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Mastering Predictive Analytics with Python
by Joseph BabcockExploit the power of data in your business by building advanced predictive modeling applications with Python About This Book * Master open source Python tools to build sophisticated predictive models * Learn to identify the right machine learning algorithm for your problem with this forward-thinking guide * Grasp the major methods of predictive modeling and move beyond the basics to a deeper level of understanding Who This Book Is For This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python. You're expected to have basic development experience with Python. What You Will Learn * Gain an insight into components and design decisions for an analytical application * Master the use Python notebooks for exploratory data analysis and rapid prototyping * Get to grips with applying regression, classification, clustering, and deep learning algorithms * Discover the advanced methods to analyze structured and unstructured data * Find out how to deploy a machine learning model in a production environment * Visualize the performance of models and the insights they produce * Scale your solutions as your data grows using Python * Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis In Detail The volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations. In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to life Style and approach This book emphasizes on explaining methods through example data and code, showing you templates that you can quickly adapt to your own use cases. It focuses on both a practical application of sophisticated algorithms and the intuitive understanding necessary to apply the correct method to the problem at hand. Through visual examples, it also demonstrates how to convey insights through insightful charts and reporting.
Mastering Predictive Analytics with R
by Rui Miguel ForteThis book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed to be a reference for experienced professionals wanting to brush up on the details of a particular type of predictive model. Mastering Predictive Analytics with R assumes familiarity with only the fundamentals of R, such as the main data types, simple functions, and how to move data around. No prior experience with machine learning or predictive modeling is assumed, however you should have a basic understanding of statistics and calculus at a high school level.
Mastering Predictive Analytics with R
by Rui Miguel Forte<P><P>Master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts <P><P>About This Book <P><P>Grasp the major methods of predictive modeling and move beyond black box thinking to a deeper level of understanding <P><P>Leverage the flexibility and modularity of R to experiment with a range of different techniques and data types <P><P>Packed with practical advice and tips explaining important concepts and best practices to help you understand quickly and easily <P><P>Who This Book Is For <P><P>This book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed to be a reference for experienced professionals wanting to brush up on the details of a particular type of predictive model. Mastering Predictive Analytics with R assumes familiarity with only the fundamentals of R, such as the main data types, simple functions, and how to move data around. No prior experience with machine learning or predictive modeling is assumed, however you should have a basic understanding of statistics and calculus at a high school level. <P><P>What You Will Learn <P><P>Master the steps involved in the predictive modeling process <P><P>Learn how to classify predictive models and distinguish which models are suitable for a particular problem <P><P>Understand how and why each predictive model works <P><P>Recognize the assumptions, strengths, and weaknesses of a predictive model, and that there is no best model for every problem <P><P>Select appropriate metrics to assess the performance of different types of predictive model <P><P>Diagnose performance and accuracy problems when they arise and learn how to deal with them <P><P>Grow your expertise in using R and its diverse range of packages <P><P>In Detail <P><P>R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems. <P><P>This book is designed to be both a guide and a reference for moving beyond the basics of predictive modeling. The book begins with a dedicated chapter on the language of models and the predictive modeling process. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real world data sets. <P><P>By the end of this book, you will have explored and tested the most popular modeling techniques in use on real world data sets and mastered a diverse range of techniques in predictive analytics.
Mastering Predictive Analytics with R - Second Edition
by Rui Miguel Forte James D. MillerMaster the craft of predictive modeling in R by developing strategy, intuition, and a solid foundation in essential concepts About This Book • Grasping the major methods of predictive modeling and moving beyond black box thinking to a deeper level of understanding • Leveraging the flexibility and modularity of R to experiment with a range of different techniques and data types • Packed with practical advice and tips explaining important concepts and best practices to help you understand quickly and easily Who This Book Is For Although budding data scientists, predictive modelers, or quantitative analysts with only basic exposure to R and statistics will find this book to be useful, the experienced data scientist professional wishing to attain master level status , will also find this book extremely valuable.. This book assumes familiarity with the fundamentals of R, such as the main data types, simple functions, and how to move data around. Although no prior experience with machine learning or predictive modeling is required, there are some advanced topics provided that will require more than novice exposure. What You Will Learn • Master the steps involved in the predictive modeling process • Grow your expertise in using R and its diverse range of packages • Learn how to classify predictive models and distinguish which models are suitable for a particular problem • Understand steps for tidying data and improving the performing metrics • Recognize the assumptions, strengths, and weaknesses of a predictive model • Understand how and why each predictive model works in R • Select appropriate metrics to assess the performance of different types of predictive model • Explore word embedding and recurrent neural networks in R • Train models in R that can work on very large datasets In Detail R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems. The book begins with a dedicated chapter on the language of models and the predictive modeling process. You will understand the learning curve and the process of tidying data. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real-world datasets. How do you train models that can handle really large datasets? This book will also show you just that. Finally, you will tackle the really important topic of deep learning by implementing applications on word embedding and recurrent neural networks. By the end of this book, you will have explored and tested the most popular modeling techniques in use on real- world datasets and mastered a diverse range of techniques in predictive analytics using R. Style and approach This book takes a step-by-step approach in explaining the intermediate to advanced concepts in predictive analytics. Every concept is explained in depth, supplemented with practical examples applicable in a real-world setting.
Mastering Predictive Analytics with scikit-learn and TensorFlow: Implement machine learning techniques to build advanced predictive models using Python
by Alvaro FuentesLearn advanced techniques to improve the performance and quality of your predictive modelsKey FeaturesUse ensemble methods to improve the performance of predictive analytics modelsImplement feature selection, dimensionality reduction, and cross-validation techniquesDevelop neural network models and master the basics of deep learningBook DescriptionPython is a programming language that provides a wide range of features that can be used in the field of data science. Mastering Predictive Analytics with scikit-learn and TensorFlow covers various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems.This book starts with ensemble methods and their features. You will see that scikit-learn provides tools for choosing hyperparameters for models. As you make your way through the book, you will cover the nitty-gritty of predictive analytics and explore its features and characteristics. You will also be introduced to artificial neural networks and TensorFlow, and how it is used to create neural networks. In the final chapter, you will explore factors such as computational power, along with improvement methods and software enhancements for efficient predictive analytics.By the end of this book, you will be well-versed in using deep neural networks to solve common problems in big data analysis.What you will learnUse ensemble algorithms to obtain accurate predictionsApply dimensionality reduction techniques to combine features and build better modelsChoose the optimal hyperparameters using cross-validationImplement different techniques to solve current challenges in the predictive analytics domainUnderstand various elements of deep neural network (DNN) modelsImplement neural networks to solve both classification and regression problemsWho this book is forMastering Predictive Analytics with scikit-learn and TensorFlow is for data analysts, software engineers, and machine learning developers who are interested in implementing advanced predictive analytics using Python. Business intelligence experts will also find this book indispensable as it will teach them how to progress from basic predictive models to building advanced models and producing more accurate predictions. Prior knowledge of Python and familiarity with predictive analytics concepts are assumed.
Mastering Prezi for Business Presentations
by Russell Anderson-WilliamsAccompanied by plenty of tips and tricks, this tutorial style book has ample examples and screenshots to ease your learning curve. If you use Prezi in business and want to take your presentations to the next level, or if you want to become the office Prezi guru, this book is for you.
Mastering Probabilistic Graphical Models Using Python
by Abinash Panda Ankur AnkanIf you are a researcher or a machine learning enthusiast, or are working in the data science field and have a basic idea of Bayesian learning or probabilistic graphical models, this book will help you to understand the details of graphical models and use them in your data science problems.
Mastering Project Discovery: Successful Discipline in Engineering and Analytics Projects
by Elliot Bendoly Daniel Bachrach Kathy Koontz Porter SchermerhornIntroducing a comprehensive approach to invigorate project leadership, this book provides a framework – the OUtCoMES Cycle – for developing, managing, advancing, and optimizing engineering and analytics projects.All too often, issues of moral hazard and completion bias prevent engineering and analytics managers and team leaders from asking the critical question 'What’s the problem?', before committing time, energy, and resources to solve it. This book draws attention to the definition, structuring, option consideration and ultimately the addressing of the right problems, exploring the OUtCoMES Cycle framework that facilitates and energizes systematic thinking, knowledge sharing, and on-the-fly adjustment with an explicit focus on the maximization of value and ROI. Each chapter includes discussions and lessons in analytical and engineering problem identification, problem structuring, iterative problem development (mental and computational) and problem resolution, at least three embedded real-world case studies, and a closing 'Practitioner’s Recap' to contextualize key chapter takeaways.Written by a team of established academic scholars and practicing analysts and engineers, this is an accessible and culture-shifting action guide for instructors interested in training the next generation of project and analytics leaders, students of analytics and engineering, as well as practicing project leaders and principals.
Mastering Proxmox
by Wasim AhmedThis is not an instructional guide, but a practical, scenario-based book which guides you through everything you need to know in a practical manner by letting you build your own cluster. By the end of the book, you will have a fully functional Proxmox cluster setup at your disposal and have the knowledge to replicate virtualization solutions .If you already know what the word "virtualization" means and you are ready to stand out from the crowd equipped with the unique ability to design and implement a rock-solid virtualized network environment using Proxmox, then you have just picked up the only book you will need. Linux system administration experience together with knowledge of networking and virtualization concepts is assumed. This book is also useful if you are already using Proxmox and simply want to master its advanced features.
Mastering Proxmox - Second Edition
by Wasim AhmedThe book targets Linux and system administrators and professionals working in IT teams who would like to design and implement a enterprise-quality virtualized environment using Proxmox.Knowledge of networking and virtualization concepts is assumed.
Mastering Puppet
by Thomas UphillIn Detail Puppet is a configuration management system written for system administrators to manage a large number of systems efficiently and help maintain order. Mastering Puppetdeals with the issues faced in larger deployments such as scaling and duplicate resource definitions. It will show you how to fit Puppet into your organization and keep everyone working. The concepts presented can be adapted to suit any size organization. This book starts with setting up and installing Puppet in your organization and then moves on to implementing version control in Puppet, creating custom modules, and extending your Puppet infrastructure. Finally, you will learn tips and tricks that are useful when troubleshooting Puppet and the best practices to make you a pro. Approach Presented in an easy-to-follow, step-by-step tutorial format and packed with examples, this book will lead you through making the best out of Puppet in an enterprise environment. Who this book is for If you are a system administrator or developer who has used Puppet in production and are looking for ways to easily use Puppet in an enterprise environment, this book is for you. This book assumes an intermediate knowledge of Puppet and is intended for those writing modules or deploying Puppet in an enterprise environment.
Mastering Puppet 5: Optimize enterprise-grade environment performance with Puppet
by Jason Southgate Ryan Russell-YatesLeverage Puppet 5 for medium to large scale enterprise deployment.Key FeaturesUse and deploy Puppet 5, irrespective the size of your organizationScaling, performance improvements, and managing multiple developer requestsTroubleshooting techniques, tips and tricks to make the most of Puppet 5Book DescriptionPuppet is a configuration management system and a language written for and by system administrators to manage a large number of systems efficiently and prevent configuration drift.The core topics this book addresses are Puppet's latest features and mastering Puppet Enterprise. You will begin by writing a new Puppet module, gaining an understanding of the guidelines and style of the Puppet community. Following on from this, you will take advantage of the roles and profiles pattern, and you will learn how to structure your code. Next, you will learn how to extend Puppet and write custom facts, functions, types, and providers in Ruby, and also use the new features of Hiera 5. You will also learn how to configure the new Code Manager component, and how to ensure code is automatically deployed to (multiple) Puppet servers. Next, you will learn how to integrate Puppet with Jenkins and Git to build an effective workflow for multiple teams, and use the new Puppet Tasks feature and the latest Puppet Orchestrator language extensions. Finally, you will learn how to scale and troubleshoot Puppet.By the end of the book, you will be able to deal with problems of scale and exceptions in your code, automate workflows, and support multiple developers working simultaneously.What you will learnSolve problems using modules and the roles and profiles patternExtend Puppet with custom facts, functions, types, and providersUse Hiera 5 and Code Manager/r10k to separate code from dataContinuously integrate your code using Jenkins, Git and automated testingUse exported resources and the new Puppet Orchestration featuresExplore Puppet Discovery features and their useTroubleshoot various parts of the Puppet Enterprise infrastructureScale up and scale out Puppet infrastructure using various techniquesWho this book is forIf you are a system administrator or developer who has used Puppet in production and are looking for ways to easily use Puppet in an enterprise environment, this book is for you. Some knowledge of writing simple configuration management modules would be necessary.
Mastering Puppet - Second Edition
by Thomas UphillMaster Puppet for configuration management of your systems in an enterprise deployment About This Book * This book is an advanced guide to using and deploying Puppet 4 in your organization with a special focus on issues faced in larger enterprise deployments * From an experienced author, learn to deal with scaling, performance, and multiple developers with the help of real-world examples * This is the most up-to-date guide on Puppet, and covers the advanced concepts of Puppet 4 Who This Book Is For This book is for those who have intermediate knowledge of Puppet and are looking to deploy it in their environment. Some idea how to write simple modules for configuration management with Puppet is a prerequisite for this book. What You Will Learn * Scale out your Puppet infrastructure using proxying techniques * Automate your code promotion workflow using Git and r10k * Solve real-world problems using public modules from the Puppet Forge * Use Hiera to separate the data of your configuration from the code of your configuration * Write your own custom facts in Ruby * Extend Puppet with your own custom facts, modules, and types * Use exported resources to orchestrate change between machines * Debug a puppetserver using Java techniques In Detail Puppet is a configuration management system and a language. It was written for and by system administrators to manage large numbers of systems efficiently and prevent configuration drifts. Mastering Puppet deals with the issues faced when scaling out Puppet to handle large numbers of nodes. It will show you how to fit Puppet into your enterprise and allow many developers to work on your Puppet code simultaneously. In addition, you will learn to write custom facts and roll your own modules to solve problems. Next, popular options for performing reporting and orchestration tasks will be introduced in this book. Moving over to troubleshooting techniques, which will be very useful. The concepts presented are useful to any size organization. By the end of the book, you will know how to deal with problems of scale and exceptions in your code, automate workflows, and support multiple developers working simultaneously. Style and approach This book is a step-wise guide packed with examples to help you configure complex systems in Puppet.
Mastering PyCharm
by Quazi Nafiul IslamUse PyCharm with fluid efficiency to write idiomatic python codeAbout This BookUnderstand how PyCharm works and how you can leverage its strength to develop applications quicklyMaster PyCharm's editor to get a fast workflowFull of examples and illustrations that focus on the practical aspects of using PyCharmWho This Book Is ForIf you know PyCharm but want to understand it better and leverage its more powerful but less obvious tool set, this is the book for you. Serving as a launch pad for those who want to master PyCharm and completely harness its best features, it would be helpful if you were familiar with some of Python's most prominent tools such as virtualenv and Python's popular docstring formats such as reStructuredText and EpyType.What You Will LearnUnderstand the internal workings of the IntelliJ PlatformLeverage PyCharm's powerful search tools, and learn which ones are the best for you and your workflowCustomize PyCharm's enhanced Python interpreter and its inbuilt terminalDevelop web applications quickly and easily with different frameworks such as Flask and DjangoUnderstand how code completion works in PyCharm for Python and JavaScriptMake calls to external tools, and use PyCharm macros to your advantageIn DetailPyCharm is addictive, with powerful and configurable code completion, superb editing tools, top-notch support, diverse plugins, and a vibrant ecosystem to boot. Learning how PyCharm works and maximising the synergy of its powerful tools will help you to rapidly develop applications.From leveraging the power of the editor to understanding PyCharm's internals, this book will give you a comprehensive view of PyCharm and allow you to make your own choices about which workflow and tools are best for you.You will start by getting comfortable with PyCharm and making it look exactly like you want. You can customize the tools and taskbars to suit individual developers' coding styles. You also learn how to assign keyboard shortcuts. You will master debugging by inserting breakpoints, collecting runtime data, and debugging from the console. You will understand how PyCharm works underneath and how plugins such as Codemap, Vim, Bitbucket, Assets compressor, markdown, bash file, shortcut translator, and .gitignore leverage the power of the IntelliJ platform.You will become comfortable using the VCS interface in PyCharm and see the benefits of using it for some simple tasks as well as some more complex tasks such as partial commits using changelists.You will take an in-depth look at the various tools in PyCharm, improving your workflow drastically. Finally, you will deploy powerful PyCharm tools for Django, Flask, GAE, and Pyramid Development, becoming well acquainted with PyCharm's toolset for web development with popular platforms.Packed with insider tricks, this book will help you boost productivity with PyCharm.Style and approachAn easy-to-follow guide with plenty of examples and screenshots. Each topic starts off with the goal of enhancing or changing a part of PyCharm to make it suit your needs.
Mastering Python
by Rick Van HattemMaster the art of writing beautiful and powerful Python by using all of the features that Python 3.5 offers About This Book * Become familiar with the most important and advanced parts of the Python code style * Learn the trickier aspects of Python and put it in a structured context for deeper understanding of the language * Offers an expert's-eye overview of how these advanced tasks fit together in Python as a whole along with practical examples Who This Book Is For Almost anyone can learn to write working script and create high quality code but they might lack a structured understanding of what it means to be 'Pythonic'. If you are a Python programmer who wants to code efficiently by getting the syntax and usage of a few intricate Python techniques exactly right, this book is for you. What You Will Learn * Create a virtualenv and start a new project * Understand how and when to use the functional programming paradigm * Get familiar with the different ways the decorators can be written in * Understand the power of generators and coroutines without digressing into lambda calculus * Create metaclasses and how it makes working with Python far easier * Generate HTML documentation out of documents and code using Sphinx * Learn how to track and optimize application performance, both memory and cpu * Use the multiprocessing library, not just locally but also across multiple machines * Get a basic understanding of packaging and creating your own libraries/applications In Detail Python is a dynamic programming language. It is known for its high readability and hence it is often the first language learned by new programmers. Python being multi-paradigm, it can be used to achieve the same thing in different ways and it is compatible across different platforms. Even if you find writing Python code easy, writing code that is efficient, easy to maintain, and reuse is not so straightforward. This book is an authoritative guide that will help you learn new advanced methods in a clear and contextualised way. It starts off by creating a project-specific environment using venv, introducing you to different Pythonic syntax and common pitfalls before moving on to cover the functional features in Python. It covers how to create different decorators, generators, and metaclasses. It also introduces you to functools.wraps and coroutines and how they work. Later on you will learn to use asyncio module for asynchronous clients and servers. You will also get familiar with different testing systems such as py.test, doctest, and unittest, and debugging tools such as Python debugger and faulthandler. You will learn to optimize application performance so that it works efficiently across multiple machines and Python versions. Finally, it will teach you how to access C functions with a simple Python call. By the end of the book, you will be able to write more advanced scripts and take on bigger challenges. Style and Approach This book is a comprehensive guide that covers advanced features of the Python language, and communicate them with an authoritative understanding of the underlying rationale for how, when, and why to use them.
Mastering Python: Write powerful and efficient code using the full range of Python's capabilities, 2nd Edition
by Rick van HattemUse advanced features of Python to write high-quality, readable code and packagesKey FeaturesExtensively updated for Python 3.10 with new chapters on design patterns, scientific programming, machine learning, and interactive PythonShape your scripts using key concepts like concurrency, performance optimization, asyncio, and multiprocessingLearn how advanced Python features fit together to produce maintainable codeBook DescriptionEven if you find writing Python code easy, writing code that is efficient, maintainable, and reusable is not so straightforward. Many of Python's capabilities are underutilized even by more experienced programmers. Mastering Python, Second Edition, is an authoritative guide to understanding advanced Python programming so you can write the highest quality code. This new edition has been extensively revised and updated with exercises, four new chapters and updates up to Python 3.10.Revisit important basics, including Pythonic style and syntax and functional programming. Avoid common mistakes made by programmers of all experience levels. Make smart decisions about the best testing and debugging tools to use, optimize your code's performance across multiple machines and Python versions, and deploy often-forgotten Python features to your advantage. Get fully up to speed with asyncio and stretch the language even further by accessing C functions with simple Python calls. Finally, turn your new-and-improved code into packages and share them with the wider Python community.If you are a Python programmer wanting to improve your code quality and readability, this Python book will make you confident in writing high-quality scripts and taking on bigger challengesWhat you will learnWrite beautiful Pythonic code and avoid common Python coding mistakesApply the power of decorators, generators, coroutines, and metaclassesUse different testing systems like pytest, unittest, and doctestTrack and optimize application performance for both memory and CPU usageDebug your applications with PDB, Werkzeug, and faulthandlerImprove your performance through asyncio, multiprocessing, and distributed computingExplore popular libraries like Dask, NumPy, SciPy, pandas, TensorFlow, and scikit-learnExtend Python's capabilities with C/C++ libraries and system callsWho this book is forThis book will benefit more experienced Python programmers who wish to upskill, serving as a reference for best practices and some of the more intricate Python techniques. Even if you have been using Python for years, chances are that you haven't yet encountered every topic discussed in this book. A good understanding of Python programming is necessary
Mastering Python Data Analysis
by Magnus Vilhelm Persson Luiz Felipe MartinsBecome an expert at using Python for advanced statistical analysis of data using real-world examples About This Book * Clean, format, and explore data using graphical and numerical summaries * Leverage the IPython environment to efficiently analyze data with Python * Packed with easy-to-follow examples to develop advanced computational skills for the analysis of complex data Who This Book Is For If you are a competent Python developer who wants to take your data analysis skills to the next level by solving complex problems, then this advanced guide is for you. Familiarity with the basics of applying Python libraries to data sets is assumed. What You Will Learn * Read, sort, and map various data into Python and Pandas * Recognise patterns so you can understand and explore data * Use statistical models to discover patterns in data * Review classical statistical inference using Python, Pandas, and SciPy * Detect similarities and differences in data with clustering * Clean your data to make it useful * Work in Jupyter Notebook to produce publication ready figures to be included in reports In Detail Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. Ever imagined how to become an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? Well, look no further, this is the book you want! Through this comprehensive guide, you will explore data and present results and conclusions from statistical analysis in a meaningful way. You'll be able to quickly and accurately perform the hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making. You'll start off by learning about the tools available for data analysis in Python and will then explore the statistical models that are used to identify patterns in data. Gradually, you'll move on to review statistical inference using Python, Pandas, and SciPy. After that, we'll focus on performing regression using computational tools and you'll get to understand the problem of identifying clusters in data in an algorithmic way. Finally, we delve into advanced techniques to quantify cause and effect using Bayesian methods and you'll discover how to use Python's tools for supervised machine learning. Style and approach This book takes a step-by-step approach to reading, processing, and analyzing data in Python using various methods and tools. Rich in examples, each topic connects to real-world examples and retrieves data directly online where possible. With this book, you are given the knowledge and tools to explore any data on your own, encouraging a curiosity befitting all data scientists.
Mastering Python Data Visualization
by Kirthi RamanGenerate effective results in a variety of visually appealing charts using the plotting packages in PythonAbout This BookExplore various tools and their strengths while building meaningful representations that can make it easier to understand dataPacked with computational methods and algorithms in diverse fields of scienceWritten in an easy-to-follow categorical style, this book discusses some niche techniques that will make your code easier to work with and reuseWho This Book Is ForIf you are a Python developer who performs data visualization and wants to develop existing knowledge about Python to build analytical results and produce some amazing visual display, then this book is for you. A basic knowledge level and understanding of Python libraries is assumed.What You Will LearnGather, cleanse, access, and map data to a visual frameworkRecognize which visualization method is applicable and learn best practices for data visualizationGet acquainted with reader-driven narratives and author-driven narratives and the principles of perceptionUnderstand why Python is an effective tool to be used for numerical computation much like MATLAB, and explore some interesting data structures that come with itExplore with various visualization choices how Python can be very useful in computation in the field of finance and statisticsGet to know why Python is the second choice after Java, and is used frequently in the field of machine learningCompare Python with other visualization approaches using Julia and a JavaScript-based framework such as D3.jsDiscover how Python can be used in conjunction with NoSQL such as Hive to produce results efficiently in a distributed environmentIn DetailPython has a handful of open source libraries for numerical computations involving optimization, linear algebra, integration, interpolation, and other special functions using array objects, machine learning, data mining, and plotting. Pandas have a productive environment for data analysis. These libraries have a specific purpose and play an important role in the research into diverse domains including economics, finance, biological sciences, social science, health care, and many more. The variety of tools and approaches available within Python community is stunning, and can bolster and enhance visual story experiences.This book offers practical guidance to help you on the journey to effective data visualization. Commencing with a chapter on the data framework, which explains the transformation of data into information and eventually knowledge, this book subsequently covers the complete visualization process using the most popular Python libraries with working examples. You will learn the usage of Numpy, Scipy, IPython, MatPlotLib, Pandas, Patsy, and Scikit-Learn with a focus on generating results that can be visualized in many different ways. Further chapters are aimed at not only showing advanced techniques such as interactive plotting; numerical, graphical linear, and non-linear regression; clustering and classification, but also in helping you understand the aesthetics and best practices of data visualization. The book concludes with interesting examples such as social networks, directed graph examples in real-life, data structures appropriate for these problems, and network analysis.By the end of this book, you will be able to effectively solve a broad set of data analysis problems.Style and approachThe approach of this book is not step by step, but rather categorical. The categories are based on fields such as bioinformatics, statistical and machine learning, financial computation, and linear algebra. This approach is beneficial for the community in many different fields of work and also helps you learn how one approach can make sense across many fields
Mastering Python Design Patterns
by Sakis KasampalisThis book is for Python programmers with an intermediate background and an interest in design patterns implemented in idiomatic Python. Programmers of other languages who are interested in Python can also benefit from this book, but it would be better if they first read some introductory materials that explain how things are done in Python.
Mastering Python Design Patterns
by Sakis Kasampalis<P><P>Create various design patterns to master the art of solving problems using Python <P><P>About This Book <P><P>Simplify design pattern implementation using the power of Python <P><P>Each pattern is accompanied with a real-world example demonstrating its key features <P><P>This is an easy-to-follow guide focusing on the practical aspects of Python design patterns <P><P>Who This Book Is For <P><P>This book is for Python programmers with an intermediate background and an interest in design patterns implemented in idiomatic Python. Programmers of other languages who are interested in Python can also benefit from this book, but it would be better if they first read some introductory materials that explain how things are done in Python. <P><P>What You Will Learn <P><P>Explore Factory Method and Abstract Factory for object creation <P><P>Clone objects using the Prototype pattern <P><P>Make incompatible interfaces compatible using the Adapter pattern <P><P>Secure an interface using the Proxy pattern <P><P>Choose an algorithm dynamically using the Strategy pattern <P><P>Extend an object without subclassing using the Decorator pattern <P><P>Keep the logic decoupled from the UI using the MVC pattern <P><P>In Detail <P><P>Python is an object-oriented, scripting language that is used in wide range of categories. In software engineering, a design pattern is a recommended solution to a software design problem. Although not new, design patterns remain one of the hottest topics in software engineering and they come as a ready reference for software developers to solve the common problems they face at work. <P><P>This book will take you through each and every design pattern explained with the help of real-world examples. The aim of the book is to introduce more low-level detail and concepts on how to write Pythonic code, not just focusing on common solutions as implemented in Java and C++. It includes small sections on troubleshooting, best practices, system architecture, and its design aspects. With the help of this book, you will be able to understand Python design pattern concepts and the framework, as well as issues and their resolution. You'll focus on all 16 design patterns that are used to solve everyday problems.
Mastering Python Design Patterns: A guide to creating smart, efficient, and reusable software, 2nd Edition
by Sakis Kasampalis Kamon AyevaExploit various design patterns to master the art of solving problems using PythonKey FeaturesMaster the application design using the core design patterns and latest features of Python 3.7Learn tricks to solve common design and architectural challengesChoose the right plan to improve your programs and increase their productivityBook DescriptionPython is an object-oriented scripting language that is used in a wide range of categories. In software engineering, a design pattern is an elected solution for solving software design problems. Although they have been around for a while, design patterns remain one of the top topics in software engineering, and are a ready source for software developers to solve the problems they face on a regular basis. This book takes you through a variety of design patterns and explains them with real-world examples. You will get to grips with low-level details and concepts that show you how to write Python code, without focusing on common solutions as enabled in Java and C++. You'll also fnd sections on corrections, best practices, system architecture, and its designing aspects. This book will help you learn the core concepts of design patterns and the way they can be used to resolve software design problems. You'll focus on most of the Gang of Four (GoF) design patterns, which are used to solve everyday problems, and take your skills to the next level with reactive and functional patterns that help you build resilient, scalable, and robust applications. By the end of the book, you'll be able to effciently address commonly faced problems and develop applications, and also be comfortable working on scalable and maintainable projects of any size.What you will learnExplore Factory Method and Abstract Factory for object creationClone objects using the Prototype patternMake incompatible interfaces compatible using the Adapter patternSecure an interface using the Proxy patternChoose an algorithm dynamically using the Strategy patternKeep the logic decoupled from the UI using the MVC patternLeverage the Observer pattern to understand reactive programmingExplore patterns for cloud-native, microservices, and serverless architecturesWho this book is forThis book is for intermediate Python developers. Prior knowledge of design patterns is not required to enjoy this book.
Mastering Python for Bioinformatics: How To Write Flexible, Documented, Tested Python Code For Research Computing
by Ken Youens-ClarkLife scientists today urgently need training in bioinformatics skills. Too many bioinformatics programs are poorly written and barely maintained--usually by students and researchers who've never learned basic programming skills. This practical guide shows postdoc bioinformatics professionals and students how to exploit the best parts of Python to solve problems in biology while creating documented, tested, reproducible software.Ken Youens-Clark, author of Tiny Python Projects (Manning), demonstrates not only how to write effective Python code but also how to use tests to write and refactor scientific programs. You'll learn the latest Python features and toolsâ??including linters, formatters, type checkers, and testsâ??to create documented and tested programs. You'll also tackle 14 challenges in Rosalind, a problem-solving platform for learning bioinformatics and programming.Create command-line Python programs to document and validate parametersWrite tests to verify refactor programs and confirm they're correctAddress bioinformatics ideas using Python data structures and modules such as BiopythonCreate reproducible shortcuts and workflows using makefilesParse essential bioinformatics file formats such as FASTA and FASTQFind patterns of text using regular expressionsUse higher-order functions in Python like filter(), map(), and reduce()
Mastering Python for Data Science
by Samir MadhavanExplore the world of data science through Python and learn how to make sense of data About This Book * Master data science methods using Python and its libraries * Create data visualizations and mine for patterns * Advanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learning Who This Book Is For If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed. What You Will Learn * Manage data and perform linear algebra in Python * Derive inferences from the analysis by performing inferential statistics * Solve data science problems in Python * Create high-end visualizations using Python * Evaluate and apply the linear regression technique to estimate the relationships among variables. * Build recommendation engines with the various collaborative filtering algorithms * Apply the ensemble methods to improve your predictions * Work with big data technologies to handle data at scale In Detail Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving. This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science. Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods. Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics. Style and approach This book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios.
Mastering Python for Finance
by James Ma WeimingIf you are an undergraduate or graduate student, a beginner to algorithmic development and research, or a software developer in the financial industry who is interested in using Python for quantitative methods in finance, this is the book for you. It would be helpful to have a bit of familiarity with basic Python usage, but no prior experience is required.
Mastering Python for Finance
by James Ma Weiming<P><P>Understand, design, and implement state-of-the-art mathematical and statistical applications used in finance with Python <P><P>About This Book <P><P>Explore financial models used by the industry and ways of solving them with this guide <P><P>Discover the various features that Python provides for scientific computing and harness them to enhance your financial applications <P><P>Build state-of-the-art infrastructure for critical aspects such as modeling, trading, pricing, and analytics <P><P>Who This Book Is For <P><P>If you are an undergraduate or graduate student, a beginner to algorithmic development and research, or a software developer in the financial industry who is interested in using Python for quantitative methods in finance, this is the book for you. It would be helpful to have a bit of familiarity with basic Python usage, but no prior experience is required. <P><P>What You Will Learn <P><P>Perform interactive computing with IPython Notebook <P><P>Solve linear equations of financial models and perform ordinary least squares regression <P><P>Explore nonlinear modeling and solutions for optimum points using root-finding algorithms and solvers <P><P>Discover different types of numerical procedures used in pricing options <P><P>Model fixed-income instruments with bonds and interest rates <P><P>Manage big data with NoSQL and perform analytics with Hadoop <P><P>Build a high-frequency algorithmic trading platform with Python <P><P>Create an event-driven backtesting tool and measure your strategies <P><P>In Detail <P><P>Built initially for scientific computing, Python quickly found its place in finance. Its flexibility and robustness can be easily incorporated into applications for mathematical studies, research, and software development. <P><P>With this book, you will learn about all the tools you need to successfully perform research studies and modeling, improve your trading strategies, and effectively manage risks. You will explore the various tools and techniques used in solving complex problems commonly faced in finance. <P><P>You will learn how to price financial instruments such as stocks, options, interest rate derivatives, and futures using computational methods. Also, you will learn how you can perform data analytics on market indexes and use NoSQL to store tick data.