Видавець: 16
Nataraj Dasgupta
Big Data analytics relates to the strategies used by organizations to collect, organize, and analyze large amounts of data to uncover valuable business insights that cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization’s data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages, and BI tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that. With the help of this guide, you will be able to bridge the gap between the theoretical world of technology and the practical reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB, and even learn how to write R code for neural networks.By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using the different tools and methods articulatedin this book.
Practical Business Intelligence. Optimize Business Intelligence for Efficient Data Analysis
Ahmed Sherif
Business Intelligence (BI) is at the crux of revolutionizing enterprise. Everyone wants to minimize losses and maximize profits. Thanks to Big Data and improved methodologies to analyze data, Data Analysts and Data Scientists are increasingly using data to make informed decisions. Just knowing how to analyze data is not enough, you need to start thinking how to use data as a business asset and then perform the right analysis to build an insightful BI solution. Efficient BI strives to achieve the automation of data for ease of reporting and analysis. Through this book, you will develop the ability to think along the right lines and use more than one tool to perform analysis depending on the needs of your business. We start off by preparing you for data analytics. We then move on to teach you a range of techniques to fetch important information from various databases, which can be used to optimize your business.The book aims to provide a full end-to-end solution for an environment setup that can help you make informed business decisions and deliver efficient and automated BI solutions to any company.It is a complete guide for implementing Business intelligence with the help of the most powerful tools like D3.js, R, Tableau, Qlikview and Python that are available on the market.
Jim Sinur, Zbigniew Misiak, BJ Biernatowski, Pedro...
Every business transformation begins with one question, “How can we do this better?” Whether it’s eliminating inefficiencies, optimizing business operations, or reimagining entire workflows with the help of AI, success depends on understanding and optimizing business processes. However, finding the right approach can be challenging with shifting market demands and evolving technologies.In this book, three seasoned experts in BPM, automation, and AI-driven process optimization guide you through frameworks, techniques, and tools that drive digital transformation by helping you explore business process modelling, before and after process execution. You'll visualize complex workflows, establish scalable process architectures that drive digital transformation, and integrate automation for efficiency. With insights into BPMN, business value analysis, and field-tested consulting guidance, you'll see how process-led design and data-driven decisions can lead to smarter, more agile operations. Through real-world examples, you’ll grasp how leading organizations have optimized their processes and how you can apply the same principles in your digital change program.By the end of this book, you’ll be able to identify, design, analyze, and transform business processes for measurable impact, as well as master the synergy of technology, process, and strategy to build systems that drive sustainable growth.*Email sign-up and proof of purchase required
Zhenyu George Li, Charles Shih-I Yeh, Dr....
Creating responsive, intelligent games takes more than just following engine tutorials. It requires solving real development challenges with practical, efficient code. This book bridges theory and practice with proven algorithms and techniques grounded in real-world production needs, emphasizing clean, portable C++ implementations.Starting with raylib and the custom Knight engine layer, you’ll be introduced to a simple object-oriented scene and game object system. From there, you'll build responsive gameplay systems using essential data structures and algorithms, progressing through 2D rendering, collision detection, effects, and camera control before advancing to 3D graphics environments built using shaders, lighting, quad-tree terrain, and dynamic camera setups.You’ll animate characters using skeletal rigs, interpolation, and inverse kinematics for fluid movement and then develop game AI using FSMs, behavior trees, A* pathfinding, and steering behaviors. The book also covers neural networks, exploring their underlying theories, training processes, and tools. Throughout, the code is written in legacy-compatible C++ to ensure broad support across platforms and compilers.By the end, you’ll have gained the skills to build game systems that not only work but also scale, perform, and adapt like professional-grade code.
B. M. Harwani
Used in everything from microcontrollers to operating systems, C is a popular programming language among developers because of its flexibility and versatility. This book helps you get hands-on with various tasks, covering the fundamental as well as complex C programming concepts that are essential for making real-life applications.You’ll start with recipes for arrays, strings, user-defined functions, and pre-processing directives. Once you’re familiar with the basic features, you’ll gradually move on to learning pointers, file handling, concurrency, networking, and inter-process communication (IPC). The book then illustrates how to carry out searching and arrange data using different sorting techniques, before demonstrating the implementation of data structures such as stacks and queues. Later, you’ll learn interesting programming features such as using graphics for drawing and animation, and the application of general-purpose utilities. Finally, the book will take you through advanced concepts such as low-level programming, embedded software, IoT, and security in coding, as well as techniques for improving code performance.By the end of this book, you'll have a clear understanding of C programming, and have the skills you need to develop robust apps.
Matt Eland, Kevin Griffin
Too many C# developers feel stuck building the same apps day in and day out, but learning through side projects can reignite your passion and level up your skills. This book offers a practical, hands-on approach to building confidence with .NET 10 and modern C# by building a variety of engaging applications, from interactive games and productivity tools to machine learning apps and browser-based chatbots.These projects are designed to teach practical patterns and modern tooling, with a focus on learning and experimentation over production hardening. You'll work with tools like Spectre.Console, ML.NET, Uno Platform, and more, developing everything from an adventure game and a card tracker to an AI chatbot. You’ll also build modern AI-enabled systems using Ollama, Microsoft Agent Framework, OpenTelemetry, and Aspire.Written by an experienced C# engineer and teacher, this book blends technical depth with a developer-friendly tone, helping you learn faster and retain more. You'll sharpen your understanding of core .NET capabilities and gain confidence to apply them in your own work or hobby projects.By the end of this book, you'll not only have a portfolio of practical .NET apps - you’ll also have grown as a developer and rediscovered the joy of programming.*Email sign-up and proof of purchase required
Emily Carr
Transform your IT project and make change stick with this step-by-step guide.In today’s fast-paced world of change, companies expect you to do more, with less. Drawing on over a decade of Change Management experience as a consultant with Fortune 500 companies including IBM and NCR, Emily Carr shares the secrets to making change happen smoothly.If your company is like most, the number one reason that projects have failed over the years don’t have to do with technology. They have to do with people. People didn’t like the new technology. People weren’t trained properly on the change. People hadn’t received adequate communications and didn’t understand the change. Sound familiar?Project teams rarely forget to work on the technology, but they often forget to work with the people, and no matter how amazing your new technology is, it’s useless unless people use it efficiently.This book will help you focus on the people.Packed with templates, checklists, and real-life examples, this user-friendly guide will provide you with the insights and guidance of an expert consultant, for a fraction of the price. You’ll follow a clearly laid out path from Change Management novice to confident and prepared change manager. You’ll be introduced to the Five Pillars of Change: Sponsorship, Stakeholder Management, Communication, Training, and Organization Design. You will work step-by-step through templates in each pillar to build and run a comprehensive Change Management plan tailor-made to your project and organization.
Emily Jiang, Andrew McCright, John Alcorn, David...
In this cloud-native era, most applications are deployed in a cloud environment that is public, private, or a combination of both. To ensure that your application performs well in the cloud, you need to build an application that is cloud native. MicroProfile is one of the most popular frameworks for building cloud-native applications, and fits well with Kubernetes. As an open standard technology, MicroProfile helps improve application portability across all of MicroProfile's implementations.Practical Cloud-Native Java Development with MicroProfile is a comprehensive guide that helps you explore the advanced features and use cases of a variety of Jakarta and MicroProfile specifications. You'll start by learning how to develop a real-world stock trader application, and then move on to enhancing the application and adding day-2 operation considerations. You'll gradually advance to packaging and deploying the application. The book demonstrates the complete process of development through to deployment and concludes by showing you how to monitor the application's performance in the cloud. By the end of this book, you will master MicroProfile's latest features and be able to build fast and efficient cloud-native applications.
Abhinav Dadhich
In this book, you will find several recently proposed methods in various domains of computer vision. You will start by setting up the proper Python environment to work on practical applications. This includes setting up libraries such as OpenCV, TensorFlow, and Keras using Anaconda. Using these libraries, you'll start to understand the concepts of image transformation and filtering. You will find a detailed explanation of feature detectors such as FAST and ORB; you'll use them to find similar-looking objects.With an introduction to convolutional neural nets, you will learn how to build a deep neural net using Keras and how to use it to classify the Fashion-MNIST dataset. With regard to object detection, you will learn the implementation of a simple face detector as well as the workings of complex deep-learning-based object detectors such as Faster R-CNN and SSD using TensorFlow. You'll get started with semantic segmentation using FCN models and track objects with Deep SORT. Not only this, you will also use Visual SLAM techniques such as ORB-SLAM on a standard dataset. By the end of this book, you will have a firm understanding of the different computer vision techniques and how to apply them in your applications.
Practical Convolutional Neural Networks. Implement advanced deep learning models using Python
Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari
Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models. This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available.Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets.
Ed Moyle, Diana Kelley
Cybersecurity architects work with others to develop a comprehensive understanding of the business' requirements. They work with stakeholders to plan designs that are implementable, goal-based, and in keeping with the governance strategy of the organization.With this book, you'll explore the fundamentals of cybersecurity architecture: addressing and mitigating risks, designing secure solutions, and communicating with others about security designs. The book outlines strategies that will help you work with execution teams to make your vision a concrete reality, along with covering ways to keep designs relevant over time through ongoing monitoring, maintenance, and continuous improvement. As you progress, you'll also learn about recognized frameworks for building robust designs as well as strategies that you can adopt to create your own designs.By the end of this book, you will have the skills you need to be able to architect solutions with robust security components for your organization, whether they are infrastructure solutions, application solutions, or others.
Diana Kelley, Ed Moyle
Cybersecurity architecture is the discipline of systematically ensuring that an organization is resilient against cybersecurity threats. Cybersecurity architects work in tandem with stakeholders to create a vision for security in the organization and create designs that are implementable, goal-based, and aligned with the organization’s governance strategy.Within this book, you'll learn the fundamentals of cybersecurity architecture as a practical discipline. These fundamentals are evergreen approaches that, once mastered, can be applied and adapted to new and emerging technologies like artificial intelligence and machine learning. You’ll learn how to address and mitigate risks, design secure solutions in a purposeful and repeatable way, communicate with others about security designs, and bring designs to fruition. This new edition outlines strategies to help you work with execution teams to make your vision a reality, along with ways of keeping designs relevant over time. As you progress, you'll also learn about well-known frameworks for building robust designs and strategies that you can adopt to create your own designs.By the end of this book, you’ll have the foundational skills required to build infrastructure, cloud, AI, and application solutions for today and well into the future with robust security components for your organization.
Ed Moyle, Diana Kelley
Cybersecurity architects work with others to develop a comprehensive understanding of the business' requirements. They work with stakeholders to plan designs that are implementable, goal-based, and in keeping with the governance strategy of the organization.With this audiobook, you'll explore the fundamentals of cybersecurity architecture: addressing and mitigating risks, designing secure solutions, and communicating with others about security designs. The book outlines strategies that will help you work with execution teams to make your vision a concrete reality, along with covering ways to keep designs relevant over time through ongoing monitoring, maintenance, and continuous improvement. As you progress, you'll also learn about recognized frameworks for building robust designs as well as strategies that you can adopt to create your own designs.By the end of this audiobook, you will have the skills you need to be able to architect solutions with robust security components for your organization, whether they are infrastructure solutions, application solutions, or others.
Practical Data Analysis Cookbook. Over 60 practical recipes on data exploration and analysis
Tomasz Drabas
Data analysis is the process of systematically applying statistical and logical techniques to describe and illustrate, condense and recap, and evaluate data. Its importance has been most visible in the sector of information and communication technologies. It is an employee asset in almost all economy sectors.This book provides a rich set of independent recipes that dive into the world of data analytics and modeling using a variety of approaches, tools, and algorithms. You will learn the basics of data handling and modeling, and will build your skills gradually toward more advanced topics such as simulations, raw text processing, social interactions analysis, and more.First, you will learn some easy-to-follow practical techniques on how to read, write, clean, reformat, explore, and understand your data—arguably the most time-consuming (and the most important) tasks for any data scientist. In the second section, different independent recipes delve into intermediate topics such as classification, clustering, predicting, and more. With the help of these easy-to-follow recipes, you will also learn techniques that can easily be expanded to solve other real-life problems such as building recommendation engines or predictive models. In the third section, you will explore more advanced topics: from the field of graph theory through natural language processing, discrete choice modeling to simulations. You will also get to expand your knowledge on identifying fraud origin with the help of a graph, scrape Internet websites, and classify movies based on their reviews.By the end of this book, you will be able to efficiently use the vast array of tools that the Python environment has to offer.
Hector Cuesta
Plenty of small businesses face big amounts of data but lack the internal skills to support quantitative analysis. Understanding how to harness the power of data analysis using the latest open source technology can lead them to providing better customer service, the visualization of customer needs, or even the ability to obtain fresh insights about the performance of previous products. Practical Data Analysis is a book ideal for home and small business users who want to slice and dice the data they have on hand with minimum hassle.Practical Data Analysis is a hands-on guide to understanding the nature of your data and turn it into insight. It will introduce you to the use of machine learning techniques, social networks analytics, and econometrics to help your clients get insights about the pool of data they have at hand. Performing data preparation and processing over several kinds of data such as text, images, graphs, documents, and time series will also be covered.Practical Data Analysis presents a detailed exploration of the current work in data analysis through self-contained projects. First you will explore the basics of data preparation and transformation through OpenRefine. Then you will get started with exploratory data analysis using the D3js visualization framework. You will also be introduced to some of the machine learning techniques such as, classification, regression, and clusterization through practical projects such as spam classification, predicting gold prices, and finding clusters in your Facebook friends' network. You will learn how to solve problems in text classification, simulation, time series forecast, social media, and MapReduce through detailed projects. Finally you will work with large amounts of Twitter data using MapReduce to perform a sentiment analysis implemented in Python and MongoDB. Practical Data Analysis contains a combination of carefully selected algorithms and data scrubbing that enables you to turn your data into insight.
Practical Data Analysis. Pandas, MongoDB, Apache Spark, and more - Second Edition
Hector Cuesta, Dr. Sampath Kumar
Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service.This book explains the basic data algorithms without the theoretical jargon, and you’ll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark.
Marc Wintjen
Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data.After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps.Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries.By the end of this book, you'll have gained the practical skills you need to analyze data with confidence.
Robert Hawker
Poor data quality can lead to increased costs, hinder revenue growth, compromise decision-making, and introduce risk into organizations. This leads to employees, customers, and suppliers finding every interaction with the organization frustrating.Practical Data Quality provides a comprehensive view of managing data quality within your organization, covering everything from business cases through to embedding improvements that you make to the organization permanently. Each chapter explains a key element of data quality management, from linking strategy and data together to profiling and designing business rules which reveal bad data. The book outlines a suite of tried-and-tested reports that highlight bad data and allow you to develop a plan to make corrections. Throughout the book, you’ll work with real-world examples and utilize re-usable templates to accelerate your initiatives.By the end of this book, you’ll have gained a clear understanding of every stage of a data quality initiative and be able to drive tangible results for your organization at pace.
RATNADIP ADHIKARI, Rajib Bhattacharya, Prabhanjan Narayanachar Tattar,...
As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don’t. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python.
Nathan George
Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science.The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion.As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments.By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.
Allan Visochek
Around 80% of time in data analysis is spent on cleaning and preparing data for analysis. This is, however, an important task, and is a prerequisite to the rest of the data analysis workflow, including visualization, analysis and reporting. Python and R are considered a popular choice of tool for data analysis, and have packages that can be best used to manipulate different kinds of data, as per your requirements. This book will show you the different data wrangling techniques, and how you can leverage the power of Python and R packages to implement them.You’ll start by understanding the data wrangling process and get a solid foundation to work with different types of data. You’ll work with different data structures and acquire and parse data from various locations. You’ll also see how to reshape the layout of data and manipulate, summarize, and join data sets. Finally, we conclude with a quick primer on accessing and processing data from databases, conducting data exploration, and storing and retrieving data quickly using databases.The book includes practical examples on each of these points using simple and real-world data sets to give you an easier understanding. By the end of the book, you’ll have a thorough understanding of all the data wrangling concepts and how to implement them in the best possible way.
Nino Vidović, Patrick Kirner-Fehrenbach
Are you truly unlocking the full potential of your embedded debugging tools? This hands-on guide cuts through the confusion of common workflows and shows you how to trace, profile, and debug ARM-based systems like a professional firmware engineer.Written by an industry expert, this book guides you through practical debugging scenarios using real hardware setups. You’ll explore both essential and advanced techniques, from setting breakpoints and analyzing memory to using hardware tracing, power profiling, and RTOS awareness. Through real-world crash analysis, you'll learn how to detect stack overflows, communication errors, memory leaks, and more.You will explore practical examples based on ARM Cortex-M target devices, which help you build structured and efficient debugging workflows. The learned skills can then be easily applied to other chip architectures as well. You’ll walk away with a clear understanding of the tools available, how to apply them in complex firmware projects, and the confidence to tackle even the most elusive bugs in production systems.Whether you’re refining your setup or debugging embedded systems at scale, this book will sharpen your skills and elevate your embedded development workflow.*Email sign-up and proof of purchase required
Yong Liu
The book starts with an overview of the deep learning (DL) life cycle and the emerging Machine Learning Ops (MLOps) field, providing a clear picture of the four pillars of deep learning: data, model, code, and explainability and the role of MLflow in these areas.From there onward, it guides you step by step in understanding the concept of MLflow experiments and usage patterns, using MLflow as a unified framework to track DL data, code and pipelines, models, parameters, and metrics at scale. You’ll also tackle running DL pipelines in a distributed execution environment with reproducibility and provenance tracking, and tuning DL models through hyperparameter optimization (HPO) with Ray Tune, Optuna, and HyperBand. As you progress, you’ll learn how to build a multi-step DL inference pipeline with preprocessing and postprocessing steps, deploy a DL inference pipeline for production using Ray Serve and AWS SageMaker, and finally create a DL explanation as a service (EaaS) using the popular Shapley Additive Explanations (SHAP) toolbox.By the end of this book, you’ll have built the foundation and gained the hands-on experience you need to develop a DL pipeline solution from initial offline experimentation to final deployment and production, all within a reproducible and open source framework.
Miroslav Wengner
Design patterns are proven solutions to standard problems in software design and development, allowing you to create reusable, flexible, and maintainable code. This book enables you to upskill by understanding popular patterns to evolve into a proficient software developer.You’ll start by exploring the Java platform to understand and implement design patterns. Then, using various examples, you’ll create different types of vehicles or their parts to enable clarity in design pattern thinking, along with developing new vehicle instances using dedicated design patterns to make the process consistent. As you progress, you’ll find out how to extend vehicle functionalities and keep the code base structure and behavior clean and shiny. Concurrency plays an important role in application design, and you'll learn how to employ a such design patterns with the visualization of thread interaction. The concluding chapters will help you identify and understand anti-pattern utilization in the early stages of development to address refactoring smoothly. The book covers the use of Java 17+ features such as pattern matching, switch cases, and instances of enhancements to enable productivity.By the end of this book, you’ll have gained practical knowledge of design patterns in Java and be able to apply them to address common design problems.