Search results for "Elements of Data Science"
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Data Science For Dummies
Understand the foundations of data science and modern AI tools with this fully updated beginner's guide Whether you're a business analyst who's never coded, a manager who’s making data-driven decisions, or a career changer eyeing one of the fastest-growing professions, data science skills are vital to success. In the fourth edition of Data Science For Dummies, Lillian Pierson, a fractional CMO and GTM engineer who has trained over two million learners, delivers a hands-on introduction to the key concepts and tools such as AI-enabled shortcuts that make advanced analytics accessible to everyone. This updated edition covers generative AI, prompt engineering, and predictive analytics right alongside evergreen fundamentals like Python programming, statistics, and data wrangling. Brand-new chapters explain practical applications using ChatGPT and Claude, while expanded no-code workflows make AI-powered analysis accessible to readers who have never written a single line of code. Data Science For Dummies walks you through: Foundational skills in Python, statistics, and machine learning for beginners and career changersHands-on generative AI coverage with practical demos using ChatGPT and ClaudeNo-code workflows that show non-programmers how to perform AI-powered data analysis tasksUpdated guidance on ethical AI practices, data regulations, and responsible complianceCareer-focused advice on emerging roles, portfolio building, and industry preparation Perfect for professionals, students, career changers, and business leaders looking for an accessible launching pad into an exciting and rewarding discipline, Data Science For Dummies gives you the foundational skills and modern AI tools you need to extract insights, create visualizations, and make smarter, data-driven decisions.
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R 1,627.00
Data Science
The aim of this book is to provide an internationally respected collection of scientific research methods, technologies and applications in the area of data science. This book can prove useful to the researchers, professors, research students and practitioners as it reports novel research work on challenging topics in the area surrounding data science. In this book, some of the chapters are written in tutorial style concerning machine learning algorithms, data analysis, information design, infographics, relevant applications, etc. The book is structured as follows:• Part I: Data Science: Theory, Concepts, and AlgorithmsThis part comprises five chapters on data Science theory, concepts, techniques and algorithms. • Part II: Data Design and AnalysisThis part comprises five chapters on data design and analysis.• Part III: Applications and New Trends in Data ScienceThis part comprises four chapters on applications and new trends in data science.
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Fundamentals of Data Science
Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science.Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processesReaders will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue.This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge.Features : Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets. Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools. Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice. Information is presented in an accessible way for students, researchers and academicians and professionals.
R 7,580.72
Fundamentals of Data Science
Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science.Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processesReaders will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue.This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge.Features : Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets. Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools. Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice. Information is presented in an accessible way for students, researchers and academicians and professionals.
R 2,879.71
Fundamentals of Data Science
Fundamentals of Data Science: Theory and Practice presents basic and advanced concepts in data science along with real-life applications. The book provides students, researchers and professionals at different levels a good understanding of the concepts of data science, machine learning, data mining and analytics. Users will find the authors’ research experiences and achievements in data science applications, along with in-depth discussions on topics that are essential for data science projects, including pre-processing, that is carried out before applying predictive and descriptive data analysis tasks and proximity measures for numeric, categorical and mixed-type data. The book's authors include a systematic presentation of many predictive and descriptive learning algorithms, including recent developments that have successfully handled large datasets with high accuracy. In addition, a number of descriptive learning tasks are included.
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Data Science Essentials in Python
Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python. Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data. This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data.Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume. Keep this handy quick guide at your side whether you're a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn't want to memorize every function and option. What You Need: You need a decent distribution of Python 3.3 or above that includes at least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. A great distribution that meets the requirements is Anaconda, available for free from www.continuum.io. If you plan to set up your own database servers, you also need MySQL (www.mysql.com) and MongoDB (www.mongodb.com). Both packages are free and run on Windows, Linux, and Mac OS.
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Data Science for Neuroimaging
Data science methods and tools—including programming, data management, visualization, and machine learning—and their application to neuroimaging researchAs neuroimaging turns toward data-intensive discovery, researchers in the field must learn to access, manage, and analyze datasets at unprecedented scales. Concerns about reproducibility and increased rigor in reporting of scientific results also demand higher standards of computational practice. This book offers neuroimaging researchers an introduction to data science, presenting methods, tools, and approaches that facilitate automated, reproducible, and scalable analysis and understanding of data. Through guided, hands-on explorations of openly available neuroimaging datasets, the book explains such elements of data science as programming, data management, visualization, and machine learning, and describes their application to neuroimaging. Readers will come away with broadly relevant data science skills that they can easily translate to their own questions.• Fills the need for an authoritative resource on data science for neuroimaging researchers• Strong emphasis on programming• Provides extensive code examples written in the Python programming language• Draws on openly available neuroimaging datasets for examples• Written entirely in the Jupyter notebook format, so the code examples can be executed, modified, and re-executed as part of the learning process
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Data Science
This textbook provides an easy-to-understand introduction to the mathematical concepts and algorithms at the foundation of data science. It covers essential parts of data organization, descriptive and inferential statistics, probability theory, and machine learning. These topics are presented in a clear and mathematical sound way to help readers gain a deep and fundamental understanding. Numerous application examples based on real data are included. The book is well-suited for lecturers and students at technical universities, and offers a good introduction and overview for people who are new to the subject. Basic mathematical knowledge of calculus and linear algebra is required.
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Data Science
This book covers the topic of data science in a comprehensive manner and synthesizes both fundamental and advanced topics of a research area that has now reached its maturity. The book starts with the basic concepts of data science. It highlights the types of data and their use and importance, followed by a discussion on a wide range of applications of data science and widely used techniques in data science.Key Features• Provides an internationally respected collection of scientific research methods, technologies and applications in the area of data science.• Presents predictive outcomes by applying data science techniques to real-life applications.• Provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods.• Gives the reader a variety of intelligent applications that can be designed using data science and its allied fields.The book is aimed primarily at advanced undergraduates and graduates studying machine learning and data science. Researchers and professionals will also find this book useful.
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Data Science Fundamentals
As part of the best-selling Pocket Primer Series, this book is designed to introduce the reader to the basic concepts of data science using Python 3 and other computer applications. It is intended to be a fast-paced introduction to some basic features of data analytics and also covers statistics, data visualization, linear algebra, and regular expressions. The book includes numerous code samples using Python, NumPy, R, SQL, NoSQL, and Pandas. Companion files with source code and color figures are available. FEATURES:Includes a concise introduction to Python 3 and linear algebraProvides a thorough introduction to data visualization and regular expressionsCovers NumPy, Pandas, R, and SQLIntroduces probability and statistical conceptsFeatures numerous code samples throughout Includes companion files with source code and figures
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Data Science
Tap into the power of data science with this comprehensive resource for non-technical professionals Data Science: The Executive Summary – A Technical Book for Non-Technical Professionals is a comprehensive resource for people in non-engineer roles who want to fully understand data science and analytics concepts. Accomplished data scientist and author Field Cady describes both the "business side" of data science, including what problems it solves and how it fits into an organization, and the technical side, including analytical techniques and key technologies. Data Science: The Executive Summary covers topics like: Assessing whether your organization needs data scientists, and what to look for when hiring themWhen Big Data is the best approach to use for a project, and when it actually ties analysts’ handsCutting edge Artificial Intelligence, as well as classical approaches that work better for many problemsHow many techniques rely on dubious mathematical idealizations, and when you can work around them Perfect for executives who make critical decisions based on data science and analytics, as well as mangers who hire and assess the work of data scientists, Data Science: The Executive Summary also belongs on the bookshelves of salespeople and marketers who need to explain what a data analytics product does. Finally, data scientists themselves will improve their technical work with insights into the goals and constraints of the business situation.
R 2,571.00
Data Science
Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You’ll be able to: Gain the necessary knowledge of different data science techniques to extract value from data. Master the concepts and inner workings of 30 commonly used powerful data science algorithms. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more...
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Discrete Mathematics for Data Science
Discrete Mathematics for Data Science provides an early course in both Data Science and Discrete Mathematics, focusing on how a deeper understanding of the former can unlock a more effective implementation of the latter. Students of Data Science come from a variety of disciplines, with Business, Statistics, Computer Science, Economics, and Psychology among the departments offering courses on the subject. Therefore, for many students, Data Science is considered a means of insight into a particular field of interest, with the study of its underlying discrete mathematics not a primary objective.This book covers the topics of Discrete Mathematical Structures relevant to students of Data Science, offering a relevant and gentle introduction to both the theoretical and practical elements required to be a successful data scientist. The relaxed, accessible style makes it a perfect textbook for undergraduates.Features• Numerous exercises and examples.• Ideal as a textbook for a Discrete Mathematics course for data science and computer science students.• Source code and solutions provided as a supplementary resource.
R 9,336.27
Statistics for Data Science and Analytics
Introductory statistics textbook with a focus on data science topics such as prediction, correlation, and data exploration Statistics for Data Science and Analytics is a comprehensive guide to statistical analysis using Python, presenting important topics useful for data science such as prediction, correlation, and data exploration. The authors provide an introduction to statistical science and big data, as well as an overview of Python data structures and operations. A range of statistical techniques are presented with their implementation in Python, including hypothesis testing, probability, exploratory data analysis, categorical variables, surveys and sampling, A/B testing, and correlation. The text introduces binary classification, a foundational element of machine learning, validation of statistical models by applying them to holdout data, and probability and inference via the easy-to-understand method of resampling and the bootstrap instead of using a myriad of “kitchen sink” formulas. Regression is taught both as a tool for explanation and for prediction. This book is informed by the authors’ experience designing and teaching both introductory statistics and machine learning at Statistics.com. Each chapter includes practical examples, explanations of the underlying concepts, and Python code snippets to help readers apply the techniques themselves. Statistics for Data Science and Analytics includes information on sample topics such as: Int, float, and string data types, numerical operations, manipulating strings, converting data types, and advanced data structures like lists, dictionaries, and setsExperiment design via randomizing, blinding, and before-after pairing, as well as proportions and percents when handling binary dataSpecialized Python packages like numpy, scipy, pandas, scikit-learn and statsmodels—the workhorses of data science—and how to get the most value from themStatistical versus practical significance, random number generators, functions for code reuse, and binomial and normal probability distributions Written by and for data science instructors, Statistics for Data Science and Analytics is an excellent learning resource for data science instructors prescribing a required intro stats course for their programs, as well as other students and professionals seeking to transition to the data science field.
R 3,908.00
Elements of Network Science
This book provides readers with a comprehensive guide to designing rigorous and effective network science tools using the statistical software platforms Stata, R, and Python.Network science offers a means to understand and analyze complex systems that involve various types of relationships. This text bridges the gap between theoretical understanding and practical application, making network science more accessible to a wide range of users. It presents the statistical models pertaining to individual network techniques, followed by empirical applications that use both built-in and user-written packages, and reveals the mathematical and statistical foundations of each model, along with demonstrations involving calculations and step-by-step code implementation. In addition, each chapter is complemented by a case study that illustrates one of the several techniques discussed.The introductory chapter serves as a roadmap for readers, providing an initial understanding of network science and guidance on the required packages, the second chapter focuses on the main concepts related to network properties. The next two chapters present the primary definitions and concepts in network science and various classes of graphs observed in real contexts. The final chapter explores the main social network models, including the family of exponential random graph models. Each chapter includes real-world data applications from the social sciences, using at least one of the platforms Stata, R, and Python, providing a more comprehensive understanding of the availability of network science methods across different software platforms. The underlying computer code and data sets are available online.The book will appeal to graduate students, researchers and data scientists, mainly from the social sciences, who seek theoretical and applied tools to implement network science techniques in their work.
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Data Science Concepts and Techniques with Applications
This textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared to the first edition, is devoted to various techniques and tools applied in data science. Its chapters 4 to 10 detail data pre-processing, classification, clustering, text mining, deep learning, frequent pattern mining, and regression analysis. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main data science programming languages, and shows in a completely new chapter practical data science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different machine learning and data mining tasks. An appendix explaining the basic mathematical concepts of data science completes the book. This textbook is suitable for advanced undergraduate and graduate students as well as for industrial practitioners who carry out research in data science. They both will not only benefit from the comprehensive presentation of important topics, but also from the many application examples and the comprehensive list of further readings, which point to additional publications providing more in-depth research results or provide sources for a more detailed description of related topics. "This book delivers a systematic, carefully thoughtful material on Data Science." from the Foreword by Witold Pedrycz, U Alberta, Canada.
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Data Science
Data Science: A First Introduction focuses on using the R programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference.The text emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. All source code is available online, demonstrating the use of good reproducible project workflows.Based on educational research and active learning principles, the book uses a modern approach to R and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The book will leave readers well-prepared for data science projects.The book is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates in the University of British Columbia’s DSCI100: Introduction to Data Science course.
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R 611.00
The Elements of Science
For anyone working in the sciences, from aspiring research students to seasoned professionals, this book sets out every step of the scientific method in a pithy, easy-to-read, practical guide. Covering every component of this long-established process, it organizes them into a unique three-part workflow: pre-experiment, at the bench, and post-experiment. While ostensibly taught from a young age, many steps of the scientific method are ignored or skipped in contemporary research. Elements of Science walks you through each step in turn and underpins them with the pressing ethical concerns in each case. Following these steps and understanding their role in your processes is the surest route to making sure that your research is rigorous and reproducible. The book’s first part covers everything that you need to consider when forming a hypothesis and formulating an experiment, from initial observations to preparatory research. The second part deals with the bench work – conducting the experiment, recording the data, analysing the results and drawing your conclusions. Finally, the book rounds out a vital but often overlooked aspect of scientific impact: how to report your results clearly and compellingly. To avoid flawed design, interpret data with confidence, and conduct ‘good science’ reliably, the scientific method is a crucial tool in every scientist’s kit.
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Essentials of Data Science and Analytics
This text provides a comprehensive overview of Data Science. With the continued advancement in storage and computing technologies, data science has emerged as one of the most desired fields in driving business decisions. Data science employs techniques and methods from many other fields, such as statistics, mathematics, computer science, and information science. Besides the methods and theories drawn from several fields, data science uses visualization techniques using specially designed big data software and statistical programming language, such as R programming, and Python. Data Science has wide applications in the areas of Machine Learning (ML) and Artificial Intelligence (AI). The book is divided into four different areas divided into different chapters. These chapters explain the core of Data Science. Part I of the book introduces the field of Data Science, different disciplines it comprises of, and the scope with future outlook and career prospects. This section also explains analytics, business analytics, and business intelligence and their similarities and differences with Data Science. Since the data is at the core of Data science, Part II is devoted to explaining the data, big data, and other features of data. One full chapter is devoted to Data Analysis, creating visuals, pivot table, and other applications using Excel with office 365. Part III explains the statistics behind Data Science. It uses several chapters to explain the statistics and its importance, numerical and data visualization tools and methods, probability, and probability distribution applications in Data Science. Other chapters in the Part III are Sampling, Estimation, and Hypothesis Testing. All these are integral part of Data Science applications. Part IV of the book provides the basics of Machine Learning (ML) and R-statistical software. Data Science has wide applications in the areas of Machine Learning (ML) and Artificial Intelligence (AI) and R-statistical software is widely used by data science professionals. The book also outlines a brief history, the body of knowledge, skills and education requirements for Data Scientist and data science professionals. Some statistics on job growth and prospects are also summarized. A career in data science is ranked at the third best job in America for 2020 by Glassdoor, and was ranked the number one best job from 2016-2019.[29]
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Introduction To Data Science
The book systematically introduces the basic contents of data science, including data preprocessing and basic methods of data analysis, handling special problems (e.g. text analysis), deep learning, and distributed systems.In addition to systematically introducing the basic content of data science from a theoretical point of view, the book also provides a large number of data analysis practice cases.
R 2,125.00
Data Science Essentials For Dummies
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.
R 388.00
Data Science and Big Data Analytics in Smart Environments
Most applications generate large datasets, like social networking and social influence programs, smart cities applications, smart house environments, Cloud applications, public web sites, scientific experiments and simulations, data warehouse, monitoring platforms, and e-government services. Data grows rapidly, since applications produce continuously increasing volumes of both unstructured and structured data. Large-scale interconnected systems aim to aggregate and efficiently exploit the power of widely distributed resources. In this context, major solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance and to create a smart environment. The impact on data processing, transfer and storage is the need to re-evaluate the approaches and solutions to better answer the user needs. A variety of solutions for specific applications and platforms exist so a thorough and systematic analysis of existing solutions for data science, data analytics, methods and algorithms used in Big Data processing and storage environments is significant in designing and implementing a smart environment.Fundamental issues pertaining to smart environments (smart cities, ambient assisted leaving, smart houses, green houses, cyber physical systems, etc.) are reviewed. Most of the current efforts still do not adequately address the heterogeneity of different distributed systems, the interoperability between them, and the systems resilience. This book will primarily encompass practical approaches that promote research in all aspects of data processing, data analytics, data processing in different type of systems: Cluster Computing, Grid Computing, Peer-to-Peer, Cloud/Edge/Fog Computing, all involving elements of heterogeneity, having a large variety of tools and software to manage them. The main role of resource management techniques in this domain is to create the suitable frameworks for development of applications and deployment in smart environments, with respect to high performance. The book focuses on topics covering algorithms, architectures, management models, high performance computing techniques and large-scale distributed systems.
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Beginner's Guide to Data Science
This book discusses the principles and practical applications of data science, addressing key topics including data wrangling, statistics, machine learning, data visualization, natural language processing and time series analysis. Detailed investigations of techniques used in the implementation of recommendation engines and the proper selection of metrics for distance-based analysis are also covered.Utilizing numerous comprehensive code examples, figures, and tables to help clarify and illuminate essential data science topics, the authors provide an extensive treatment and analysis of real-world questions, focusing especially on the task of determining and assessing answers to these questions as expeditiously and precisely as possible. This book addresses the challenges related to uncovering the actionable insights in “big data,” leveraging database and data collection tools such as web scraping and text identification.This book is organized as 11 chapters, structured as independent treatments of the following crucial data science topics:Data gathering and acquisition techniques including data creationManaging, transforming, and organizing data to ultimately package the information into an accessible format ready for analysisFundamentals of descriptive statistics intended to summarize and aggregate data into a few concise but meaningful measurementsInferential statistics that allow us to infer (or generalize) trends about the larger population based only on the sample portion collected and recordedMetrics that measure some quantity such as distance, similarity, or error and which are especially useful when comparing one or more data observationsRecommendation engines representing a set of algorithms designed to predict (or recommend) a particular product, service, or other item of interest a user or customer wishes to buy or utilize in some mannerMachine learning implementations and associated algorithms, comprising core data science technologies with many practical applications, especially predictive analyticsNatural Language Processing, which expedites the parsing and comprehension of written and spoken language in an effective and accurate mannerTime series analysis, techniques to examine and generate forecasts about the progress and evolution of data over timeData science provides the methodology and tools to accurately interpret an increasing volume of incoming information in order to discern patterns, evaluate trends, and make the right decisions. The results of data science analysis provide real world answers to real world questions. Professionals working on data science and business intelligence projects as well as advanced-level students and researchers focused on data science, computer science, business and mathematics programs will benefit from this book.
R 2,381.00
Data Science
Data Science: A First Introduction focuses on using the R programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference.The text emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. All source code is available online, demonstrating the use of good reproducible project workflows.Based on educational research and active learning principles, the book uses a modern approach to R and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The book will leave readers well-prepared for data science projects.The book is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates in the University of British Columbia’s DSCI100: Introduction to Data Science course.
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R 610.00
R 1,213.00
Introduction to Data Science
Introduction to Data Science: Statistics and Prediction Algorithms Through Case Studies teaches data science as a way of thinking statistically, not just as a collection of computational tools. Building on the topics covered in Introduction to Data Science: Data Wrangling and Visualization with R, this book is designed for students with some programming experience and basic mathematical maturity, this book builds the foundations of probability, statistical inference, regression, high-dimensional data analysis, and machine learning through real data examples and reproducible R code. It is suitable for one-semester course in advanced data science.The book shows how to reason about variability, uncertainty, prediction error, model assumptions, and validation. Through case studies involving polling, genetics, baseball, recommendation systems, image classification, and other modern datasets, readers learn how to connect probability models to data, summarize complex information, quantify uncertainty, fit and interpret models, evaluate prediction algorithms, and understand the statistical ideas behind machine learning. Each chapter is designed to support classroom teaching, self-study, and hands-on analysis, with exercises and companion web materials available through the book website.Key Features:Includes base R, data.table, and tidyverse code.Focuses on the statistical and probabilistic foundations of machine learning.Contains real-world case studies.Rafael A. Irizarry is Professor and Chair of the Department of Data Science at Dana-Farber Cancer Institute and Professor of Applied Statistics at Harvard. His research focuses on Genomics and he has taught several Data Science courses.
R 6,960.00
Dive Into Data Science
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.
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