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Mathematical Problems in Data Science

of data science problems including machine learning methods, mathematical image and video processing, topological data analysis, and statistical;

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Practical Statistics for Data Scientists

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic;

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Introduction to Statistical and Machine Learning Methods for Data Science

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Introduction to Statistical and Machine Learning Methods for Data Science

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Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies

, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation;

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Essential Statistics for Non-STEM Data Analysts

Reinforce your understanding of data science and data analysis from a statistical perspective to extract meaningful insights from your data;

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Machine Learning for Knowledge Discovery with R

Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction;

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Machine Learning and Data Science

techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised;

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Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference;

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Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference;

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Statistical Learning and Data Science

new heights of achievement in the incredibly rich data world that we inhabit. Statistical Learning and Data Science is a work;

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Statistical Learning and Data Science

new heights of achievement in the incredibly rich data world that we inhabit. Statistical Learning and Data Science is a work;

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Essentials of Data Science and Analytics

. All these are integral part of Data Science applications. Part IV of the book provides the basics of Machine Learning (ML) and R-statistical;

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Statistical Learning from a Regression Perspective

statistical learning; the impact of big data on statistical learning; the nature and consequences of post-model selection statistical inference;

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Statistical Learning Theory

A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem;

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Practical Data Science With R

, mathematics, machine learning, and computer science, for most non-academics, data science looks like applying analysis techniques to answer key;

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Computer Age Statistical Inference

science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets;

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Julia for Data Science

data mining. This has the practical coverage of statistics and machine learning. You will develop knowledge to build statistical models and;

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Introduction to Data Science

experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis;

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Classification, (Big) Data Analysis and Statistical Learning

This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data;

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Computer Age Statistical Inference, Student Edition

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and;

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Quantitative Medical Data Analysis Using Mathematical Tools And Statistical Techniques

in data analysis, survival data analysis and survival models for cancer patients, statistical analysis and neural network techniques for genomic;

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Data Science and Its Applications

information from data, and insight for developing the concept from different domains, highlighting mathematical and statistical models, operations;

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Machine Learning Research Progress

automatically, by computational and statistical methods. Hence, machine learning is closely related not only to data mining and statistics, but also;

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The Mathematics of Data

interdisciplinary foundations of machine learning and data science.;

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Productive and Efficient Data Science with Python

measure the memory footprint and execution speed of machine learning models, quality test a data science pipelines, and modularizing a data;

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Statistical Inference for Engineers and Data Scientists

in engineering and data science, ideal for graduate students taking courses on statistical inference and detection and estimation, and an invaluable;

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