using Tableau, the world’s #1 visualization software. Renowned expert Lindy Ryan shows you how to communicate the full business implications of your data analyses by combining Tableau’s remarkable capabilities with a deep understanding of storytelling and design. Each chapter illuminates key aspects of design practice and data visualization, and guides you step-by-step through applying them in Tableau. Ryan demonstrates how “data stories” resemble and differ from traditional storytelling, and helps you use Tableau to analyze, visualize, and communicate insights that are meaningful to any stakeholder, in any medium. Information Visualization in Tableau presents exercises that give you hands-on practice with the most up-to-date capabilities available through Tableau 10 and the full Tableau software ecosystem. Ryan’s classroom-tested exercises won’t just help you master the software: they’ll show you to craft data stories that inspire action. Coverage includes: The visual data storytelling paradigm: moving beyond static charts to powerful visualizations that combine narrative with interactive graphics How to think like a data scientist, a storyteller, and a designer -- all in the same project Data storytelling case studies: the good, the bad, and the ugly Shaping data stories: blending ... Unknown localization key: "more"

Objev podobné jako Visual Data Storytelling with Tableau - Lindy Ryan

Data science fundamentals, using clear, expert explanations and comic illustrations to spark interest and enthusiasm in the next generation of computer scientists!Data, Big and Small delves into the details behind computer science data and information, which help people and organisations to make decisions. Learn how and why data are created, stored and shared. Find out how we make sense of information through graphs, maps and pictures, and understand how machines learn from the data that we feed them. The end of the book fast-forwards to the future of computer science, data and robotics, and considers what it might mean to live in a ''smarter'' world.Contents: What are data? / Data, information and knowledge / Data are vital / Where do data come from? / Getting the format right / Storing all those data / The value is in the processing / Picture it! / Big data / Getting the most out of data / Keeping data secure / A trip into the future of data ... / Pioneer portraits / Further information / Glossary / Quiz yourself! / IndexThe Click, Connect, Compute series untangles the computer science web and teaches children about the essentials of computer software, hardware and digital ... Unknown localization key: "more"

Objev podobné jako Click, Connect, Compute: Data, Big and Small - Dr Dharini Balasubramaniam

A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming A General Introduction to Data Analytics is an essential guide to understand and use data analytics. This book is written using easy-to-understand terms and does not require familiarity with statistics or programming. The authors—noted experts in the field—highlight an explanation of the intuition behind the basic data analytics techniques. The text also contains exercises and illustrative examples. Thought to be easily accessible to non-experts, the book provides motivation to the necessity of analyzing data. It explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. The book also explores predictive tasks, be them classification or regression. Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems. The learning resources offer: A guide to the reasoning behind data mining techniquesA unique illustrative example that extends throughout all the chaptersExercises at the end of each chapter and larger projects at the end of each of the text’s two main parts Together with these learning resources, the book can be used in ... Unknown localization key: "more"

Objev podobné jako A General Introduction to Data Analytics - Tomáš Horváth, Andre Carvalho, Joao Moreira

Jump-start your career as a data scientist—learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that’s dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls. You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data. This guide for data scientists differs from other instructional guides on the subject. It doesn’t cover SQL broadly. Instead, you’ll learn the subset of SQL skills that data analysts and data scientists use frequently. You’ll also gain practical advice and direction on ... Unknown localization key: "more"

Objev podobné jako SQL for Data Scientists - Renee M. P. Teate

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher-quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

Objev podobné jako Practical Statistics for Data Scientists - Peter Bruce, Andrew Bruce, Peter Gedeck

Data is an integral part of our lives. It helps us to unlock hidden mysteries and even predict the future. The Expanding World of Data uncovers the world of data in a way that is accessible, engaging and thought-provoking, using colourful artwork, illustrations, comic strips, ''Speak like a Scientist'' and data hero sections. This book is the perfect resource for those curious minds who want to know more about data, what it is and what it does. Data can even help us to make the planet a better place.This title is one of an exciting series from Oxford, giving accessible introductions to the ideas, facts, and vocabulary behind an absorbing range of subjects. Meticulously researched and written by experts in their fields, curious young readers will quickly get to grips with the basic principles and terminology of each subject.Author Tom Jackson has been a writer for 25 years, written about 200 books and specializes in science and technology. Consultant Dr Bran Knowles is a Senior Lecturer in the Data Science Institute at Lancaster University where she leads the Data and Society theme.The Expanding World of Data is part of a wider collectible set. If you love this title, why not ... Unknown localization key: "more"

Objev podobné jako Very Short Introductions for Curious Young Minds: The Expanding World of Data - Tom Jackson

Stochastic Modelling of Big Data in Finance provides a rigorous overview and exploration of stochastic modelling of big data in finance (BDF). The book describes various stochastic models, including multivariate models, to deal with big data in finance. This includes data in high-frequency and algorithmic trading, specifically in limit order books (LOB), and shows how those models can be applied to different datasets to describe the dynamics of LOB, and to figure out which model is the best with respect to a specific data set. The results of the book may be used to also solve acquisition, liquidation and market making problems, and other optimization problems in finance.FeaturesSelf-contained book suitable for graduate students and post-doctoral fellows in financial mathematics and data science, as well as for practitioners working in the financial industry who deal with big dataAll results are presented visually to aid in understanding of conceptsDr. Anatoliy Swishchuk is a Professor in Mathematical Finance at the Department of Mathematics and Statistics, University of Calgary, Calgary, AB, Canada. He got his B.Sc. and M.Sc. degrees from Kyiv State University, Kyiv, Ukraine. He earned two doctorate degrees in Mathematics and Physics (PhD and DSc) from the prestigious National Academy of Sciences ... Unknown localization key: "more"

Objev podobné jako Stochastic Modelling of Big Data in Finance - Anatoliy Swishchuk

We are drowning in data but are starved for knowledge. Data Analytics is the discipline of extracting actionable insights by structuring, processing, analysing and visualising data using methods and software tools. Hence, we gain knowledge by understanding the data. A roadmap to achieve this is encapsulated in the knowledge discovery in databases (KDD) process. Databases help us store data in a structured way. The structure query language (SQL) allows us to gain first insights about business opportunities. Visualising the data using business intelligence tools and data science languages deepens our understanding of the key performance indicators and business characteristics. This can be used to create relevant classification and prediction models; for instance, to provide customers with the appropriate products or predict the eruption time of geysers. Machine learning algorithms help us in this endeavour. Moreover, we can create new classes using unsupervised learning methods, which can be used to define new market segments or group customers with similar characteristics. Finally, artificial intelligence allows us to reason under uncertainty and find optimal solutions for business challenges. All these topics are covered in this book with a hands-on process, which means we use numerous examples to introduce the concepts and several software ... Unknown localization key: "more"

Objev podobné jako Data Analytics for Business - Wolfgang Garn

Naučný komiks představuje život a dílo Leonarda da Vinciho, renesančního génia. Forma komiksu propojená s animovaným seriálem zpřístupňuje téma dětem od 8 let. Kniha má 48 stran v českém jazyce a rozměry 21 x 29 cm.

• Naučný obsah podaný zábavnou komiksovou formou
• Propojení s populárním animovaným seriálem České televize
• Vhodné pro děti od 8 let s českým jazykem
• Kvalitní rozměry 21x29 cm pro pohodlné čtení

Objev podobné jako Byl jednou jeden člověk - Leonardo da Vinci

Data Game: The Story of Liverpool FC''s Analytics Revolution explores the lesser-known story of how Liverpool rose to greatness in the 21st century with the help of big data. The Anfield institution is an industry leader in the field of data science, but little is known about how Liverpool''s relationship with numbers began, and how the marriage of data and football helped to deliver wins on the pitch by impacting tactics and recruitment. Upon their takeover, Fenway Sports Group set out to transform Liverpool into a data-driven organisation, but there is a story behind why that vision took around a decade to become a reality. From errors in the transfer market, to suboptimal playing styles, to conflicting egos, Liverpool jumped many hurdles before achieving their ambitions under Jürgen Klopp, with the German surrounded by unsung heroes who shunned the limelight. This is the tale of how Liverpool gained an edge over their wealthier rivals by getting smart.

Objev podobné jako Data Game - Josh Williams