Python 3 and Data Analytics: Pocket Primer, Oswald Campesato
Автор: Gouse Baig Mohammad, S. Shitharth, Sachi Nandan Mohanty, Sirisha Potluri Название: Cloud Analytics for Industry 4.0 ISBN: 3110771497 ISBN-13(EAN): 9783110771497 Издательство: Walter de Gruyter Рейтинг: Цена: 28814.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides research on the state-of-the-art methods for data management in the fourth industrial revolution, with particular focus on cloud.based data analytics for digital manufacturing infrastructures. Innovative techniques and methods for secure, flexible and profi table cloud manufacturing will be gathered to present advanced and specialized research in the selected area.
Автор: Burk, Scott , Miner, Gary Название: It`s All Analytics! ISBN: 0367359685 ISBN-13(EAN): 9780367359683 Издательство: Taylor&Francis Рейтинг: Цена: 9033.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book, the first in a series of three, provides a look at the foundations of artificial intelligence and analytics and why readers need an unbiased understanding of the subject.
Описание: Chapter 1: Static 2D and 3D GraphsChapter Goal: This chapter introduces the basics of tabulating data and constructing staticgraphical representations. To begin with, it exhibits an approach of extracting and tabulating data by implementing the pandas and sqlalchemy library. Subsequently, it reveals a prevalent 2D and 3D charting recognized as Matplotlib, then exhibits a technique of constructing basic charts (i.e. box-whisker plot, histogram, line plot, and scatter plot).● Tabulating Data● 2D Chartingo Box-whisker-ploto Histogramo Line ploto Scatter ploto Density Plot● 3D Charting● Conclusion Chapter 2: Interactive ChartingChapter Goal: This chapter introduces an approach for constructing interactive charts byimplementing the most prevalent library, recognized as Plotly.● Plotly● 2D Chartingo Box-whisker-ploto Histogramo Line ploto Scatter ploto Density Ploto Bar Charto Pie Charto Sunburst● 3D Charting● Conclusion Chapter 3: Containing functionality in Interactive GraphsChapter Goal: This chapter extends to the preceding chapter. It introduces an approach toupdating interactive graphs to improve user experience. For instance, you will learn how to add buttons and range sliders, among other functionalities. Besides that, it exhibits an approach for integrating innumerable graphs into one graph with some functionality.● Updating Graph Layout● Updating Plotly Axes● Including Range Slider● Including Buttons to a Graph● Styling Interactive Graphs● Updating Plotly X-Axis● Color Sequencing● Subplots● Conclusions Chapter 4: Essentials of HTMLChapter Goal: This chapter introduces the most prevalent markup language for developingwebsites. It acquaints you with the essentials of designing websites. Besides that, it contains a richset of code and examples to support you in getting started with coding using HTML.● The Communication between a Web Browser and Web Server● Domain Hostingo Shared Hostingo Managed Hosting● HyperText Markup Languageo HTML Elements▪ Headings▪ Paragraphs▪ Div▪ Span▪ Buttons▪ Text Box▪ Input▪ File Upload▪ Label▪ Form▪ Meta Tag● Practical Example● Conclusion Chapter 5: Python Web Frameworks and ApplicationsChapter Goal: The preceding chapter acquainted you with interactive visualization using Plotly. This chapter introduces key Python web frameworks (i.e., flask and dash) and how they differ.Besides that, it provides practical examples and helps you get started with Python web development.● Web Frameworks● Web Applications● Flasko WSGIo Werkzeugo Jinjao Installing Flasko Initializing a Flask Web Applicationo Flask Application Codeo Deploy a Flask Web Application● Dasho Installing Dash Dependencieso Initializing a Dash Web Applicationo Dash Application Codeo Deploy a Dash Web Application● Jupyter Dash● Conclusion Chapter 6: Dash Bootstrap ComponentsChapter Goal: This chapter covers dash_bootstrap_component. It is a Python library from the Plotly family, which enables us to have key bootstrap func
Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This second edition has gone through a major revamp and introduces several significant changes and new topics based on the recent trends in NLP.
You'll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised sentiment analysis powered by Python to solve actual case studies. Start by reviewing Python for NLP fundamentals on strings and text data and move on to engineering representation methods for text data, including both traditional statistical models and newer deep learning-based embedding models. Improved techniques and new methods around parsing and processing text are discussed as well.
Text summarization and topic models have been overhauled so the book showcases how to build, tune, and interpret topic models in the context of an interest dataset on NIPS conference papers. Additionally, the book covers text similarity techniques with a real-world example of movie recommenders, along with sentiment analysis using supervised and unsupervised techniques.
There is also a chapter dedicated to semantic analysis where you'll see how to build your own named entity recognition (NER) system from scratch. While the overall structure of the book remains the same, the entire code base, modules, and chapters has been updated to the latest Python 3.x release.
What You'll Learn -Understand NLP and text syntax, semantics and structure-Discover text cleaning and feature engineering-Review text classification and text clustering - Assess text summarization and topic models- Study deep learning for NLP Who This Book Is For IT professionals, data analysts, developers, linguistic experts, data scientists and engineers and basically anyone with a keen interest in linguistics, analytics and generating insights from textual data.
Автор: Corea Название: Big Data Analytics: A Management Perspective ISBN: 3319389912 ISBN-13(EAN): 9783319389912 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is about innovation, big data, and data science seen from a business perspective. Big data is a buzzword nowadays, and there is a growing necessity within practitioners to understand better the phenomenon, starting from a clear stated definition. This book aims to be a starting reading for executives who want (and need) to keep the pace with the technological breakthrough introduced by new analytical techniques and piles of data. Common myths about big data will be explained, and a series of different strategic approaches will be provided. By browsing the book, it will be possible to learn how to implement a big data strategy and how to use a maturity framework to monitor the progress of the data science team, as well as how to move forward from one stage to the next. Crucial challenges related to big data will be discussed, where some of them are more general - such as ethics, privacy, and ownership – while others concern more specific business situations (e.g., initial public offering, growth strategies, etc.). The important matter of selecting the right skills and people for an effective team will be extensively explained, and practical ways to recognize them and understanding their personalities will be provided. Finally, few relevant technological future trends will be acknowledged (i.e., IoT, Artificial intelligence, blockchain, etc.), especially for their close relation with the increasing amount of data and our ability to analyse them faster and more effectively.
Описание: Data science revolves around two giants, which are big data analytics and deep learning. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. This book presents the technologies and tools to simplify and streamline the formation of big data along with deep learning systems.
Описание: Provides a comprehensive overview of business analytics, for those who have either a technical background (quantitative methods) or a practitioner business background. The book provides a comprehensive primer and overview of the field (and related fields), and discusses the field as it applies to financial institutions.
Описание: Provides a comprehensive overview of business analytics, for those who have either a technical background (quantitative methods) or a practitioner business background. The book provides a comprehensive primer and overview of the field (and related fields), and discusses the field as it applies to financial institutions.
Описание: Chapter 1. Introducing Data Representation FeaturesSet the context for the reader with important data representation features, present the need for adaptive algorithms to compute them and demonstrate how these algorithms are important in multiple disciplines. Additionally, discuss a common methodology adopted to derive all our algorithms.Sub-topics: 1. Data representation features2. Computational models for time-varying multi-dimensional data3. Multi-disciplinary origin of adaptive algorithms4. Common Methodology for Derivations of Algorithms5. Outline of The Book Chapter 2. General Theories and NotationsIntroduce the reader to types of data in real-world streaming applications, discuss practical use cases and derive adaptive algorithms for mean, normalized mean, median, and covariances. Support the results with experiments on real data.Sub-topics: 1. Introduction2. Stationary and Non-Stationary Sequences3. Use Cases for Algorithms Covered in this Chapter 4. Adaptive Mean and Covariance of Nonstationary Sequences5. Adaptive Covariance and Inverses6. Adaptive Normalized Mean Algorithm7. Adaptive Median Algorithm8. Experimental Results Chapter 3. Square Root and Inverse Square RootIntroduce readers to practical applications of square roots and inverse square roots of streaming data matrices, then present algorithms to compute them. Support the algorithms with real data.Sub-topics: 1. Introduction and Use Cases2. Adaptive Square Root Algorithms3. Adaptive Inverse Square Root Algorithms4. Experimental Results Chapter 4. First Principal EigenvectorIntroduce the reader to adaptive computation of first principal component of streaming data, discuss the use cases with examples, derive ten algorithms with the common methodology adopted here. Demonstrate the algorithms with real-world non-stationary streaming data examples.Sub-topics: 1. Introduction and Use Cases2. Algorithms and Objective Functions3. OJA Algorithm4. RQ, OJAN, and LUO Algorithms5. IT and XU Algorithms6. Penalty Function Algorithm 7. Augmented Lagrangian Algorithms8. Summary of Algorithms9. Experimental Results Chapter 5. Principal and Minor EigenvectorsIntroduce the reader to adaptive computation of all principal components, discuss powerful use cases with examples, derive 21 adaptive algorithms and demonstrate the algorithms on real-world time-varying data.Sub-topics: 1. Introduction and Use Cases2. Algorithms and Objective Functions3. OJA Algorithms4. XU Algorithms5. PF Algorithms6. AL1 Algorithms7. AL2 Algorithms8. IT Algorithms9. RQ Algorithms10. Summary of Adaptive Eigenvector Algorithms11. Experimental Results Chapter 6. Accelerated Computation eigenvectorsIntroduce the reader to methods to speed up the adaptive algorithms presented in this book. Help the reader speed up a few algorithms and demonstrate their usefulness and acceleration on real-world stationery and non-stationary data.Sub-topics: 1. Introduction2. Gradient Descent Algorithm3. Steepest Descent Algorithm4. Conjugate Direction Algorithm5. Newton-Raphson Algorithm6. Experimental Results Chapter 7. Generalized EigenvectorsIntroduce the reader to the adaptive computation of generalized eigenvectors of streaming data matrices in real-time applications. Dis
Автор: Hodeghatta Название: Practical Business Analytics Using R and Python ISBN: 1484287533 ISBN-13(EAN): 9781484287538 Издательство: Springer Рейтинг: Цена: 8384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. You’ll learn how to analyze data by applying concepts of statistics, probability theory, and linear algebra. In this new edition, both R and Python are used to demonstrate these analyses. Practical Business Analytics Using R and Python also features new chapters covering databases, SQL, Neural networks, Text Analytics, and Natural Language Processing. Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models using statistical machine learning and discusses concepts like regression, classification, and neural networks. Part three covers two of the most popular unsupervised learning techniques, clustering and association mining, as well as text mining and natural language processing (NLP). The book concludes with an overview of big data analytics, R and Python essentials for analytics including libraries such as pandas and NumPy. Upon completing this book, you will understand how to improve business outcomes by leveraging R and Python for data analytics. What You Will Learn * Master the mathematical foundations required for business analytics * Understand various analytics models and data mining techniques such as regression, supervised machine learning algorithms for modeling, unsupervised modeling techniques, and how to choose the correct algorithm for analysis in any given task * Use R and Python to develop descriptive models, predictive models, and optimize models * Interpret and recommend actions based on analytical model outcomes Who This Book Is For Software professionals and developers, managers, and executives who want to understand and learn the fundamentals of analytics using R and Python.
Автор: Mukhopadhyay, Sayan Samanta, Pratip Название: Advanced data analytics using python ISBN: 1484280040 ISBN-13(EAN): 9781484280041 Издательство: Springer Рейтинг: Цена: 6288.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python. This book covers architectural patterns in data analytics, text and image classification, optimization techniques, natural language processing, and computer vision in the cloud environment. Generic design patterns in Python programming is clearly explained, emphasizing architectural practices such as hot potato anti-patterns. You'll review recent advances in databases such as Neo4j, Elasticsearch, and MongoDB. You'll then study feature engineering in images and texts with implementing business logic and see how to build machine learning and deep learning models using transfer learning. Advanced Analytics with Python, 2nd edition features a chapter on clustering with a neural network, regularization techniques, and algorithmic design patterns in data analytics with reinforcement learning. Finally, the recommender system in PySpark explains how to optimize models for a specific application. What You'll Learn * Build intelligent systems for enterprise * Review time series analysis, classifications, regression, and clustering * Explore supervised learning, unsupervised learning, reinforcement learning, and transfer learning * Use cloud platforms like GCP and AWS in data analytics * Understand Covers design patterns in Python Who This Book Is For Data scientists and software developers interested in the field of data analytics.
Автор: Manohar Swamynathan Название: Mastering Machine Learning with Python in Six Steps ISBN: 1484249461 ISBN-13(EAN): 9781484249468 Издательство: Springer Рейтинг: Цена: 8384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages.You’ll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You’ll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data. Finally, you’ll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.What You'll LearnUnderstand machine learning development and frameworksAssess model diagnosis and tuning in machine learningExamine text mining, natuarl language processing (NLP), and recommender systemsReview reinforcement learning and CNNWho This Book Is ForPython developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.
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