This book is your practical and comprehensive guide to learning Google Cloud Platform (GCP) for data science, using only the free tier services offered by the platform. Data science and machine learning are increasingly becoming critical to businesses of all sizes, and the cloud provides a powerful platform for these applications. GCP offers a range of data science services that can be used to store, process, and analyze large datasets, and train and deploy machine learning models. The book is organized into seven chapters covering various topics such as GCP account setup, Google Colaboratory, Big Data and Machine Learning, Data Visualization and Business Intelligence, Data Processing and Transformation, Data Analytics and Storage, and Advanced Topics. Each chapter provides step-by-step instructions and examples illustrating how to use GCP services for data science and big data projects. Readers will learn how to set up a Google Colaboratory account and run Jupyternotebooks, access GCP services and data from Colaboratory, use BigQuery for data analytics, and deploy machine learning models using Vertex AI. The book also covers how to visualize data using Looker Data Studio, run data processing pipelines using Google Cloud Dataflow and Dataprep, and store data using Google Cloud Storage and SQL. What You Will Learn Set up a GCP account and project Explore BigQuery and its use cases, including machine learning Understand Google Cloud AI Platform and its capabilities Use Vertex AI for training and deploying machine learning models Explore Google Cloud Dataproc and its use cases for big data processing Create and share data visualizations and reports with Looker Data Studio Explore Google Cloud Dataflow and its use cases for batch and stream data processing Run data processing pipelines on Cloud Dataflow Explore Google Cloud Storageand its use cases for data storage Get an introduction to Google Cloud SQL and its use cases for relational databases Get an introduction to Google Cloud Pub/Sub and its use cases for real-time data streaming Who This Book Is For Data scientists, machine learning engineers, and analysts who want to learn how to use Google Cloud Platform (GCP) for their data science and big data projects

Why is Google Analytics 4 the most modern data model available for digital marketing analytics? Rather than simply reporting what has happened, GA4's new cloud integrations enable more data activation, linking online and offline data across all your streams to provide end-to-end marketing data. This practical book prepares you for the future of digital marketing by demonstrating how GA4 supports these additional cloud integrations. Author Mark Edmondson, Google developer expert for Google Analytics and Google Cloud, provides a concise yet comprehensive overview of GA4 and its cloud integrations. Data, business, and marketing analysts will learn major facets of GA4's powerful new analytics model, with topics including data architecture and strategy, and data ingestion, storage, and modeling. You'll explore common data activation use cases and get the guidance you need to implement them. You'll learn: How Google Cloud integrates with GA4 The potential use cases that GA4 integrations can enable Skills and resources needed to create GA4 integrations How much GA4 data capture is necessary to enable use cases The process of designing dataflows from strategy through data storage, modeling, and activation How to adapt the use cases to fit your business needs

Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP. Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way. You'll learn how to: Employ best practices in building highly scalable data and ML pipelines on Google Cloud Automate and schedule data ingest using Cloud Run Create and populate a dashboard in Data Studio Build a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQuery Conduct interactive data exploration with BigQuery Create a Bayesian model with Spark on Cloud Dataproc Forecast time series and do anomaly detection with BigQuery ML Aggregate within time windows with Dataflow Train explainable machine learning models with Vertex AI Operationalize ML with Vertex AI Pipelines

Dive into the world of reproducible data science with Pachyderm, a specialized platform designed for version-controlled data pipelines. By following this book, 'Reproducible Data Science with Pachyderm,' you'll gain the skills to implement robust, scalable machine learning workflows with Pachyderm 2.0, covering setup, integration, and advanced use cases. What this Book will help me do Build scalable, version-controlled data pipelines with Pachyderm's unique features. Understand the principles behind reproducible data science and implement them effectively. Deploy Pachyderm on AWS, Google Cloud, and Azure while integrating with popular tools. Create and manage end-to-end machine learning workflows, including hyperparameter tuning. Leverage advanced integrations, such as Pachyderm Notebooks and language clients like Python and Go. Author(s) Svetlana Karslioglu is a seasoned data scientist with extensive experience in constructing scalable machine learning and data processing systems. With years in both practical implementation and educational endeavors, she has a talent for breaking down complex concepts into accessible learning paths. Her approach is hands-on and results-oriented, aimed at empowering professionals to excel in the field of data science. Who is it for? This book is intended for data scientists, machine learning engineers, and data engineers who are keen to ensure reproducibility in their workflows. Ideal readers may have familiarity with data science basics and some exposure to Kubernetes and programming languages like Python. By studying the book, learners will establish confidence in implementing Pachyderm for scalable and reliable data pipelines.