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Regression Models for Data Science in R: Statistical inference for Data Science

Автор: Limpopo5 от 2022-02-06, 07:18:58
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Regression Models for Data Science in R: Statistical inference for Data ScienceНазвание: Regression Models for Data Science in R: Statistical inference for Data Science
Автор: Yassine Mousaif
Издательство: Independently published
Год: 2022
Страниц: 132
Язык: английский
Формат: pdf, epub
Размер: 10.2 MB

What's Special about this Book: The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming. The student should have a basic understanding of statistical inference such as contained in "Statistical inference for Data Science". The book gives a rigorous treatment of the elementary concepts of regression models from a practical perspective. After reading the book and watching the associated videos, students will be able to perform multivariable regression models and understand their interpretations. Regression models are the workhorse of Data Science. They are the most well described, practical and theoretically understood models in statistics. A data scientist well versed in regression models will be able to solve and incredible array of problems. Perhaps the key insight for regression models is that they produce highly interpretable model fits. This is unlike Machine Learning algorithms, which often sacrifice interpretability for improved prediction performance or automation.

Advances in Data Science

Автор: Limpopo5 от 2022-01-25, 05:55:47
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Advances in Data ScienceНазвание: Advances in Data Science
Автор: Ilke Demir, Yifei Lou, Xu Wang
Издательство: Springer
Год: 2021
Страниц: 374
Язык: английский
Формат: pdf (true)
Размер: 11.2 MB

This volume highlights recent advances in Data Science, including image processing and enhancement on large data, shape analysis and geometry processing in 2D/3D, exploration and understanding of neural networks, and extensions to atypical data types such as social and biological signals. Edge enhancement is an essential problem in image processing, computer graphics, computer vision. It aims to make image edges salient so that users can better visualize images and videos. In this study, we mainly focus on edge sharpening, a subset of edge enhancement that can be applied to many important problems, including image super-resolution (SR), deblurring, etc.

Thinking Clearly with Data: A Guide to Quantitative Reasoning and Analysis

Автор: Limpopo5 от 2022-01-22, 21:24:01
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Thinking Clearly with Data: A Guide to Quantitative Reasoning and AnalysisНазвание: Thinking Clearly with data: A Guide to Quantitative Reasoning and Analysis
Автор: Ethan Bueno de Mesquita, Anthony Fowler
Издательство: Princeton University Press
Год: 2021
Страниц: 400
Язык: английский
Формат: pdf, epub
Размер: 10.0 MB

An engaging introduction to Data Science that emphasizes critical thinking over statistical techniques. An 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.

Business Analytics: Data Science for Business Problems

Автор: Limpopo5 от 2022-01-05, 05:48:59
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Business Analytics: Data Science for Business ProblemsНазвание: Business Analytics: Data Science for Business Problems
Автор: Walter R. Paczkowski
Издательство: Springer
Год: 2022
Страниц: 416
Язык: английский
Формат: pdf (true)
Размер: 14.0 MB

Practical in orientation, the volume offers illustrative case studies throughout and examples using Python in the context of Jupyter notebooks. Covered topics include demand measurement and forecasting, predictive modeling, pricing analytics, customer satisfaction assessment, market and advertising research, and new product development and research. This volume will be useful to business data analysts, data scientists, and market research professionals, as well as aspiring practitioners in business data analytics. It can also be used in colleges and universities offering courses and certifications in business data analytics, Data Science, and market research. In Machine Learning, the independent variables are called features. In regression analysis, an example of supervised learning, the goal is to estimate the unknown parameters based on the features to give the best estimate of the target. Since unsupervised learning has neither a target nor a set of parameters, the goal is to find clusters or groups of features using algorithms.

Practical Data Science for Information Professionals

Автор: Limpopo5 от 2022-01-05, 05:46:25
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Practical Data Science for Information ProfessionalsНазвание: Practical Data Science for Information Professionals
Автор: David Stuart
Издательство: Facet Publishing
Год: 2020
Страниц: 201
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

The growing importance of Data Science, and the increasing role of information professionals in the management and use of data, are brought together in Practical Data Science for Information Professionals to provide a practical introduction specifically designed for information professionals. This book is not designed as an introduction to programming; it is an introduction to Data Science, for which programming is an extremely useful skill. As can be seen from the examples throughout the book, quite a lot can be achieved with relatively little programming knowledge by building on the work of others. All that is required is an understanding of a few basic concepts (variables, functions, loops). These are briefly covered in the Appendix, which gives simple examples for R and Python. R and Python are only two of the many hundreds of computer languages available. This book uses Python and R: Python because of its versatility and widespread use, and R because of the statistical packages that have been built for it. R and Python are very powerful, extensive languages with active sets of users, and the web is filled with tutorials for them.

Event- and Data-Centric Enterprise Risk-Adjusted Return Management: A Banking Practitioner’s Handbook

Автор: Limpopo5 от 2022-01-04, 08:41:51
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Event- and Data-Centric Enterprise Risk-Adjusted Return Management: A Banking Practitioner’s HandbookНазвание: Event- and Data-Centric Enterprise Risk-Adjusted Return Management: A Banking Practitioner’s Handbook
Автор: Kannan Subramanian R., Dr. Sudheesh Kumar Kattumannil
Издательство: Apress
Год: 2022
Страниц: 1112
Язык: английский
Формат: pdf (true)
Размер: 72.2 MB

The book provides guidance on the underlying knowledge areas of banking, enterprise risk management, enterprise architecture, technology, event management, processes, and Data Science. The first part of the book explains the current state of banking architecture and its limitations. After defining a target model, it explains an approach to determine the "gap" and the second part of the book guides banks on how to implement the enterprise risk-adjusted return model. Data virtualization (DV) can unify both structured and unstructured data in real-time to power the ERR model. DV has grown in its usage and is a critical part of modern enterprise data architectures. Data as a service (DaaS) is a business-centric service that delivers data assets on demand using a standard connectivity protocol in a predetermined, configurable format and frequency. DV can be exploited by enabling DaaS for data democratization and decision making. Machine learning (ML) and deep learning algorithms are finding more use cases in risk management and in general banking functions. Banks with large derivative portfolios have been early adopters as ML is good at managing non-linear relationships between explanatory variables and explained variables. ML can improve accuracy as be applied for a broader set of variables.

Modern Data Science with R, 2nd Edition

Автор: Limpopo5 от 2022-01-03, 20:40:25
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Modern Data Science with R, 2nd EditionНазвание: Modern Data Science with R, 2nd Edition
Автор: Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton
Издательство: CRC Press
Год: 2021
Страниц: 650
Язык: английский
Формат: pdf (true)
Размер: 83.4 MB

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.

Data Analysis for Business, Economics, and Policy

Автор: Limpopo5 от 2021-11-13, 21:29:14
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Data Analysis for Business, Economics, and PolicyНазвание: Data Analysis for Business, Economics, and Policy
Автор: Gabor Bekes, Gabor Kezdi
Издательство: Cambridge University Press
Год: 2021
Страниц: 742
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, Machine Learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at site.

Mining Complex Networks

Автор: Limpopo5 от 2021-11-12, 13:56:00
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Mining Complex NetworksНазвание: Mining Complex Networks
Автор: Bogumił Kaminski, Paweł Prałat
Издательство: CRC Press
Год: 2022
Страниц: 278
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

This book concentrates on mining networks, a subfield within data science. Data science uses scientific and computational tools to extract valuable knowledge from large data sets. Once data is processed and cleaned, it is analyzed and presented to support decision-making processes. Data science and machine learning tools have become widely used in companies of all sizes. Networks are often large-scale, decentralized, and evolve dynamically over time. Mining complex networks aim to understand the principles governing the organization and the behavior of such networks is crucial for a broad range of fields of study. Here are a few selected typical applications of mining networks: Jupiter notebooks (in Python and Julia) accompany the book and can be accessed on site.

Physics of Data Science and Machine Learning

Автор: Limpopo5 от 2021-11-04, 15:30:16
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Physics of Data Science and Machine LearningНазвание: Physics of Data Science and Machine Learning
Автор: Ijaz A. Rauf
Издательство: CRC Press
Год: 2022
Страниц: 211
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

Physics of Data Science and Machine Learning links fundamental concepts of physics to Data Science, Machine Learning and Artificial Intelligence for physicists looking to integrate these techniques into their work. This book is written explicitly for physicists, marrying quantum and statistical mechanics with modern data mining, Data Science, and Machine Learning. It also explains how to integrate these techniques into the design of experiments, whilst exploring neural networks and Machine Learning building on fundamental concepts of statistical and quantum mechanics. This book is a self-learning tool for physicists looking to learn how to utilize data science and Machine Learning in their research. It will also be of interest to computer scientists and applied mathematicians, alongside graduate students looking to understand the basic concepts and foundations of Data Science, Machine Learning, and Artificial Intelligence.

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