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Excel Data Analysis For Dummies
Turn jumbles of numbers into graphics, insights, and answers with Excel With Microsoft Excel, you can, well, excel at data analysis. And Excel Data Analysis For Dummies can help, with clear and easy explanations of the mountain of features for creating, visualizing, and analyzing data. PivotTables, charts, what-if analysis, statistical functions—it's all in here, with examples and ideas for Excel users of all skill levels. This latest edition covers the most recent updates to Excel and Microsoft 365. You'll beef up your data skills and learn powerful techniques for turning numbers into knowledge. For students, researchers, and business professionals, Excel is the spreadsheet and data application of choice—and Dummies is the best choice for learning how to make those numbers sing. Learn how to use Excel's built-in data analysis features and write your own functions to explore patterns in your dataCreate striking charts and visualizations, and discover multiple ways to tell the stories hidden in the numbersClean up large datasets and identify statistical operations that will answer your questionsPerform financial calculations, database operations, and more—without leaving Excel Excel Data Analysis For Dummies is the go-to resource for Excel users who are looking for better ways to crunch the numbers.
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Advances in Data Analysis
This book focuses on exploratory data analysis, learning of latent structures in datasets, and unscrambling of knowledge. Coverage details a broad range of methods from multivariate statistics, clustering and classification, visualization and scaling as well as from data and time series analysis. It provides new approaches for information retrieval and data mining and reports a host of challenging applications in various fields.
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Data Analysis
"Data Analysis" in the broadest sense is the general term for a field of activities of ever-increasing importance in a time called the information age. It covers new areas with such trendy labels as, e.g., data mining or web mining as well as traditional directions emphazising, e.g., classification or knowledge organization. Leading researchers in data analysis have contributed to this volume and delivered papers on aspects ranging from scientific modeling to practical application. They have devoted their latest contributions to a book edited to honor a colleague and friend, Hans-Hermann Bock, who has been active in this field for nearly thirty years.
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Concept Building in Fisheries Data Analysis
This authored book is primarily for the readers who are in search of how to use basic concepts of statistics in the field of fishery science. It has been written for the new comers coming in the fishery sector including university students, teachers and research scholars. It enriches the reader’s knowledge of data analysis step by step starting from data collection to its analysis and interpretation along with practical application with real example. It supports readers for enhancing theoretical and practical concept of application of fundamental statistics in fishery domain. Reader may also learn how to analyses data using different statistical software. Each chapter starts with an introduction of the topic followed by statistical concept with example and ends with chapter based question for comprehension purpose. This book is of interest to teachers, researchers, scientists, capacity builders in fishery sciences. Also the book serves as additional reading material for undergraduate and graduate students of fisheries and aquatic sciences.
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Programming with Python
Based on the latest version of the language, this book offers a self-contained, concise and coherent introduction to programming with Python. The book’s primary focus is on realistic case study applications of Python. Each practical example is accompanied by a brief explanation of the problem-terminology and concepts, followed by necessary program development in Python using its constructs, and simulated testing. Given the open and participatory nature of development, Python has a variety of incorporated data structures, which has made it difficult to present it in a coherent manner. Further, some advanced concepts (super, yield, generator, decorator, etc.) are not easy to explain. The book specially addresses these challenges; starting with a minimal subset of the core, it offers users a step-by-step guide to achieving proficiency.
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Introduction to Machine Learning with Python
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills
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Advanced Machine Learning with Python
Solve challenging data science problems by mastering cutting-edge machine learning techniques in PythonAbout This Book• Resolve complex machine learning problems and explore deep learning• Learn to use Python code for implementing a range of machine learning algorithms and techniques• A practical tutorial that tackles real-world computing problems through a rigorous and effective approachWho This Book Is ForThis title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you!Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful.What You Will Learn• Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms• Apply your new found skills to solve real problems, through clearly-explained code for every technique and test• Automate large sets of complex data and overcome time-consuming practical challenges• Improve the accuracy of models and your existing input data using powerful feature engineering techniques• Use multiple learning techniques together to improve the consistency of results• Understand the hidden structure of datasets using a range of unsupervised techniques• Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach• Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models togetherIn DetailDesigned to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data.The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce.This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano.By this end of th
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Statistical Analysis of Financial Data
Statistical Analysis of Financial Data covers the use of statistical analysis and the methods of data science to model and analyze financial data. The first chapter is an overview of financial markets, describing the market operations and using exploratory data analysis to illustrate the nature of financial data. The software used to obtain the data for the examples in the first chapter and for all computations and to produce the graphs is R. However discussion of R is deferred to an appendix to the first chapter, where the basics of R, especially those most relevant in financial applications, are presented and illustrated. The appendix also describes how to use R to obtain current financial data from the internet. Chapter 2 describes the methods of exploratory data analysis, especially graphical methods, and illustrates them on real financial data. Chapter 3 covers probability distributions useful in financial analysis, especially heavy-tailed distributions, and describes methods of computer simulation of financial data. Chapter 4 covers basic methods of statistical inference, especially the use of linear models in analysis, and Chapter 5 describes methods of time series with special emphasis on models and methods applicable to analysis of financial data.Features* Covers statistical methods for analyzing models appropriate for financial data, especially models with outliers or heavy-tailed distributions. * Describes both the basics of R and advanced techniques useful in financial data analysis.* Driven by real, current financial data, not just stale data deposited on some static website.* Includes a large number of exercises, many requiring the use of open-source software to acquire real financial data from the internet and to analyze it.
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Data Analysis with Stata
Explore the big data field and learn how to perform data analytics and predictive modelling in STATAAbout This Book• Visualize and analyse data in STATA to devise a business strategy• Learn STATA programming and predictive modeling• Discover how you can become a data scientist with the power of STATAWho This Book Is ForThis book is for all the professionals and students who want to learn STATA programming and apply predictive modelling concepts. This book is also very helpful for experienced STATA programmers as it provides advanced statistical modelling concepts and their application.What You Will Learn• Perform important statistical tests to become a STATA data scientist• Be guided through how to program in STATA• Implement logistic and linear regression models• Visualize and program the data in STATA• Analyse survey data, time series data, and survival data• Perform database management in STATAIn DetailSTATA is an integrated software package that provides you with everything you need for data analysis, data management, and graphics. STATA also provides you with a platform to efficiently perform simulation, regression analysis (linear and multiple) [and custom programming.This book covers data management, graphs visualization, and programming in STATA. Starting with an introduction to STATA and data analytics you'll move on to STATA programming and data management. Next, the book takes you through data visualization and all the important statistical tests in STATA. Linear and logistic regression in STATA is also covered.As you progress through the book, you will explore a few analyses, including the survey analysis, time series analysis, and survival analysis in STATA. You'll also discover different types of statistical modelling techniques and learn how to implement these techniques in STATA.Style and approachThis book is a hands-onguide to STATA programming and statistical modelling providing many STATA code examples and taking. You through the working of the code in detail.
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Learning Scientific Programming with Python
Learn to master basic programming tasks from scratch with real-life, scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to gain proficiency quickly. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving on to the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualization, this textbook also discusses the use of Jupyter Notebooks to build rich-media, shareable documents for scientific analysis. The second edition features a new chapter on data analysis with the pandas library and comprehensive updates, and new exercises and examples. A final chapter introduces more advanced topics such as floating-point precision and algorithm stability, and extensive online resources support further study. This textbook represents a targeted package for students requiring a solid foundation in Python programming.
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Statistics and Calculus with Python Workshop
With examples and activities that help you achieve real results, applying calculus and statistical methods relevant to advanced data science has never been so easyKey FeaturesDiscover how most programmers use the main Python libraries when performing statistics with PythonUse descriptive statistics and visualizations to answer business and scientific questionsSolve complicated calculus problems, such as arc length and solids of revolution using derivatives and integralsBook DescriptionAre you looking to start developing artificial intelligence applications? Do you need a refresher on key mathematical concepts? Full of engaging practical exercises, The Statistics and Calculus with Python Workshop will show you how to apply your understanding of advanced mathematics in the context of Python.The book begins by giving you a high-level overview of the libraries you'll use while performing statistics with Python. As you progress, you'll perform various mathematical tasks using the Python programming language, such as solving algebraic functions with Python starting with basic functions, and then working through transformations and solving equations. Later chapters in the book will cover statistics and calculus concepts and how to use them to solve problems and gain useful insights. Finally, you'll study differential equations with an emphasis on numerical methods and learn about algorithms that directly calculate values of functions.By the end of this book, you'll have learned how to apply essential statistics and calculus concepts to develop robust Python applications that solve business challenges.What you will learnGet to grips with the fundamental mathematical functions in PythonPerform calculations on tabular datasets using pandasUnderstand the differences between polynomials, rational functions, exponential functions, and trigonometric functionsUse algebra techniques for solving systems of equationsSolve real-world problems with probabilitySolve optimization problems with derivatives and integralsWho this book is forIf you are a Python programmer who wants to develop intelligent solutions that solve challenging business problems, then this book is for you. To better grasp the concepts explained in this book, you must have a thorough understanding of advanced mathematical concepts, such as Markov chains, Euler's formula, and Runge-Kutta methods as the book only explains how these techniques and concepts can be implemented in Python.
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Machine Learning for Decision Sciences with Case Studies in Python
This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learning including unsupervised, supervised, and reinforcement learning.This book is aimed at researchers, professionals, and graduate students in data science, machine learning, computer science, and electrical and computer engineering.
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Interactive Visual Data Analysis
In the age of big data, being able to make sense of data is an important key to success. Interactive Visual Data Analysis advocates the synthesis of visualization, interaction, and automatic computation to facilitate insight generation and knowledge crystallization from large and complex data.The book provides a systematic and comprehensive overview of visual, interactive, and analytical methods. It introduces criteria for designing interactive visual data analysis solutions, discusses factors influencing the design, and examines the involved processes. The reader is made familiar with the basics of visual encoding and gets to know numerous visualization techniques for multivariate data, temporal data, geo-spatial data, and graph data. A dedicated chapter introduces general concepts for interacting with visualizations and illustrates how modern interaction technology can facilitate the visual data analysis in many ways. Addressing today’s large and complex data, the book covers relevant automatic analytical computations to support the visual data analysis. The book also sheds light on advanced concepts for visualization in multi-display environments, user guidance during the data analysis, and progressive visual data analysis.The authors present a top-down perspective on interactive visual data analysis with a focus on concise and clean terminology. Many real-world examples and rich illustrations make the book accessible to a broad interdisciplinary audience from students, to experts in the field, to practitioners in data-intensive application domains.Features:Dedicated to the synthesis of visual, interactive, and analysis methodsSystematic top-down view on visualization, interaction, and automatic analysisBroad coverage of fundamental and advanced visualization techniquesComprehensive chapter on interacting with visual representationsExtensive integration of automatic computational methodsAccessible portrayal of cutting-edge visual analytics technologyForeword by Jack van WijkFor more information, you can also visit the author website, where the book's figures are made available under the CC BY Open Access license.
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Machine Learning for Streaming Data with Python
Apply machine learning to streaming data with the help of practical examples, and deal with challenges that surround streamingKey FeaturesWork on streaming use cases that are not taught in most data science coursesGain experience with state-of-the-art tools for streaming dataMitigate various challenges while handling streaming dataBook DescriptionStreaming data is the new top technology to watch out for in the field of data science and machine learning. As business needs become more demanding, many use cases require real-time analysis as well as real-time machine learning. This book will help you to get up to speed with data analytics for streaming data and focus strongly on adapting machine learning and other analytics to the case of streaming data.You will first learn about the architecture for streaming and real-time machine learning. Next, you will look at the state-of-the-art frameworks for streaming data like River. Later chapters will focus on various industrial use cases for streaming data like Online Anomaly Detection and others. As you progress, you will discover various challenges and learn how to mitigate them. In addition to this, you will learn best practices that will help you use streaming data to generate real-time insights.By the end of this book, you will have gained the confidence you need to stream data in your machine learning models.What you will learnUnderstand the challenges and advantages of working with streaming dataDevelop real-time insights from streaming dataUnderstand the implementation of streaming data with various use cases to boost your knowledgeDevelop a PCA alternative that can work on real-time dataExplore best practices for handling streaming data that you absolutely need to rememberDevelop an API for real-time machine learning inferenceWho this book is forThis book is for data scientists and machine learning engineers who have a background in machine learning, are practice and technology-oriented, and want to learn how to apply machine learning to streaming data through practical examples with modern technologies. Although an understanding of basic Python and machine learning concepts is a must, no prior knowledge of streaming is required.
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Automated Data Analysis Using Excel
This new edition covers some of the key topics relating to the latest version of MS Office through Excel 2019, including the creation of custom ribbons by injecting XML code into Excel Workbooks and how to link Excel VBA macros to customize ribbon objects. It now also provides examples in using ADO, DAO, and SQL queries to retrieve data from databases for analysis. Operations such as fully automated linear and non-linear curve fitting, linear and non-linear mapping, charting, plotting, sorting, and filtering of data have been updated to leverage the newest Excel VBA object models. The text provides examples on automated data analysis and the preparation of custom reports suitable for legal archiving and dissemination.Functionality Demonstrated in This Edition Includes: Find and extract information raw data files Format data in color (conditional formatting) Perform non-linear and linear regressions on data Create custom functions for specific applications Generate datasets for regressions and functions Create custom reports for regulatory agencies Leverage email to send generated reports Return data to Excel using ADO, DAO, and SQL queries Create database files for processed data Create tables, records, and fields in databases Add data to databases in fields or records Leverage external computational engines Call functions in MATLAB® and Origin® from Excel
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Hands-On Data Analysis with Scala
Master scala's advanced techniques to solve real-world problems in data analysis and gain valuable insights from your dataKey FeaturesA beginner's guide for performing data analysis loaded with numerous rich, practical examplesAccess to popular Scala libraries such as Breeze, Saddle for efficient data manipulation and exploratory analysisDevelop applications in Scala for real-time analysis and machine learning in Apache SparkBook DescriptionEfficient business decisions with an accurate sense of business data helps in delivering better performance across products and services. This book helps you to leverage the popular Scala libraries and tools for performing core data analysis tasks with ease.The book begins with a quick overview of the building blocks of a standard data analysis process. You will learn to perform basic tasks like Extraction, Staging, Validation, Cleaning, and Shaping of datasets. You will later deep dive into the data exploration and visualization areas of the data analysis life cycle. You will make use of popular Scala libraries like Saddle, Breeze, Vegas, and PredictionIO for processing your datasets. You will learn statistical methods for deriving meaningful insights from data. You will also learn to create applications for Apache Spark 2.x on complex data analysis, in real-time. You will discover traditional machine learning techniques for doing data analysis. Furthermore, you will also be introduced to neural networks and deep learning from a data analysis standpoint.By the end of this book, you will be capable of handling large sets of structured and unstructured data, perform exploratory analysis, and building efficient Scala applications for discovering and delivering insightsWhat you will learnTechniques to determine the validity and confidence level of dataApply quartiles and n-tiles to datasets to see how data is distributed into many bucketsCreate data pipelines that combine multiple data lifecycle stepsUse built-in features to gain a deeper understanding of the dataApply Lasso regression analysis method to your dataCompare Apache Spark API with traditional Apache Spark data analysisWho this book is forIf you are a data scientist or a data analyst who wants to learn how to perform data analysis using Scala, this book is for you. All you need is knowledge of the basic fundamentals of Scala programming.
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Data Analysis BriefBook
This BriefBook is a much extended glossary or a much condensed handbook, depending on the way one looks at it. In encyclopedic format, it covers subjects in statistics, computing, analysis, and related fields, resulting in a book that is both an introduction and a reference for scientists and engineers, especially experimental physicists dealing with data analysis.
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Introduction to Data Analysis
Covering the general process of data analysis to finding, collecting, organizing, and presenting data, this book offers a complete introduction to the fundamentals of data analysis. Using real-world case studies as illustrations, it helps readers understand theories behind and develop techniques for conducting quantitative, qualitative, and mixed methods data analysis. With an easy-to-follow organization and clear, jargon-free language, it helps readers not only become proficient data analysts, but also develop the critical thinking skills necessary to assess analyses presented by others in both academic research and the popular media. It includes advice on: - Data analysis frameworks - Validity and credibility of data - Sampling techniques - Data management - The big data phenomenon - Data visualisation - Effective data communication Whether you are new to data analysis or looking for a quick-reference guide to key principles of the process, this book will help you uncover nuances, complexities, patterns, and relationships among all types of data.
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Data Analysis and Presentation Skills
Data Analysis and Presentation Skills: An Introduction for the Life and Medical Sciences is an invaluable text allowing students to develop appropriate key skills when designing experiments, generating results, analysing data and ultimately presenting findings to academics and referees. Taking a hands-on approach, each of these key areas is introduced clearly and carefully, showing how to access and evaluate information using a variety of resources. Basic analytical theory is gradually introduced alongside practical applications to enhance student understanding. The reader is shown how to present data in charts using Microsoft Excel and statistical analysis is carefully explained showing clearly how to manipulate data in spreadsheets and analyse the results using commonly used tests. A section is also included on the use of PowerPoint as well as giving advice on how to prepare a short talk or seminar. Includes numerous relevant examples and case studies drawn from the Life SciencesGuidance on how to complete and present practical and project work through to postgraduate dissertation.Clear step-by-step introduction to Microsoft Excel, presentation skills and statistical analysis.Invaluable for all students within the Life and Medical Sciences
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Data Analysis Using SQL and Excel
A practical guide to data mining using SQL and Excel Data Analysis Using SQL and Excel, 2nd Edition shows you how to leverage the two most popular tools for data query and analysis—SQL and Excel—to perform sophisticated data analysis without the need for complex and expensive data mining tools. Written by a leading expert on business data mining, this book shows you how to extract useful business information from relational databases. You'll learn the fundamental techniques before moving into the "where" and "why" of each analysis, and then learn how to design and perform these analyses using SQL and Excel. Examples include SQL and Excel code, and the appendix shows how non-standard constructs are implemented in other major databases, including Oracle and IBM DB2/UDB. The companion website includes datasets and Excel spreadsheets, and the book provides hints, warnings, and technical asides to help you every step of the way. Data Analysis Using SQL and Excel, 2nd Edition shows you how to perform a wide range of sophisticated analyses using these simple tools, sparing you the significant expense of proprietary data mining tools like SAS. Understand core analytic techniques that work with SQL and ExcelEnsure your analytic approach gets you the results you needDesign and perform your analysis using SQL and Excel Data Analysis Using SQL and Excel, 2nd Edition shows you how to best use the tools you already know to achieve expert results.
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Introduction to Data Analysis with R for Forensic Scientists
Statistical methods provide a logical, coherent framework in which data from experimental science can be analyzed. However, many researchers lack the statistical skills or resources that would allow them to explore their data to its full potential. Introduction to Data Analysis with R for Forensic Sciences minimizes theory and mathematics and focuses on the application and practice of statistics to provide researchers with the dexterity necessary to systematically analyze data discovered from the fruits of their research.Using traditional techniques and employing examples and tutorials with real data collected from experiments, this book presents the following critical information necessary for researchers: A refresher on basic statistics and an introduction to RConsiderations and techniques for the visual display of data through graphicsAn overview of statistical hypothesis tests and the reasoning behind themA comprehensive guide to the use of the linear model, the foundation of most statistics encounteredAn introduction to extensions to the linear model for commonly encountered scenarios, including logistic and Poisson regressionInstruction on how to plan and design experiments in a way that minimizes cost and maximizes the chances of finding differences that may existFocusing on forensic examples but useful for anyone working in a laboratory, this volume enables researchers to get the most out of their experiments by allowing them to cogently analyze the data they have collected, saving valuable time and effort.
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Introduction to Engineering and Scientific Computing with Python
As more and more engineering departments and companies choose to use Python, this book provides an essential introduction to this open-source, free-to-use language. Expressly designed to support first-year engineering students, this book covers engineering and scientific calculations, Python basics, and structured programming.Based on extensive teaching experience, the text uses practical problem solving as a vehicle to teach Python as a programming language. By learning computing fundamentals in an engaging and hands-on manner, it enables the reader to apply engineering and scientific methods with Python, focusing this general language to the needs of engineers and the problems they are required to solve on a daily basis. Rather than inundating students with complex terminology, this book is designed with a leveling approach in mind, enabling students at all levels to gain experience and understanding of Python. It covers such topics as structured programming, graphics, matrix operations, algebraic equations, differential equations, and applied statistics. A comprehensive chapter on working with data brings this book to a close.This book is an essential guide to Python, which will be relevant to all engineers, particularly undergraduate students in their first year. It will also be of interest to professionals and graduate students looking to hone their programming skills, and apply Python to engineering and scientific contexts.
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Building Machine Learning Systems with Python
Get more from your data by creating practical machine learning systems with PythonKey FeaturesDevelop your own Python-based machine learning systemDiscover how Python offers multiple algorithms for modern machine learning systemsExplore key Python machine learning libraries to implement in your projectsBook DescriptionMachine learning allows systems to learn things without being explicitly programmed to do so. Python is one of the most popular languages used to develop machine learning applications, which take advantage of its extensive library support. This third edition of Building Machine Learning Systems with Python addresses recent developments in the field by covering the most-used datasets and libraries to help you build practical machine learning systems.Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python, being a dynamic language, allows for fast exploration and experimentation. This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and being introduced to libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling and creating recommendation systems. With Building Machine Learning Systems with Python, you’ll gain the tools and understanding required to build your own systems, all tailored to solve real-world data analysis problems.By the end of this book, you will be able to build machine learning systems using techniques and methodologies such as classification, sentiment analysis, computer vision, reinforcement learning, and neural networks.What you will learnBuild a classification system that can be applied to text, images, and soundEmploy Amazon Web Services (AWS) to run analysis on the cloudSolve problems related to regression using scikit-learn and TensorFlowRecommend products to users based on their past purchasesUnderstand different ways to apply deep neural networks on structured dataAddress recent developments in the field of computer vision and reinforcement learningWho this book is forBuilding Machine Learning Systems with Python is for data scientists, machine learning developers, and Python developers who want to learn how to build increasingly complex machine learning systems. You will use Python's machine learning capabilities to develop effective solutions. Prior knowledge of Python programming is expected.
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Quantitative Data Analysis
This book offers postgraduate and early career researchers in accounting and information systems a guide to choosing, executing and reporting appropriate data analysis methods to answer their research questions. It provides readers with a basic understanding of the steps that each method involves, and of the facets of the analysis that require special attention. Rather than presenting an exhaustive overview of the methods or explaining them in detail, the book serves as a starting point for developing data analysis skills: it provides hands-on guidelines for conducting the most common analyses and reporting results, and includes pointers to more extensive resources. Comprehensive yet succinct, the book is brief and written in a language that everyone can understand - from students to those employed by organizations wanting to study the context in which they work. It also serves as a refresher for researchers who have learned data analysis techniques previously but who need a reminder for the specific study they are involved in.
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Statistical Analysis of Network Data with R
The new edition of this book provides an easily accessible introduction to the statistical analysis of network data using R. It has been fully revised and can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. The new edition of this book includes an overhaul to recent changes in igraph. The material in this book is organized to flow from descriptive statistical methods to topics centered on modeling and inference with networks, with the latter separated into two sub-areas, corresponding first to the modeling and inference of networks themselves, and then, to processes on networks. The book begins by covering tools for the manipulation of network data. Next, it addresses visualization and characterization of networks. The book then examines mathematical and statistical network modeling. This is followed by a special case of network modeling wherein the network topology must be inferred. Network processes, both static and dynamic are addressed in the subsequent chapters. The book concludes by featuring chapters on network flows, dynamic networks, and networked experiments. Statistical Analysis of Network Data with R, 2nd Ed. has been written at a level aimed at graduate students and researchers in quantitative disciplines engaged in the statistical analysis of network data, although advanced undergraduates already comfortable with R should find the book fairly accessible as well.
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Complex Network Analysis in Python
Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.
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Dancing with Python
Develop skills in Python and Quantum Computing by implementing exciting algorithms, mathematical functions, classical searching, data analysis, plotting data, machine learning techniques, and quantum circuits.Key FeaturesCreate quantum circuits and algorithms using Qiskit and run them on quantum computing hardware and simulatorsLearn the Pythonic way to write elegant and efficient codeDelve into Python's advanced features, including machine learning, analyzing data, and searchingBook DescriptionDancing with Python helps you learn Python and quantum computing in a practical way. It will help you explore how to work with numbers, strings, collections, iterators, and files.The book goes beyond functions and classes and teaches you to use Python and Qiskit to create gates and circuits for classical and quantum computing. Learn how quantum extends traditional techniques using the Grover Search Algorithm and the code that implements it. Dive into some advanced and widely used applications of Python and revisit strings with more sophisticated tools, such as regular expressions and basic natural language processing (NLP). The final chapters introduce you to data analysis, visualizations, and supervised and unsupervised machine learning.By the end of the book, you will be proficient in programming the latest and most powerful quantum computers, the Pythonic way.What you will learnExplore different quantum gates and build quantum circuits with Qiskit and PythonWrite succinct code the Pythonic way using magic methods, iterators, and generatorsAnalyze data, build basic machine learning models, and plot the resultsSearch for information using the quantum Grover Search AlgorithmOptimize and test your code to run efficientlyWho this book is forThe book will help you get started with coding for Python and Quantum Computing. Basic familiarity with algebra, geometry, trigonometry, and logarithms is required as the book does not cover the detailed mathematics and theory of quantum computing. You can check out the author’s Dancing with Qubits book, also published by Packt, for an approachable and comprehensive introduction to quantum computing.
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R 947.00
Data Analysis for Chemistry
Chemical data analysis, with aspects of metrology in chemistry and chemometrics, is an evolving discipline where new and better ways of doing things are constantly being developed. This book makes data analysis simple by demystifying the language and whenever possible giving unambiguous ways of doing things. Based on author D. Brynn Hibberts lectures on data analysis to undergraduates and graduate students, Data Analysis for Chemistry covers topics including measurements, means and confidence intervals, hypothesis testing, analysis of variance, and calibration models. The end result is a compromise between recipes of how to perform different aspects of data analysis, and basic information on the background principles behind the recipes to be performed. An entry level book targeted at learning andteaching undergraduate data analysis, Data Analysis for Chemistry makes it easy for readers to find the information they are seeking to perform the data analysis they think they need.
R 1,302.85
Essential First Steps to Data Analysis
Carol S. Parke′s Essential First Steps to Data Analysis: Scenario-Based Examples Using SPSS provides instruction and guidance on preparing quantitative data sets prior to answering a study′s research questions. Such preparation may involve data management and manipulation tasks, data organization, structural changes to the data files, or conducting preliminary analysis. Twelve research-based scenarios are used to present the content. Each scenario tells the "story" of a researcher who thoroughly examined their data and the decisions they made along the way. The scenario begins with a description of the researcher′s study and his/her data file(s), then describes the issues the researcher must address, explains why they are important, shows how SPSS was used to address the issues and prepare data, and shares the researcher′s reflections and any additional decision-making. Finally, each scenario ends with the researcher′s written summary of the procedures and outcomes from the initial data preparation or analysis.
R 2,363.48
Data Analysis for Chemistry
Chemical data analysis, with aspects of metrology in chemistry and chemometrics, is an evolving discipline where new and better ways of doing things are constantly being developed. This book makes data analysis simple by demystifying the language and whenever possible giving unambiguous ways of doing things. Based on author D. Brynn Hibberts lectures on data analysis to undergraduates and graduate students, Data Analysis for Chemistry covers topics including measurements, means and confidence intervals, hypothesis testing, analysis of variance, and calibration models. The end result is a compromise between recipes of how to perform different aspects of data analysis, and basic information on the background principles behind the recipes to be performed. An entry level book targeted atlearning and teaching undergraduate data analysis, Data Analysis for Chemistry makes it easy for readers to find the information they are seeking to perform the data analysis they think they need.
R 3,929.05
Supervised Learning with Python
Gain a thorough understanding of supervised learning algorithms by developing use cases with Python. You will study supervised learning concepts, Python code, datasets, best practices, resolution of common issues and pitfalls, and practical knowledge of implementing algorithms for structured as well as text and images datasets.You’ll start with an introduction to machine learning, highlighting the differences between supervised, semi-supervised and unsupervised learning. In the following chapters you’ll study regression and classification problems, mathematics behind them, algorithms like Linear Regression, Logistic Regression, Decision Tree, KNN, Naïve Bayes, and advanced algorithms like Random Forest, SVM, Gradient Boosting and Neural Networks. Python implementation is provided for all the algorithms. You’ll conclude with an end-to-end model development process including deployment and maintenance of the model.After reading Supervised Learning with Python you’ll have a broad understanding of supervised learning and its practical implementation, and be able to run the code and extend it in an innovative manner.What You'll LearnReview the fundamental building blocks and concepts of supervised learning using PythonDevelop supervised learning solutions for structured data as well as text and images Solve issues around overfitting, feature engineering, data cleansing, and cross-validation for building best fit modelsUnderstand the end-to-end model cycle from business problem definition to model deployment and model maintenance Avoid the common pitfalls and adhere to best practices while creating a supervised learning model using PythonWho This Book Is ForData scientists or data analysts interested in best practices and standards for supervised learning, and using classification algorithms and regression techniques to develop predictive models.
R 1,254.00
Hands-On Markov Models with Python
Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearnKey FeaturesBuild a variety of Hidden Markov Models (HMM)Create and apply models to any sequence of data to analyze, predict, and extract valuable insightsUse natural language processing (NLP) techniques and 2D-HMM model for image segmentationBook DescriptionHidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone.Once you’ve covered the basic concepts of Markov chains, you’ll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you’ll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you’ll explore the Bayesian approach of inference and learn how to apply it in HMMs.In further chapters, you’ll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You’ll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you’ll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading.By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects.What you will learnExplore a balance of both theoretical and practical aspects of HMMImplement HMMs using different datasets in Python using different packagesUnderstand multiple inference algorithms and how to select the right algorithm to resolve your problemsDevelop a Bayesian approach to inference in HMMsImplement HMMs in finance, natural language processing (NLP), and image processingDetermine the most likely sequence of hidden states in an HMM using the Viterbi algorithmWho this book is forHands-On Markov Models with Python is for you if you are a data analyst, data scientist, or machine learning developer and want to enhance your machine learning knowledge and skills. This book will also help you build your own hidden Markov models by applying them to any sequence of data.Basic knowledge of machine learning and the Python programming language is expected to get the most out of the book
R 1,335.00
Applied Multivariate Data Analysis
Multivariate analysis plays an important role in the understanding of complex data sets requiring simultaneous examination of all variables. Breaking through the apparent disorder of the information, it provides the means for both describing and exploring data, aiming to extract the underlying patterns and structure. This intermediate-level textbook introduces the reader to the variety of methods by which multivariate statistical analysis may be undertaken. Now in its 2nd edition, 'Applied Multivariate Data Analysis' has been fully expanded and updated, including major chapter revisions as well as new sections on neural networks and random effects models for longitudinal data. Maintaining the easy-going style of the first edition, the authors provide clear explanations of each technique, as well as supporting figures and examples, and minimal technical jargon. With extensive exercises following every chapter, 'Applied Multivariate Data Analysis' is a valuable resource for students on applied statistics courses and applied researchers in many disciplines.
R 2,343.00
Tabular Modeling with SQL Server 2016 Analysis Services Cookbook
Expert tabular modeling techniques for building and deploying cutting-edge business analytical reporting solutionsAbout This Book• Build and deploy Tabular Model projects from relational data sources• Leverage DAX and create high-performing calculated fields and measures• Create ad-hoc reports based on a Tabular Model solution• Useful tips to monitor and optimize your tabular solutionsWho This Book Is ForThis book is for SQL BI professionals and Architects who want to exploit the full power of the new Tabular models in Analysis Services. Some knowledge of previous versions of Analysis services would be helpful but is not essential.What You Will Learn• Learn all about Tabular services mode and how it speeds up development• Build solutions using sample datasets• Explore built-in actions and transitions in SSAS 2016• Implement row-column, and role-based security in a Tabular Data model• Realize the benefits of in-memory and DirectQuery deployment modes• Get up to date with the new features added to SQL Server 2016 Analysis Services• Optimize Data Models and Relationships UsageIn DetailSQL Server Analysis Service (SSAS) has been widely used across multiple businesses to build smart online analytical reporting solutions. It includes two different types of modeling for analysis services: Tabular and Multi Dimensional. This book covers Tabular modeling, which uses tables and relationships with a fast in-memory engine to provide state of the art compression algorithms and query performance.The book begins by quickly taking you through the concepts required to model tabular data and set up the necessary tools and services. As you learn to create tabular models using tools such as Excel and Power View, you'll be shown various strategies to deploy your model on the server and choose a query mode (In-memory or DirectQuery) that best suits your reporting needs.You'll also learn how to implement key and newly introduced DAX functions to create calculated columns and measures for your model data. Last but not least, you'll be shown techniques that will help you administer and secure your BI implementation along with some widely used tips and tricks to optimize your reporting solution.By the end of this book, you'll have gained hands-on experience with the powerful new features that have been added to Tabular models in SSAS 2016 and you'll be able to improve user satisfaction with faster reports and analytical queries.Style and approachThis book takes a practical, recipe-based approach where each recipe lists the steps to address or implement a solution. You will be provided with several approaches to creating a business intelligence semantic model using analysis services.
R 2,084.00
