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This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendices that provide more background on some of the more abstract concepts.
Monitoring of Air Pollutants: Sampling, Sample Preparation and Analytical Techniques provides a comprehensive reference on air pollutant monitoring, addressing experimental approaches to sampling and sample preparation, as well as analytical technologies (instrumental methods) which are applicable to a wide range of topics.
Python was recently ranked as today’s most popular programming language on the TIOBE index, thanks to its broad applicability to design and prototyping to testing, deployment, and maintenance. With this updated fourth edition, you’ll learn how to get the most out of Python, whether you’re a professional programmer or someone who needs this language to solve problems in a particular field.
Fay and Brittain present statistical hypothesis testing and compatible confidence intervals, focusing on application and proper interpretation. The emphasis is on equipping applied statisticians with enough tools – and advice on choosing among them – to find reasonable methods for almost any problem and enough theory to tackle new problems by modifying existing methods. After covering the basic mathematical theory and scientific principles, tests and confidence intervals are developed for specific types of data. Essential methods for applications are covered, such as general procedures for creating tests (e.g., likelihood ratio, bootstrap, permutation, testing from models), adjustments for multiple testing, clustering, stratification, causality, censoring, missing data, group sequential tests, and non-inferiority tests. New methods developed by the authors are included throughout, such as melded confidence intervals for comparing two samples and confidence intervals associated with Wilcoxon-Mann-Whitney tests and Kaplan-Meier estimates. Examples, exercises, and the R package asht support practical use.
In a brand new Jumanji adventure, four high school kids discover an old video game console and are drawn into the game’s jungle setting, literally becoming the adult avatars they chose. What they discover is that you don’t just play Jumanji – you must survive it. To beat the game and return to the real world, they’ll have to go on the most dangerous adventure of their lives, discover what Alan Parrish left 20 years ago, and change the way they think about themselves – or they’ll be stuck in the game forever, to be played by others without break.
