Foundations Of Data Science
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Foundations Of Data Science
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Author : Avrim Blum
language : en
Publisher: Cambridge University Press
Release Date : 2020-01-23
Foundations Of Data Science written by Avrim Blum and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-23 with Computers categories.
Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.
Statistical Foundations Of Data Science
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Author : Jianqing Fan
language : en
Publisher: CRC Press
Release Date : 2020-09-20
Statistical Foundations Of Data Science written by Jianqing Fan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-20 with Mathematics categories.
Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.
Foundations Of Statistics For Data Scientists
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Author : Alan Agresti
language : en
Publisher: CRC Press
Release Date : 2021-11-29
Foundations Of Statistics For Data Scientists written by Alan Agresti and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-29 with Business & Economics categories.
Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modeling. The book assumes knowledge of basic calculus, so the presentation can focus on "why it works" as well as "how to do it." Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python. Key Features: Shows the elements of statistical science that are important for students who plan to become data scientists. Includes Bayesian and regularized fitting of models (e.g., showing an example using the lasso), classification and clustering, and implementing methods with modern software (R and Python). Contains nearly 500 exercises. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists, such as Bayesian inference, generalized linear models for non-normal responses (e.g., logistic regression and Poisson loglinear models), and regularized model fitting. The nearly 500 exercises are grouped into "Data Analysis and Applications" and "Methods and Concepts." Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website (http://stat4ds.rwth-aachen.de/) has expanded R, Python, and Matlab appendices and all data sets from the examples and exercises.
Mathematical Foundations Of Data Science Using R
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Author : Frank Emmert-Streib
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2022-10-24
Mathematical Foundations Of Data Science Using R written by Frank Emmert-Streib and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-24 with Computers categories.
The aim of the book is to help students become data scientists. Since this requires a series of courses over a considerable period of time, the book intends to accompany students from the beginning to an advanced understanding of the knowledge and skills that define a modern data scientist. The book presents a comprehensive overview of the mathematical foundations of the programming language R and of its applications to data science.
Fundamentals Of Data Science
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Author : Samuel Burns
language : en
Publisher:
Release Date : 2019-09-17
Fundamentals Of Data Science written by Samuel Burns and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-17 with Big data categories.
"This book is for students or anyone, with limited or no prior programming, statistics, and data analytics knowledge. This short guide is ideal for absolute beginners, or anyone who wants to acquire a basic working knowledge of data science. It is an excellent guide if you want to learn about the principals of data science from scratch, in just a few hours. The author discussed everything that you need to know about data science. First, you are guided to learn the meaning of data science. The history of data science has been discussed to help you know how people came to realize that data is a rich source of knowledge and intelligence. The theories underlying data science have been discussed. Examples include decision and estimation theories. The author discussed the various machine learning algorithms used in data science and the various steps one has to undergo when performing data science tasks, from data collection to data presentation and visualization. The author helps you to know the various ways through which you can apply data science in your business for increased profits. A simple language has been used to ensure ease of understanding, especially for beginners." --
Foundations Of Data Science For Engineering Problem Solving
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Author : Parikshit Narendra Mahalle
language : en
Publisher: Springer Nature
Release Date : 2021-08-21
Foundations Of Data Science For Engineering Problem Solving written by Parikshit Narendra Mahalle and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-21 with Technology & Engineering categories.
This book is one-stop shop which offers essential information one must know and can implement in real-time business expansions to solve engineering problems in various disciplines. It will also help us to make future predictions and decisions using AI algorithms for engineering problems. Machine learning and optimizing techniques provide strong insights into novice users. In the era of big data, there is a need to deal with data science problems in multidisciplinary perspective. In the real world, data comes from various use cases, and there is a need of source specific data science models. Information is drawn from various platforms, channels, and sectors including web-based media, online business locales, medical services studies, and Internet. To understand the trends in the market, data science can take us through various scenarios. It takes help of artificial intelligence and machine learning techniques to design and optimize the algorithms. Big data modelling and visualization techniques of collected data play a vital role in the field of data science. This book targets the researchers from areas of artificial intelligence, machine learning, data science and big data analytics to look for new techniques in business analytics and applications of artificial intelligence in recent businesses.
Foundations Of Data Science
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Author : Blum A.
language : en
Publisher:
Release Date : 2023-08-20
Foundations Of Data Science written by Blum A. and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-20 with categories.
Dive headfirst into the vast ocean of knowledge as you navigate through the meticulously crafted pages of this treasure trove. From unraveling complex algorithms to deciphering the language of patterns, every chapter is a symphony of discovery, composed to guide both novice learners and seasoned data enthusiasts. Crafted with a rare blend of academic rigor and approachable prose, this literary masterpiece transcends mere education. It offers a bridge between theory and real-world application, empowering you to harness the true potential of data to fuel innovation and ignite change. Whether you're a budding data scientist, a curious analyst, or an inquisitive mind, the revelation of these foundational principles will undoubtedly kindle your intellectual fire. Prepare to be captivated by the elegant dance of statistics and logic, where insights gleaned from data take on a life of their own. As you flip through the pages, the authors' unparalleled expertise unfolds like a map, guiding you towards the heart of data science's most intricate enigmas. With a narrative that reads like an adventure, this tome is not just a book; it's a portal to a world where data is the ultimate protagonist, and discovery is the grand finale. The authors' passion leaps from the pages, sparking curiosity and challenging you to engage with concepts that are at once timeless and transformative. Elevate your understanding, broaden your horizons, and tap into the boundless potential of data with this magnum opus. Whether you're an eager learner, a seasoned practitioner, or simply someone drawn to the allure of data's hidden stories, this is your invitation to delve into the Foundations of Data Science.
Foundations Of Data Science
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Author : Dr. K. SivaRamaKrishna, Dr. N. Bharathi, Dr. Rajesh Kumar Verma, Dr. J. Krishna
language : en
Publisher: BR Publications
Release Date : 2025-09-16
Foundations Of Data Science written by Dr. K. SivaRamaKrishna, Dr. N. Bharathi, Dr. Rajesh Kumar Verma, Dr. J. Krishna and has been published by BR Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-16 with Computers categories.
Foundations of Data Science provides the essential principles, tools, and techniques used to collect, process, analyze, and interpret data for informed decision-making. It integrates concepts from statistics, mathematics, and computer science to understand data patterns and derive meaningful insights. The course typically covers topics such as data collection and cleaning, exploratory data analysis, probability, statistical inference, data visualization, and basic machine learning techniques. It lays the groundwork for advanced studies and applications in fields like artificial intelligence, business analytics, and big data technologies, helping learners develop strong analytical and problem-solving skills.
Foundations Of Data Science
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Author : Dr. E. Manigandan, Dr. Sajja Suneel, Prof. Smitha G.V., Dr. S. Karthick
language : en
Publisher: BR Publications
Release Date : 2025-12-03
Foundations Of Data Science written by Dr. E. Manigandan, Dr. Sajja Suneel, Prof. Smitha G.V., Dr. S. Karthick and has been published by BR Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-03 with Computers categories.
Foundations of Data Science introduces the core concepts and tools used to extract knowledge from data. It covers data collection, cleaning, and preprocessing techniques. Statistical methods provide a basis for data analysis and interpretation. Data visualization helps communicate insights effectively. The subject introduces basic machine learning concepts and algorithms. Programming tools and libraries are used for data manipulation. Ethical issues such as data privacy and bias are discussed. Real-world applications demonstrate data-driven decision making. Overall, it provides a strong base for advanced studies in data science and analytics.
Mathematical Foundations For Data Analysis
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Author : Jeff M. Phillips
language : en
Publisher: Springer
Release Date : 2021-04-17
Mathematical Foundations For Data Analysis written by Jeff M. Phillips and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-17 with Mathematics categories.
This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques needed for modern data analysis. In particular, this book was designed and written as preparation for students planning to take rigorous Machine Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis. It also surveys basic techniques in supervised (regression and classification) and unsupervised learning (dimensionality reduction and clustering) through an accessible, simplified presentation. Students are recommended to have some background in calculus, probability, and linear algebra. Some familiarity with programming and algorithms is useful to understand advanced topics on computational techniques.