What is the primary focus of 'Data Science on the Google Cloud Platform'?

The book focuses on implementing end-to-end real-time data pipelines using Google Cloud Platform (GCP). It covers the entire process from data ingestion to machine learning, providing readers with a comprehensive understanding of how to manage data workflows effectively. This guidance is essential as businesses increasingly rely on data-driven decision-making. By using this book, you will learn practical techniques to build scalable solutions that leverage GCP's extensive tools.

Who is the intended audience for this book?

The intended audience primarily includes data scientists, data engineers, and students interested in leveraging Google Cloud for data science projects. It is also beneficial for professionals looking to upgrade their skills in building real-time data pipelines for machine learning applications. The book’s clear instructions and practical examples are designed to cater to both beginners and those with existing knowledge seeking to deepen their understanding of cloud-based data projects.

What skills will I gain by reading this book?

By reading this book, you will acquire essential skills in constructing real-time data pipelines, integrating machine learning workflows, and utilizing various tools within the Google Cloud environment. You'll also learn best practices for data ingestion, processing, and analysis. These skills are particularly useful for anyone looking to implement robust data science solutions in their organization, making you adept at addressing complex data challenges in real-world scenarios.

Does this book cover practical examples and case studies?

Yes, the book is rich in practical examples and case studies that demonstrate real-world applications of data pipelines on Google Cloud. These examples are designed to help you understand theoretical concepts while seeing how they are applied in practice. Engaging with these case studies can enhance your learning process, as they provide relatable contexts to the challenges and solutions faced in data science projects, ensuring a deeper grasp of the material.

Are there any prerequisites for reading this book?

While the book is accessible to beginners, a fundamental understanding of data science concepts and familiarity with programming (preferably in Python) will be advantageous. Conceptual knowledge of cloud computing can also enhance your comprehension of the material. This background would prepare you to maximize the insights provided in the book, particularly when navigating through technical topics and practical applications in Google Cloud.

What tools and services are discussed in the book?

The book discusses a variety of tools and services available on Google Cloud, including BigQuery, Cloud Dataflow, Pub/Sub, and AI Platform. It details how to use these tools to create efficient and scalable data workflows. Understanding these services is crucial for developing sophisticated data pipelines and machine learning models, as they provide the necessary infrastructure to handle large quantities of data seamlessly.

Is this book suitable for team-learning or collaborative study?

Absolutely, the book's structured approach and comprehensive coverage make it suitable for team-learning and collaborative study. Groups can work through the chapters together to discuss concepts, tackle exercises, and share insights on real-time data pipeline challenges. This collaborative approach not only reinforces learning but also fosters team cohesion as members explore the Google Cloud ecosystem together.

How does this book help with machine learning implementation?

This book guides readers through the integration of machine learning within data pipelines, ensuring that you understand how to transition from data ingestion to actionable insights. It explains the necessary steps to prepare data, select models, and evaluate their performance on Google Cloud. By learning this process, you'll be equipped to deploy machine learning solutions that are reliable and optimized for real-time data usage.

Can the concepts in this book be applied to other cloud platforms?

While the book focuses specifically on Google Cloud, many concepts related to data pipeline design and machine learning can indeed be adapted to other cloud platforms. The underlying principles of data ingestion, processing, and analysis are universal, meaning that with some adjustments, you can leverage the knowledge gained to apply these practices in environments like AWS or Azure, although each platform has its unique tools and services.

Where can I buy 'Data Science on the Google Cloud Platform' in Qatar?

You can purchase 'Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines' through Ubuy in Qatar. Ubuy offers a user-friendly shopping experience and provides options for quick access to this valuable resource for anyone looking to enhance their skills in data science and cloud computing.

كيف تتسوق Data Science on the Google Cloud Platform: عبر الانترنت من يوباى?

من السهل التسوق في Data Science on the Google Cloud Platform: عبر الإنترنت من يوباي. كل ما عليك فعله هو البحث عن المنتج واختيار طريقة الشحن الخاصة بك أثناء الدفع وسيتم توصيله الى عنوانك

هل Data Science on the Google Cloud Platform: متوفر للتسوق عبر الإنترنت في Qatar؟

نعم ، في يوباي Qatar هذا المنتج متاح لك للتسوق بسعر مناسب. Data Science on the Google Cloud Platform: غير متوفر محليًا ولكن يمكنك الوثوق بنا بخدماتنا للشحن السريع.

كم من الوقت يستغرق الحصول على المنتج بعد تقديم الطلب؟

يختلف وقت تسليم المنتج الذي طلبته حسب ما طلبته وطريقة الشحن التي اخترتها. يتم ذكر وقت التسليم المقدر أثناء عملية الدفع ، لذا كن مرتاحًا أثناء التسوق.

عرض الأصل

Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning

Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP).

منتج #: 13516187

Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning

88% من المشترين سيوصون بهذا المنتج لصديق

منتج #: 13516187

QAR 127

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^{*سيتم استيراد جميع العناصر من أمريكا}

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معاملة آمنة

أفضل شركائنا اللوجستيين

Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP).

Paperback

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أبرز ما يلفت الانتباه

Real-Time Pipelines

Learn to build and implement real-time data pipelines on Google Cloud, addressing the need for swift data processing in modern applications.

Comprehensive Guide

This book offers a structured approach to mastering data engineering and machine learning, catering to both beginners and experienced professionals in data science.

Cloud Integration

Seamlessly integrate various Google Cloud tools, enhancing your data management skills while ensuring scalability and flexibility for future data needs.

تفاصيل المنتج

Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, you’ll work through a sample business decision by employing a variety of data science approaches.Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science.You’ll learn how to:Automate and schedule data ingest, using an App Engine applicationCreate and populate a dashboard in Google Data StudioBuild a real-time analysis pipeline to carry out streaming analyticsConduct interactive data exploration with Google BigQueryCreate a Bayesian model on a Cloud Dataproc clusterBuild a logistic regression machine-learning model with SparkCompute time-aggregate features with a Cloud Dataflow pipelineCreate a high-performing prediction model with TensorFlowUse your deployed model as a microservice you can access from both batch and real-time pipelines

Publisher	O'Reilly Media
Publication date	February 6, 2018
Edition	1st
Language	English
Print length	404 pages
ISBN-10	1491974567
ISBN-13	978-1491974568
Item Weight	1.44 pounds (650 grams)
Dimensions	7.25 x 1 x 9.25 inches (18.4 x 2.5 x 23.5 cm)

من يجب أن يشتري؟

Data Engineers
Ideal for data engineers looking to design and implement efficient real-time data pipelines using Google Cloud technologies.
Machine Learning Enthusiasts
Suitable for those interested in integrating machine learning models into their data pipelines on the Google Cloud Platform.
Cloud Developers
Great resource for developers aiming to leverage cloud technologies for data science and analytics applications.

Beginners in Data Science
Not suitable for complete beginners who lack fundamental knowledge of data science concepts and tools.

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أسئلة العملاء & الإجابات

سؤال: كيف تتسوق Data Science on the Google Cloud Platform: عبر الانترنت من يوباى?

إجابه: من السهل التسوق في Data Science on the Google Cloud Platform: عبر الإنترنت من يوباي. كل ما عليك فعله هو البحث عن المنتج واختيار طريقة الشحن الخاصة بك أثناء الدفع وسيتم توصيله الى عنوانك
سؤال: هل Data Science on the Google Cloud Platform: متوفر للتسوق عبر الإنترنت في Qatar؟

إجابه: نعم ، في يوباي Qatar هذا المنتج متاح لك للتسوق بسعر مناسب. Data Science on the Google Cloud Platform: غير متوفر محليًا ولكن يمكنك الوثوق بنا بخدماتنا للشحن السريع.
سؤال: كم من الوقت يستغرق الحصول على المنتج بعد تقديم الطلب؟

إجابه: يختلف وقت تسليم المنتج الذي طلبته حسب ما طلبته وطريقة الشحن التي اخترتها. يتم ذكر وقت التسليم المقدر أثناء عملية الدفع ، لذا كن مرتاحًا أثناء التسوق.

Data Modeling & Design Editorial Review

"Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning" is a well-researched, comprehensive guide that explores Google Cloud's features and services for building real-time data pipelines. The author takes you through a case study and offers practical examples that you can try on your own. The book starts with identifying a problem and then goes through all the stages of developing a complete data pipeline. However, customers have found some drawbacks as the book assumes you already know the GCP inside out and sometimes lacks explanations on how to navigate the platform. Additionally, code samples that are vague, and the assumption about the reader's background knowledge is inConsistent.

مراجعات العملاء وتقييماتهم

4.1

83 تقييمات العملاء

5 نجمة

62%
4 نجمة

10%
3 نجمة

15%
2 نجمة

3%
1 نجمة

10%

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شارك أفكارك مع عملاء آخرين

إيجابيات

Use of the case study for learning.
Comprehensive guide for learning which GCP services are used for which part of data analytic pipelines.
Code repository to try projects
Descriptive material with pointers to deeper material and user manuals.

سلبيات

Some customers found it hard to follow and navigate through vague and at times unexplained code.

تاريخ سعر المنتج

معلومات مهمة

القيود: بالنسبة للمنتجات التي يتم شحنها دولياً، يُرجى ملاحظة أن أي ضمان من الشركة المصنعة قد لا يكون صالحاً؛ قد لا تتوفر خيارات خدمة الشركة المصنعة؛ قد لا تكون أدلة المنتج والتعليمات وتحذيرات السلامة مكتوبة بلغة بلد المقصد؛ قد لا يتم تصميم المنتجات (والمواد المصاحبة لها) وفقاً لمعايير بلد الوجهة والمواصفات ومتطلبات الملصقات؛ وقد لا تتوافق المنتجات مع الجهد الكهربي المستخدم في بلد الوجهة والمعايير الكهربائية الأخرى (تتطلب استخدام محوّل كهربي أو جهاز تحويل إذا كان ذلك مناسباً). المستلم مسؤول عن ضمان إمكانية استيراد المنتج بشكل قانوني إلى بلد الوجهة. عند الطلب من يوباي أو الشركات التابعة لها، يكون المستلم هو المستورد المسجل ويجب أن يلتزم بجميع القوانين واللوائح الخاصة ببلد الوجهة.
ليست كل المنتجات المدرجة على يوباي معروضة للبيع، لأن يوباي هو محرك بحث عالمي. المنتجات تخضع للوائح التصدير / التجارة.

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معاملة آمنة

نوفر لك مدفوعات مشفّرة، وحماية متكاملة للمشتري، مع الالتزام بمعايير PCI DSS وشهادة ISO 27001:2022 لضمان أعلى مستويات الأمان في كل عملية شراء.

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المميزات والفوائد

Shows developers entering the data science field how to implement an end-to-end data pipeline on GCP.
Applies statistical and machine learning methods to real-world problems.
Provides a transformative and more collaborative way of doing data science.
Automation and scheduling of data ingest with an App Engine application.
Creation of a high-performing prediction model with TensorFlow.
Develop a logistic regression machine-learning model with Spark.

ضمان Ubuy

تسوّق بثقة مع منتجات أصلية %100، ومدفوعات آمنة متوافقة مع معيار PCI DSS، وحماية بيانات معتمدة وفق ISO 27001، وشحن دولي سريع، وإرجاع مجاني^*، وتغليف آمن لكل طلب.

Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning

QAR 127

شحن سريع

استرجاع مجاني*

تغليف آمن

منتجات أصلية %100

الامتثال لمعيار PCI DSS

حاصل على شهادة ISO 27001

أبرز ما يلفت الانتباه

تفاصيل المنتج

من يجب أن يشتري؟

وصف المنتج

أسئلة العملاء & الإجابات

Data Modeling & Design Editorial Review

مراجعات العملاء وتقييماتهم

أضف تقييم لهذا المنتج

إيجابيات

سلبيات

تاريخ سعر المنتج

معلومات مهمة

QAR 127

المميزات والفوائد

ضمان Ubuy

شحن
سريع

استرجاع
مجاني^*