Products related to Data:
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Data Quality Engineering in Financial Services : Applying Manufacturing Techniques to Data
Data quality will either make you or break you in the financial services industry.Missing prices, wrong market values, trading violations, client performance restatements, and incorrect regulatory filings can all lead to harsh penalties, lost clients, and financial disaster.This practical guide provides data analysts, data scientists, and data practitioners in financial services firms with the framework to apply manufacturing principles to financial data management, understand data dimensions, and engineer precise data quality tolerances at the datum level and integrate them into your data processing pipelines. You'll get invaluable advice on how to: Evaluate data dimensions and how they apply to different data types and use cases Determine data quality tolerances for your data quality specification Choose the points along the data processing pipeline where data quality should be assessed and measured Apply tailored data governance frameworks within a business or technical function or across an organization Precisely align data with applications and data processing pipelines And more
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Software Engineering for Data Scientists : From Notebooks to Scalable Systems
Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success-and is absolutely essential for those working with production code.This practical book bridges the gap between data science and software engineering, clearly explaining how to apply the best practices from software engineering to data science. Examples are provided in Python, drawn from popular packages such as NumPy and pandas.If you want to write better data science code, this guide covers the essential topics you need (and that are often missing from introductory data science or coding classes), including how to:Understand data structures and object-oriented programmingClearly and skillfully document your codePackage and share your codeIntegrate data science code with a larger codebaseWrite APIsCreate secure codeApply best practices to common tasks such as testing, error handling, and loggingWork more effectively with software engineersWrite more efficient, maintainable, and robust code in PythonPut your data science projects into productionAnd more
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Human Factors in Engineering : Manufacturing Systems, Automation, and Interactions
This book addresses aspects of human factors in engineering and provides a detailed discussion of novel approaches, systems engineering tools, artificial cognitive systems, and intelligent technologies and automation.It presents applications in diverse areas, including digital manufacturing, transportation, infrastructure development, and cybersecurity. This book:• Merges the engineering perspective with the human factors and social dimension of computing and artificial intelligence–based technologies. • Covers technological development of human factors engineering and the human dimension in applications across all areas of modern society. • Relates to human behavior in the context of technology and systems interactions. • Discusses the design and the appropriation of 3D printing techniques in the management of an innovative product system. • Presents systems engineering tools, user experience methodologies, artificial cognitive systems, intelligent technologies, and automation. The text is for students, professionals, and researchers in the fields of ergonomics, human factors, industrial engineering, and manufacturing engineering.
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Software Test Automation : Software Test Automation
This book describes how to build and implement an automated testing regime for software development.It presents a detailed account of the principles of automated testing, practical techniques for designing a good automated testing regime, and advice on choosing and applying off-the-shelf testing tools to specific needs.This sound and practical introduction to automated testing comes from two authors well known for their seminars, consultancy and training in the field.
Price: 77.99 £ | Shipping*: 0.00 £
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What do Data Science and Data Engineering mean?
Data Science involves extracting insights and knowledge from data using various techniques such as statistics, machine learning, and data visualization. Data Scientists are responsible for analyzing and interpreting complex data sets to inform business decisions and solve problems. Data Engineering, on the other hand, focuses on the design and construction of systems for collecting, storing, and processing data. Data Engineers build and maintain the infrastructure that enables data to be accessed and utilized by Data Scientists and other stakeholders. They are also responsible for ensuring the reliability, scalability, and efficiency of data pipelines and storage systems.
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What is Software Data Becker?
Software Data Becker was a German software company that specialized in developing and publishing various software products, including graphic design, web design, and office productivity software. The company was known for its high-quality and user-friendly software solutions, and it catered to both individual users and businesses. Software Data Becker was also known for its popular series of software manuals and guides, which were widely used by beginners and professionals alike. The company had a strong presence in the European market and was recognized for its innovative and reliable software products.
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What professions are there in the field of software development or data science?
Some professions in the field of software development include software engineer, software developer, front-end developer, back-end developer, and full-stack developer. In the field of data science, professions include data scientist, data analyst, data engineer, machine learning engineer, and business intelligence analyst. These professionals work with various programming languages, tools, and technologies to develop software applications, analyze data, and derive insights to drive business decisions.
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How can data be analyzed using software?
Data can be analyzed using software by first importing the data into the software platform. Then, the software can perform various analytical techniques such as statistical analysis, data mining, machine learning, and visualization to uncover patterns, trends, and insights within the data. The software can also help in cleaning and preprocessing the data, as well as in creating reports and visualizations to communicate the findings. Additionally, some software tools offer advanced features like predictive modeling and natural language processing to further analyze and interpret the data.
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Data-Driven Engineering Design
This book addresses the emerging paradigm of data-driven engineering design.In the big-data era, data is becoming a strategic asset for global manufacturers.This book shows how the power of data can be leveraged to drive the engineering design process, in particular, the early-stage design. Based on novel combinations of standing design methodology and the emerging data science, the book presents a collection of theoretically sound and practically viable design frameworks, which are intended to address a variety of critical design activities including conceptual design, complexity management, smart customization, smart product design, product service integration, and so forth.In addition, it includes a number of detailed case studies to showcase the application of data-driven engineering design.The book concludes with a set of promising research questions that warrant further investigation. Given its scope, the book will appeal to a broad readership, including postgraduate students, researchers, lecturers, and practitioners in the field of engineering design.
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Deep Learning at Scale : At the Intersection of Hardware, Software, and Data
Bringing a deep-learning project into production at scale is quite challenging.To successfully scale your project, a foundational understanding of full stack deep learning, including the knowledge that lies at the intersection of hardware, software, data, and algorithms, is required. This book illustrates complex concepts of full stack deep learning and reinforces them through hands-on exercises to arm you with tools and techniques to scale your project.A scaling effort is only beneficial when it's effective and efficient.To that end, this guide explains the intricate concepts and techniques that will help you scale effectively and efficiently. You'll gain a thorough understanding of:How data flows through the deep-learning network and the role the computation graphs play in building your modelHow accelerated computing speeds up your training and how best you can utilize the resources at your disposalHow to train your model using distributed training paradigms, i.e., data, model, and pipeline parallelismHow to leverage PyTorch ecosystems in conjunction with NVIDIA libraries and Triton to scale your model trainingDebugging, monitoring, and investigating the undesirable bottlenecks that slow down your model trainingHow to expedite the training lifecycle and streamline your feedback loop to iterate model developmentA set of data tricks and techniques and how to apply them to scale your training modelHow to select the right tools and techniques for your deep-learning projectOptions for managing the compute infrastructure when running at scale
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Data Engineering Design Patterns : Recipes for Solving the Most Common Data Engineering Problems
Data projects are an intrinsic part of an organization's technical ecosystem, but data engineers in many companies are still trying to solve problems that others have already solved.This hands-on guide shows you how to provide valuable data by focusing on various aspects of data engineering, including data ingestion, data quality, idempotency, and more. Author Bartosz Konieczny guides you through the process of building reliable end-to-end data engineering projects, from data ingestion to data observability, focusing on data engineering design patterns that solve common business problems in a secure and storage-optimized manner.Each pattern includes a user-facing description of the problem, solutions, and consequences that place the pattern into the context of real-life scenarios. Throughout this journey, you'll use open source data tools and public cloud services to see how to put each pattern into practice.You'll learn:Challenges data engineers face and their impact on data systemsHow these challenges relate to data system componentsWhat data engineering patterns are forHow to identify and fix issues with your current data componentsTechnology-agnostic solutions to new and existing data projectsHow to implement patterns with Apache Airflow, Apache Spark, Apache Flink, and Delta LakeBartosz Konieczny is a freelance data engineer who's been coding for more than 15 years.He's held various senior hands-on positions that helped him work on many data engineering problems in batch and stream processing.
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Fundamentals of Data Engineering : Plan and Build Robust Data Systems
Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice.With this practical book, you will learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available in the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers.You will understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, governance, and deployment that are critical in any data environment regardless of the underlying technology. This book will help you: Assess data engineering problems using an end-to-end data framework of best practices Cut through marketing hype when choosing data technologies, architecture, and processes Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle
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In automation, which software is it?
In automation, the software used is typically referred to as automation software or automation tools. These tools are designed to automate repetitive tasks, streamline processes, and increase efficiency in various industries. Some popular examples of automation software include UiPath, Blue Prism, Automation Anywhere, and Selenium. These tools often use technologies such as robotic process automation (RPA) and artificial intelligence to mimic human actions and perform tasks with minimal human intervention.
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What is a data sharing software in PHP?
A data sharing software in PHP is a tool or application that allows users to share and exchange data, information, or files using the PHP programming language. It enables users to upload, store, and access data from a central location, making it easier to collaborate and work on projects together. This type of software often includes features such as user authentication, file management, and access control to ensure the security and privacy of the shared data. Additionally, it may also provide options for data synchronization and version control to keep track of changes made to the shared data.
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Software or hardware?
When deciding between software and hardware, it ultimately depends on the specific needs and goals of the user. Software provides flexibility, scalability, and ease of updates, making it ideal for tasks that require frequent changes or updates. On the other hand, hardware offers reliability, security, and performance for tasks that require high processing power or data storage. It is important to carefully evaluate the requirements of the project or task at hand to determine whether software or hardware is the best solution.
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'Software or hardware?'
The choice between software and hardware depends on the specific needs and goals of the user. Software provides flexibility and can be easily updated or customized, while hardware offers physical components that may be more reliable and provide better performance for certain tasks. Ultimately, the decision between software and hardware should be based on the specific requirements of the user and the intended use of the technology.
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