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Google Cloud Training
Activities in these self-paced labs are derived from the exercises from the book Data Science on Google Cloud Platform by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this first Google Cloud Labs Series, covering up through chapter 8, you are...
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Activities in these self-paced labs are derived from the exercises from the book Data Science on Google Cloud Platform by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this first Google Cloud Labs Series, covering up through chapter 8, you are given the opportunity to practice all aspects of ingestion, preparation, processing, querying, exploring, and visualizing data sets using Google Cloud tools and services. Note: you will have timed access to the online environment. You will need to complete each lab within the allotted time.
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Teaches aspects of data ingestion, preparation, processing, querying, exploring, and visualization, which are core skills for data scientists
Uses hands-on labs and interactive materials, making the learning experience more engaging
Taught by Google Cloud Training, who are recognized for their expertise in the field of data science
Derived from exercises in the book "Data Science on Google Cloud Platform" by Valliappa Lakshmanan, published by O'Reilly Media, Inc., a reputable source in the field
Requires learners to come in with some background knowledge, which may be a barrier for beginners
Provides timed access to the online environment, which may not be suitable for learners who need more time to complete the labs

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Science on Google Cloud with these activities:
Review Python Basics
Strengthen fundamental Python programming knowledge for effective data analysis and processing
Browse courses on Python Programming
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  • Go through online tutorials or documentation on Python basics
  • Practice writing simple Python scripts and functions
  • Test your understanding by solving coding challenges
Introduction to Data Science on Google Cloud Platform
Gain foundational knowledge of data science principles and Google Cloud tools
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  • Read the first few chapters of the book
Explore Google Cloud Platform Documentation
Establish foundational concepts and terminology used throughout the course
Browse courses on Google Cloud Platform
Show steps
  • Visit Google Cloud Platform documentation website
  • Explore key sections such as getting started guides, tutorials, and reference documentation
  • Familiarize yourself with basic concepts like projects, billing, and resource management
Five other activities
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Join a Study Group
Collaborate and learn from fellow students, discuss concepts, and solve problems together
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  • Reach out to classmates or join an existing study group
  • Set regular meeting times and establish clear goals
  • Review course materials, work on assignments, and prepare for exams as a group
Create a Cloud Storage Bucket
Gain hands-on experience with fundamental data storage operations in Google Cloud
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  • Log into Google Cloud console
  • Create a new Cloud Storage bucket with appropriate settings
Follow Google Cloud Skills Boost Courses
Enhance technical skills and gain practical experience
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  • Choose a Skills Boost course related to course topics
  • Follow the course instructions and complete the hands-on labs
  • Earn badges and certificates to demonstrate your proficiency
Attend a Google Cloud Data Analytics Workshop
Gain practical experience and learn about advanced concepts from industry experts
Show steps
  • Identify and register for a relevant workshop offered by Google Cloud
  • Attend the workshop, actively participate in hands-on exercises
  • Network with other participants and industry professionals
Build a Simple Data Pipeline
Apply course concepts to design and implement a real-world data processing pipeline
Browse courses on Data Pipeline
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  • Define the input and output data sources
  • Choose appropriate Google Cloud services for data ingestion, processing, and visualization
  • Create and configure the necessary resources, such as Cloud Functions, BigQuery, and Dataflow
  • Implement the data processing logic and connect the components
  • Test and validate the pipeline's functionality

Career center

Learners who complete Data Science on Google Cloud will develop knowledge and skills that may be useful to these careers:
Data Scientist
A Data Scientist uses the principles of statistics and machine learning to provide insights from data to drive informed decision-making. This course covers many of the tools and techniques used by Data Scientists to do their work, therefore completing it may be useful in terms of building a foundation for this career.
Data Analyst
A Data Analyst analyzes data to find patterns and trends. This course covers many of the tools and techniques used by Data Analysts to do their work, therefore completing it may be useful in terms of building a foundation for this career.
Machine Learning Engineer
A Machine Learning Engineer builds, deploys, and maintains machine learning models. This course covers many of the tools and techniques used by Machine Learning Engineers to do their work, therefore completing it may be useful in terms of building a foundation for this career.
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines. This course covers many of the tools and techniques used by Data Engineers to do their work, therefore completing it may be useful in terms of building a foundation for this career.
Business Analyst
A Business Analyst uses data to understand the needs of a business and to make recommendations for how to improve its operations. This course covers many of the tools and techniques used by Business Analysts to do their work, therefore completing it may be useful in terms of building a foundation for this career.
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course covers many of the tools and techniques used by Software Engineers to do their work, therefore completing it may be useful in terms of building a foundation for this career.
Operations Research Analyst
An Operations Research Analyst uses mathematical models to solve problems in business and industry. This course covers many of the tools and techniques used by Operations Research Analysts to do their work, therefore completing it may be useful in terms of building a foundation for this career.
Statistician
A Statistician uses statistical methods to analyze data. This course covers many of the tools and techniques used by Statisticians to do their work, therefore completing it may be useful in terms of building a foundation for this career.
Actuary
An Actuary uses mathematical and statistical methods to assess risk and uncertainty. This course covers many of the tools and techniques used by Actuaries to do their work, therefore completing it may be useful in terms of building a foundation for this career.
Financial Analyst
A Financial Analyst uses financial data to make recommendations for investments. This course covers many of the tools and techniques used by Financial Analysts to do their work, therefore completing it may be useful in terms of building a foundation for this career.
Market Research Analyst
A Market Research Analyst uses data to understand the needs of consumers. This course covers many of the tools and techniques used by Market Research Analysts to do their work, therefore completing it may be useful in terms of building a foundation for this career.
Data Journalist
A Data Journalist uses data to tell stories. This course covers many of the tools and techniques used by Data Journalists to do their work, therefore completing it may be useful in terms of building a foundation for this career.
Database Administrator
A Database Administrator designs, builds, and maintains databases. This course covers many of the tools and techniques used by Database Administrators to do their work, therefore completing it may be useful in terms of building a foundation for this career.
Computer Network Architect
A Computer Network Architect designs, builds, and maintains computer networks. This course covers many of the tools and techniques used by Computer Network Architects to do their work, therefore completing it may be useful in terms of building a foundation for this career.
Information Security Analyst
An Information Security Analyst protects data from unauthorized access, use, disclosure, disruption, modification, or destruction. This course covers many of the tools and techniques used by Information Security Analysts to do their work, therefore completing it may be useful in terms of building a foundation for this career.

Reading list

We've selected 15 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Data Science on Google Cloud.
Is the basis for this MOOC series. It will provide students supplemental materials and readings for each of the chapters covered in this course.
Provides a comprehensive guide to machine learning from a probabilistic perspective. It will be a useful reference text for students in this course.
Provides a comprehensive guide to data mining and machine learning. It will be a useful reference text for students in this course.
Provides a comprehensive guide to machine learning in action. It will be a useful reference text for students in this course.
Provides a comprehensive guide to using Python for deep learning. It will be a useful reference text for students in this course.
Provides a comprehensive guide to building and training deep learning models using TensorFlow, and will be a useful reference text for students in this course.
Provides a comprehensive guide to using Apache Spark for big data processing. It will be a useful reference text for students in this course.
Provides a comprehensive guide to using Java for big data analytics. It will be a useful reference text for students in this course.
Provides a concise and accessible introduction to machine learning. It will be a useful resource for students in this course who are new to the field.

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