SKU/Artículo: AMZ-B0DNQXVC6H
Data Science with Cloud Computing: Benefits and Challenges Using cloud platforms like AWS, Google Cloud, and Azure for scalable data processing and analytics
Format:
Kindle
Detalles del producto
Disponibilidad:
Fuera de stock
Fuera de stock
Peso con empaque:
0.15 kg
0.15 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Data Science with Cloud Computing: Benefits and Challenges is your comprehensive guide to understanding how cloud platforms like AWS, Google Cloud, and Azure are transforming the way data scientists work with big data. With cloud computing, businesses and individuals can access scalable infrastructure, powerful tools, and high-performance analytics capabilities without the need to manage complex hardware and data storage systems. This book explores how cloud computing accelerates data processing, improves collaboration, and enables real-time analytics, making it essential for modern data science workflows.Through clear explanations, practical examples, and step-by-step tutorials, you’ll learn how to leverage cloud platforms to handle large datasets, perform advanced analytics, and scale your data science projects effectively. Whether you’re a beginner or an experienced data scientist, this book will equip you with the skills to utilize the cloud for your data science tasks.Inside, you’ll discover:Introduction to Cloud Computing in Data Science: Understand the fundamentals of cloud computing and how cloud platforms like AWS, Google Cloud, and Azure are used in data science. Learn about the core services offered by these platforms for data storage, processing, and analytics.Benefits of Cloud Platforms for Data Science: Explore the key advantages of using the cloud for data science, including scalability, cost-effectiveness, flexibility, and the ability to work with large datasets. Learn how cloud computing allows data scientists to access advanced tools and computational resources on-demand.Data Storage and Management in the Cloud: Learn how to store and manage data in the cloud using services like Amazon S3, Google Cloud Storage, and Azure Blob Storage. Discover how to organize and access your data for analysis, ensuring it’s secure, reliable, and easy to process.Cloud-Based Data Processing and Analytics: Dive into the powerful tools cloud platforms offer for data processing and analysis. Learn about cloud services like AWS Lambda, Google BigQuery, and Azure Data Lake Analytics for performing data transformations, running machine learning models, and executing large-scale queries.Machine Learning in the Cloud: Discover how to use cloud platforms to run machine learning models, train algorithms on massive datasets, and deploy AI applications. Explore cloud-based ML tools such as AWS SageMaker, Google AI Platform, and Azure Machine Learning Studio to automate workflows and accelerate model development.Real-Time Analytics with Cloud Computing: Learn how cloud computing enables real-time data analytics, allowing you to monitor and respond to events in real time. Understand how cloud services like Google Cloud Pub/Sub, AWS Kinesis, and Azure Stream Analytics facilitate real-time data streaming and processing.Collaboration and Sharing with Cloud-Based Data Science Tools: Explore how cloud platforms foster collaboration among data science teams by providing shared resources, version control, and collaboration tools. Learn about cloud-based notebooks (e.g., Jupyter on AWS, Google Colab, and Azure Notebooks) and how they facilitate collaboration and knowledge sharing.The Future of Cloud-Based Data Science: Gain insights into the future of cloud computing in data science, including advancements in AI, the rise of serverless computing, and the role of edge computing. Learn about emerging trends in cloud data processing and analytics that will shape the future of data science.By the end of this book, you’ll have a clear understanding of how cloud computing can transform your data science workflows, enabling you to handle large-scale datasets, scale your models, and gain real-time insights—all while overcoming the challenges that come with working in the cloud.
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