Additional information
by Shitalkumar R. Sukhdeve (Author), Sandika S. Sukhdeve (Author)
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 Jupyter notebooks, 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 Storage and 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
Back Jacket
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 Jupyter notebooks, 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 Storage and 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
Author Biography
Shitalkumar R. Sukhdeve is an experienced senior data scientist with a strong track record of developing and deploying transformative data science and machine learning solutions to solve complex business problems in the telecom industry. He has notable achievements in developing a machine learning-driven customer churn prediction and root cause exploration solution, a customer credit scoring system, and a product recommendation engine.
Shitalkumar is skilled in enterprise data science and research ecosystem development, dedicated to optimizing key business indicators, and adding revenue streams for companies. He is pursuing a doctorate in business administration from SSBM, Switzerland, and an M.Tech in computer science and engineering from VNIT Nagpur.
Shitalkumar has authored a book titled Step Up for Leadership in Enterprise Data Science and Artificial Intelligence with Big Data: Illustrations with R and Python and co-authored a book titled Web Application Development with R Using Shiny, 3rd edition. He is a speaker at various technology and business events such as WorldAI Show Jakarta 2021, 2022, and 2023, NXT CX Jakarta 2022, Global Cloud Native Open Source Summit 2022, Cyber Security Summit 2022, and ASEAN Conversational Automation Webinar. You can find him on LinkedIn.
Sandika S. Sukhdeve is an expert in Data Visualization and Google-certified Project Management. She previously served as Assistant Professor in a Mechanical Engineering Department and has authored Amazon bestseller titles across diverse markets such as the USA, Germany, Canada, and more. She has a background in Human Resources and a wealth of experience in Branding.
As an Assistant Professor, she successfully guided more than 2,000 students and delivered 1,000+ lectures, and mentored numerous projects (including Computational Fluid Dynamics). She excels in managing both people and multiple projects, ensuring timely completion. Her areas of specialization encompass Thermodynamics, Applied Thermodynamics, Industrial Engineering, Product Design and Development, Theory of Machine, Numerical Methods and Optimization, and Fluid Mechanics. She holds a master's degree in Technology (with a Specialization in Heat-Power), and she possesses exceptional skills in visualizing, analyzing, and constructing classification and prediction models using R and MATLAB. You can find her on LinkedIn.
