Best Mathematical Analysis Books
Here you will get Best Mathematical Analysis Books For you.This is an up-to-date list of recommended books.
1. Graph Paper Composition Notebook: Grid Paper Notebook, Quad Ruled, 100 Sheets (Large, 8.5 x 11) (Graph Paper Notebooks)
Author: by Joyful Journals
Graph Paper Composition Notebook, Journal, Diary One Subject 100 PagesMakes a wonderful daily graph/grid notebook to draw, write, journal, take notes, make lists, and much more creativity! This is a simple and durable all-purpose daily graph/grid notebook. There is plenty of room inside for drawing, writing notes, journaling, doodling, list making, creative writing, school notes, and capturing ideas.
It can be used as a notebook, journal, diary, or composition book. This paperback notebook is 8.5 x 11 in / 21.59 x 27. 94 cm with a soft, matte cover and has 100 quad ruled pages. Perfect for all ages – kids or adults! Wonderful as a gift, present, or personal notebook!
About this notebook:100 graph ruled pagesGrid ruled on both sides with thin gray linesPerfect for architects, artists, and any drawing activitiesHigh-quality matte cover for a professional finishPerfect size at 8.5 x 11 in / 21.59 x 27. 94 cm – Larger than mostScroll up and click ‘buy’ to grab one today!
2. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
Author: by Peter Bruce
Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.
Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.
With this book, you’ll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher-quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that “learn” from dataUnsupervised learning methods for extracting meaning from unlabeled data.
3. A Common-Sense Guide to Data Structures and Algorithms, Second Edition: Level Up Your Core Programming Skills
Author: by Jay Wengrow
This new and revised second edition features new chapters on recursion, dynamic programming, and using Big O in your daily work. Use Big O notation to measure and articulate the efficiency of your code, and modify your algorithm to make it faster.
Find out how your choice of arrays, linked lists, and hash tables can dramatically affect the code you write. Use recursion to solve tricky problems and create algorithms that run exponentially faster than the alternatives. Dig into advanced data structures such as binary trees and graphs to help scale specialized applications such as social networks and mapping software.
4. The Visual Display of Quantitative Information
Author: by Tufte
The classic book on statistical graphics, charts, tables. Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis.
Design of the high-resolution displays, small multiples. Editing and improving graphics.The data-ink ratio. Time-series, relational graphics, data maps, multivariate designs. Detection of graphical deception: design variation vs.Data variation. Sources of deception. Aesthetics and data graphical displays. This is the second edition of The Visual Display of Quantitative Information.
Recently published, this new edition provides excellent color reproductions of the many graphics of William Playfair, adds color to other images, and includes all the changes and corrections accumulated during 17 printings of the first edition.
5. Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines
Author: by Chris Fregly
With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level up your skills.
This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance.
Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and moreUse automated machine learning to implement a specific subset of use cases with SageMaker AutopilotDive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deploymentTie everything together into a repeatable machine learning operations pipelineExplore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache KafkaLearn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more
6. Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
Author: by Valliappa Lakshmanan
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice.
In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.
You’ll learn how to:Identify and mitigate common challenges when training, evaluating, and deploying ML modelsRepresent data for different ML model types, including embeddings, feature crosses, and moreChoose the right model type for specific problemsBuild a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuningDeploy scalable ML systems that you can retrain and update to reflect new dataInterpret model predictions for stakeholders and ensure models are treating users fairly
7. Real Analysis: A Long-Form Mathematics Textbook (The Long-Form Math Textbook Series)
Author: by Jay Cummings
This textbook is designed for students. Rather than the typical definition-theorem-proof-repeat style, this text includes much more commentary, motivation and explanation. The proofs are not terse, and aim for understanding over economy. Furthermore, dozens of proofs are preceded by “scratch work” or a proof sketch to give students a big-picture view and an explanation of how they would come up with it on their own.
Examples often drive the narrative and challenge the intuition of the reader. The text also aims to make the ideas visible, and contains over 200 illustrations. The writing is relaxed and includes interesting historical notes, periodic attempts at humor, and occasional diversions into other interesting areas of mathematics.
The text covers the real numbers, cardinality, sequences, series, the topology of the reals, continuity, differentiation, integration, and sequences and series of functions. Each chapter ends with exercises, and nearly all include some open questions. The first appendix contains a construction the reals, and the second is a collection of additional peculiar and pathological examples from analysis.
8. Implementing Useful Algorithms in C++
Author: by Dmytro Kedyk
Programmers use algorithms and data structures all the time, usually through numerous available APIs. Ideally an algorithm is correct, easy to understand, applicable to many problems, efficient, and free of intellectual property claims. This book covers algorithms and data structures learned in an algorithms class and many that aren’t, including statistical algorithms, external memory algorithms, numerical methods, optimization, string algorithms, and data compression.
About a fourth of the book is devoted to machine learning. There is much more theory than in the rest of the book because in machine learning relevant theory is more practical than algorithms. New learning algorithms are proposed often, and it’s easy to get lost without understanding how learning actually works.
In particular, getting comfortable with the concept of estimation error substantially improves the ability to reason about statistical algorithms. Another fourth is about numerical algorithms. Separate chapters cover matrix algorithms (such as eigenvalue calculation for spectral clustering), working with functions (integration, root finding, etc., and optimization (both continuous and convex).
9. Mastering Shiny: Build Interactive Apps, Reports, and Dashboards Powered by R
Author: by Hadley Wickham
Master the Shiny web frameworkand take your R skills to a whole new level. By letting you move beyond static reports, Shiny helps you create fully interactive web apps for data analyses. Users will be able to jump between datasets, explore different subsets or facets of the data, run models with parameter values of their choosing, customize visualizations, and much more.
Get started: Discover how the major pieces of a Shiny app fit togetherPut Shiny in action: Explore Shiny functionality with a focus on code samples, example apps, and useful techniquesMaster reactivity: Go deep into the theory and practice of reactive programming and examine reactive graph componentsApply best practices: Examine useful techniques for making your Shiny apps work well in production
10. Graph Paper Composition Notebook: Quad Ruled 5×5, Grid Paper for Math & Science Students (8.5 x 11)
Author: by Math Wizo
5×5 Graph Paper Composition Notebook:Perfect for math, science, school, college, drawing, writing, to-do lists, and more! About this notebook:Length: 103 Pages – Surprise Gift on the Last PagePaper: Good Quality White, Quad Ruled PaperSize: 8.5 x 11 IN / 21.59 x 27. 94 CMCover: High Quality Matte Soft CoverBinding: Professional Paperback Binding, Non-Perforated PagesScroll up and click ‘buy’ to get yours now!
11. Understanding Analysis (Undergraduate Texts in Mathematics)
Author: by Stephen Abbott
This lively introductory text exposes the student to the rewards of a rigorous study of functions of a real variable. In each chapter, informal discussions of questions that give analysis its inherent fascination are followed by precise, but not overly formal, developments of the techniques needed to make sense of them.
By focusing on the unifying themes of approximation and the resolution of paradoxes that arise in the transition from the finite to the infinite, the text turns what could be a daunting cascade of definitions and theorems into a coherent and engaging progression of ideas.
Acutely aware of the need for rigor, the student is much better prepared to understand what constitutes a proper mathematical proof and how to write one. Fifteen years of classroom experience with the first edition of Understanding Analysis have solidified and refined the central narrative of the second edition.
Roughly 150 new exercises join a selection of the best exercises from the first edition, and three more project-style sections have been added. Investigations of Euler’s computation of (2), the Weierstrass Approximation Theorem, and the gamma function are now among the book’s cohort of seminal results serving as motivation and payoff for the beginning student to master the methods of analysis.
12. Learning Spark: Lightning-Fast Data Analytics
Author: by Jules S. Damji
Data is bigger, arrives faster, and comes in a variety of formatsand it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently?Enter Apache Spark. Updated to include Spark 3.
0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, you’ll be able to:Learn Python, SQL, Scala, or Java high-level Structured APIsUnderstand Spark operations and SQL EngineInspect, tune, and debug Spark operations with Spark configurations and Spark UIConnect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or KafkaPerform analytics on batch and streaming data using Structured StreamingBuild reliable data pipelines with open source Delta Lake and SparkDevelop machine learning pipelines with MLlib and productionize models using MLflow
13. Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications (Technical Incerto)
Author: by Nassim Nicholas Taleb
STEM Academic Press
The book investigates the misapplication of conventional statistical techniques to fat tailed distributions and looks for remedies, when possible. Switching from thin tailed to fat tailed distributions requires more than changing the color of the dress. Traditional asymptotics deal mainly with either n=1 or n=, and the real world is in between, under the laws of the medium numberswhich vary widely across specific distributions.
Both the law of large numbers and the generalized central limit mechanisms operate in highly idiosyncratic ways outside the standard Gaussian or Levy-Stable basins of convergence. A few examples: – The sample mean is rarely in line with the population mean, with effect on nave empiricism, but can be sometimes be estimated via parametric methods.
The empirical distribution is rarely empirical. Parameter uncertainty has compounding effects on statistical metrics. Dimension reduction (principal components) fails. Inequality estimators (Gini or quantile contributions) are not additive and produce wrong results. Many biases found in psychology become entirely rational under more sophisticated probability distributions.
14. The Art of Doing Science and Engineering: Learning to Learn
Author: by Richard W. Hamming
May 26, 2020
A groundbreaking treatise by one of the great mathematicians of our time, who argues that highly effective thinking can be learned. What spurs on and inspires a great idea? Can we train ourselves to think in a way that will enable world-changing understandings and insights to emerge?
Richard Hamming said we can, and first inspired a generation of engineers, scientists, and researchers in 1986 with “You and Your Research,” an electrifying sermon on why some scientists do great work, why most don’t, why he did, and why you should, too.
The Art of Doing Science and Engineering is the full expression of what “You and Your Research” outlined. It’s a book about thinking; more specifically, a style of thinking by which great ideas are conceived. The book is filled with stories of great people performing mighty deeds-but they are not meant to simply be admired.
Instead, they are to be aspired to, learned from, and surpassed. Hamming consistently returns to Shannon’s information theory, Einstein’s relativity, Grace Hopper’s work on high-level programming, Kaiser’s work on digital fillers, and his own error-correcting codes. He also recounts a number of his spectacular failures as clear examples of what to avoid.
15. Sudoku for Kids: 100+ Sudoku Puzzles From Beginner to Advanced (Woo! Jr. Kids Activities Books)
Author: by Woo! Jr. Kids
If you’re looking for a sudoku book for beginners, here’s the perfect first sudoku puzzle book for you! Starting children out with super easy 4×4 puzzles, this book progressively challenges the reader with harder and harder puzzles. After conquering the 4×4 grid, readers then get to take on the popular 9×9 sudoku puzzle.
With over 100 puzzles for any skill level, Sudoku for Kids is published in large print so that adults can enjoy it, too. All puzzle answers are included in the back of the book. Teachers and homeschoolers: this book comes with reproduction rights for educational use.
Teach your kids to develop critical thinking and logic skills with the addicting fun of sudoku!
16. Visual Complex Analysis
Author: by Tristan Needham
This radical first course on complex analysis brings a beautiful and powerful subject to life by consistently using geometry (not calculation) as the means of explanation. Aimed at undergraduate students in mathematics, physics, and engineering, the book’s intuitive explanations, lack ofadvanced prerequisites, and consciously user-friendly prose style will help students to master the subject more readily than was previously possible.
The key to this is the book’s use of new geometric arguments in place of the standard calculational ones. These geometric arguments are communicatedwith the aid of hundreds of diagrams of a standard seldom encountered in mathematical works. A new approach to a classical topic, this work will be of interest to students in mathematics, physics, and engineering, as well as to professionals in these fields.