# Practical Statistics for Data Scientists

With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised ...

Author: Peter Bruce

Publisher: O'Reilly Media

ISBN: 149207294X

Category: Computers

Page: 350

View: 177

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 practical guide--now including examples in Python as well as R--explains how to apply various 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 scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised learning
Categories: Computers

# Practical Statistics for Data Scientists

With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design ...

Author: Peter Bruce

Publisher: "O'Reilly Media, Inc."

ISBN: 9781491952931

Category: Computers

Page: 318

View: 267

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various 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 programming language, 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 science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data
Categories: Computers

# Generatives Deep Learning

David Foster veranschaulicht die Funktionsweise jeder Methode, beginnend mit den Grundlagen des Deep Learning mit Keras, bevor er zu einigen der modernsten Algorithmen auf diesem Gebiet vorstößt.

Author: David Foster

Publisher:

ISBN: OCLC:1151051275

Category:

Page: 310

View: 247

Generative Modelle haben sich zu einem der spannendsten Themenbereiche der Künstlichen Intelligenz entwickelt: Mit generativem Deep Learning ist es inzwischen möglich, einer Maschine das Malen, Schreiben oder auch das Komponieren von Musik beizubringen - kreative Fähigkeiten, die bisher dem Menschen vorbehalten waren. Mit diesem praxisnahen Buch können Data Scientists einige der eindrucksvollsten generativen Deep-Learning-Modelle nachbilden wie z.B. Generative Adversarial Networks (GANs), Variational Autoencoder (VAEs), Encoder-Decoder- sowie World-Modelle. David Foster veranschaulicht die Funktionsweise jeder Methode, beginnend mit den Grundlagen des Deep Learning mit Keras, bevor er zu einigen der modernsten Algorithmen auf diesem Gebiet vorstößt. Die zahlreichen praktischen Beispiele und Tipps helfen dem Leser herauszufinden, wie seine Modelle noch effizienter lernen und noch kreativer werden können.
Categories:

# Neuronale Netze Selbst Programmieren

- Tariq Rashid hat eine besondere Fähigkeit, schwierige Konzepte verständlich zu erklären, dadurch werden Neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.

Author: Tariq Rashid

Publisher:

ISBN: 1492064041

Category:

Page: 232

View: 468

Neuronale Netze sind Schlüsselelemente des Deep Learning und der Künstlichen Intelligenz, die heute zu Erstaunlichem in der Lage sind. Dennoch verstehen nur wenige, wie Neuronale Netze tatsächlich funktionieren. Dieses Buch nimmt Sie mit auf eine unterhaltsame Reise, die mit ganz einfachen Ideen beginnt und Ihnen Schritt für Schritt zeigt, wie Neuronale Netze arbeiten. Dafür brauchen Sie keine tieferen Mathematik-Kenntnisse, denn alle mathematischen Konzepte werden behutsam und mit vielen Illustrationen erläutert. Dann geht es in die Praxis: Sie programmieren Ihr eigenes Neuronales Netz mit Python und bringen ihm bei, handgeschriebene Zahlen zu erkennen, bis es eine Performance wie ein professionell entwickeltes Netz erreicht. Zum Schluss lassen Sie das Netz noch auf einem Raspberry Pi Zero laufen. - Tariq Rashid hat eine besondere Fähigkeit, schwierige Konzepte verständlich zu erklären, dadurch werden Neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.
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# Cognitive Computing Using Green Technologies

Descriptive statistics for multivariate distributions. ... Practical statistics for data scientists: 50 essential concepts. ... Data science: an action plan for expanding the technical areas of the field of statistics.

Author: Asis Kumar Tripathy

Publisher: CRC Press

ISBN: 9781000337884

Category: Computers

Page: 296

View: 473

Cognitive Computing is a new topic which aims to simulate human thought processes using computers that self-learn through data mining, pattern recognition, and natural language processing. This book focuses on the applications of Cognitive Computing in areas like Robotics, Blockchain, Deep Learning, and Wireless Technologies. This book covers the basics of Green Computing, discusses Cognitive Science methodologies in Robotics, Computer Science, Wireless Networks, and Deep Learning. It goes on to present empirical data and research techniques, modelling techniques and offers a data-driven approach to decision making and problem solving. This book is written for researchers, academicians, undergraduate and graduate students, and industry persons who are working on current applications of Cognitive Computing.
Categories: Computers

# Quantitative Economics with R

A Data Science Approach Vikram Dayal ... Bruce and Bruce (2017) is accessible and written for data scientists. Kennedy (2003) has a useful appendix on sampling ... Practical statistics for data scientists: 50 essential concepts.

Author: Vikram Dayal

Publisher: Springer Nature

ISBN: 9789811520358

Category: Mathematics

Page: 326

View: 711

This book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham’s tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code, the reader’s R skills are gradually honed, with the help of “your turn” exercises. At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrap is introduced. Causal inference is illuminated using simulation, data graphs, and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and examples. Lastly, two computationally intensive methods—generalized additive models and random forests (an important and versatile machine learning method)—are introduced intuitively with applications. The book will be of great interest to economists—students, teachers, and researchers alike—who want to learn R. It will help economics students gain an intuitive appreciation of applied economics and enjoy engaging with the material actively, while also equipping them with key data science skills.
Categories: Mathematics

# Hands On Machine Learning with R

Categorical Data Analysis. Wiley Series in Probability and Statistics. ... In Proceedings of the 23rd International Conference on Machine Learning, pages 97–104. ACM. ... Practical Statistics for Data Scientists: 50 Essential Concepts.

Publisher: CRC Press

ISBN: 9781000730432

Page: 456

View: 199

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.

# Data Analytics

A. B. Bruce, Practical Statistics for Data Scientists: 50 Essential Concepts, New York: O'Reilly Publishing, 2017. [Carlberg2014] C. Carlberg, Decision Analytics: Microsoft Excel, Indianapolis: Que, 2014.

Author: Christopher Greco

Publisher: Mercury Learning and Information

ISBN: 9781683926467

Category: Computers

Page: 148

View: 248

Data analytics is creeping into the lexicon of our daily language. This book gives the reader a perspective as to the overall data analytics skill set, starting with a primer on statistics, and works toward the application of those methods. There are a variety of formulas and algorithms used in the data analytics process. These formulas can be plugged into whatever software application the reader uses to obtain the answer they need. There are several demonstrations of this process to provide straightforward instruction as to how to bring data analytics skills to your critical thinking. This book presents a variety of methods and techniques, as well as case studies, to enrich the knowledge of data analytics for project managers, systems engineers, and cybersecurity professionals. It separates the case studies so that each profession can practice some straightforward data analytics specific to their fields. The main purpose of this text is to refresh the statistical knowledge necessary to build models for data analytics. Along with that, this book encompasses the analytics thinking that is essential to all three professions. FEATURES: Provides straightforward instruction on data analytics methods Includes methods, techniques, and case studies for project managers, systems engineers, and cybersecurity professionals Refreshes the statistical knowledge needed to bring data analytics into your skillset and decision-making Focuses on getting readers up to speed quickly and efficiently to be able to see the impact of data analytics and analytical thinking
Categories: Computers

# Big Data in Psychiatry and Neurology

Practical statistics for data scientists: 50 essential concepts. O'Reilly Media, Inc. Candel, A., Parmar, V., LeDell, E., & Arora, A. (2016). Deep learning with H2O. H2O.ai Inc. Chaczko, Z., Mahadevan, V., Aslanzadeh, S., & Mcdermid, ...

Author: Ahmed A. Moustafa

ISBN: 9780128230022

Category: Medical

Page: 384

View: 436

Big Data in Psychiatry and Neurology provides an up-to-date overview of achievements in the field of big data in Psychiatry and Medicine, including applications of big data methods to aging disorders (e.g., Alzheimer’s disease and Parkinson’s disease), mood disorders (e.g., major depressive disorder), and drug addiction. This book will help researchers, students and clinicians implement new methods for collecting big datasets from various patient populations. Further, it will demonstrate how to use several algorithms and machine learning methods to analyze big datasets, thus providing individualized treatment for psychiatric and neurological patients. As big data analytics is gaining traction in psychiatric research, it is an essential component in providing predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level. Discusses longitudinal big data and risk factors surrounding the development of psychiatric disorders Analyzes methods in using big data to treat psychiatric and neurological disorders Describes the role machine learning can play in the analysis of big data Demonstrates the various methods of gathering big data in medicine Reviews how to apply big data to genetics
Categories: Medical

# Mathematical Problems in Data Science

J. Dean, S. Ghemawat, MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008) C. Feng, A. Sutherland, R. King, S. Muggleton, R. Henery, Comparison of machine learning classifiers to statistics and ...

Author: Li M. Chen

Publisher: Springer

ISBN: 9783319251271

Category: Computers

Page: 213

View: 408

This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types of big data, geometric data structures, topological data processing, and various learning methods. For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions and both statistical and computational methods for data analysis. Initial chapters focus on exploring the properties of incomplete data sets and partial-connectedness among data points or data sets. Discussions also cover the completion problem of Netflix matrix; machine learning method on massive data sets; image segmentation and video search. This book introduces software tools for data science and Big Data such MapReduce, Hadoop, and Spark. This book contains three parts. The first part explores the fundamental tools of data science. It includes basic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematical image and video processing, topological data analysis, and statistical methods. The final section provides case studies on special topics in variational learning, manifold learning, business and financial data rec overy, geometric search, and computing models. Mathematical Problems in Data Science is a valuable resource for researchers and professionals working in data science, information systems and networks. Advanced-level students studying computer science, electrical engineering and mathematics will also find the content helpful.
Categories: Computers