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Victor Dantas
In this course, *Introduction to Big Data Analytics on GCP*, you’ll learn to use GCP technologies to analyze data of any size at any scale. First, you’ll explore the big data landscape on GCP and how to design and implement a data lake. Next, you’ll discover how to stream and process data for analytics on GCP. Finally, you’ll learn how to build a modern data warehouse to visualize, analyze, and gain insights from data. When you’re finished with this course, you’ll have the skills and knowledge of Google Cloud technologies needed to run big data analytics on GCP.
Register for this course and see more details by visiting:
OpenCourser.com/course/uby64g/introduction
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Teaches how to design and implement a data lake, which is a core component of modern data analytics architectures
Suitable for beginners who want to learn about big data analytics on GCP
Provides hands-on labs and interactive materials for a more engaging learning experience
Taught by Victor Dantas, an experienced instructor in big data analytics
Covers the full spectrum of big data analytics on GCP, from data ingestion to visualization and insights
May be less relevant for experienced data analysts who are already familiar with big data analytics on GCP
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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 Introduction to Big Data Analytics on GCP with these
activities:
Review Google Cloud technical documentation
Show steps
Review Google Cloud Big Data analytics documentation to enhance your understanding of the course concepts.
Browse courses on
Big Data
Show steps
-
Read through the provided documentation on Google Cloud Big Data analytics services.
-
Familiarize yourself with the key concepts and terminology used in Google Cloud Big Data analytics.
-
Identify the different services offered by Google Cloud for Big Data analytics.
Organize and review course materials
Show steps
Enhance your retention by organizing and reviewing course materials to reinforce your understanding of the concepts.
Show steps
-
Create a system for organizing your notes, assignments, and other course materials.
-
Regularly review your organized materials to recall and solidify the concepts covered in class.
Complete Google Cloud BigQuery tutorials
Show steps
Practice using Google Cloud BigQuery through hands-on tutorials to reinforce your understanding of the course content.
Browse courses on
Big Data
Show steps
-
Work through the introductory BigQuery tutorial.
-
Complete the tutorial on loading and querying data in BigQuery.
-
Explore additional tutorials on BigQuery features relevant to the course.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow additional tutorials on Big Data analytics
Show steps
Expand your knowledge by exploring additional tutorials and resources on Big Data analytics to complement the course content.
Browse courses on
Big Data
Show steps
-
Identify reputable sources for Big Data analytics tutorials.
-
Select tutorials aligned with your learning goals and interests.
-
Work through the tutorials and apply the concepts to your understanding of the course.
Build a data pipeline using Google Cloud services
Show steps
Apply your knowledge by building a data pipeline using Google Cloud services to solidify your understanding of the course concepts.
Browse courses on
Big Data
Show steps
-
Design the data pipeline architecture.
-
Select and configure the appropriate Google Cloud services for each stage of the pipeline.
-
Implement the data pipeline and test its functionality.
Present your data pipeline project
Show steps
Demonstrate your understanding of the course concepts by presenting your data pipeline project to consolidate your knowledge.
Browse courses on
Big Data
Show steps
-
Prepare a presentation that showcases the design, implementation, and results of your data pipeline project.
-
Deliver your presentation to an audience, providing clear explanations and answering questions.
Contribute to open-source Big Data projects
Show steps
Deepen your understanding by actively contributing to open-source Big Data projects and collaborating with the community.
Browse courses on
Big Data
Show steps
-
Identify open-source Big Data projects that align with your interests.
-
Review the project documentation and select an area where you can contribute.
-
Submit your code contributions and actively participate in discussions.
Review Google Cloud technical documentation
Show steps
Review Google Cloud Big Data analytics documentation to enhance your understanding of the course concepts.
Browse courses on
Big Data
Show steps
Show steps
Show steps
- Read through the provided documentation on Google Cloud Big Data analytics services.
- Familiarize yourself with the key concepts and terminology used in Google Cloud Big Data analytics.
- Identify the different services offered by Google Cloud for Big Data analytics.
Organize and review course materials
Show steps
Enhance your retention by organizing and reviewing course materials to reinforce your understanding of the concepts.
Show steps
Show steps
Show steps
- Create a system for organizing your notes, assignments, and other course materials.
- Regularly review your organized materials to recall and solidify the concepts covered in class.
Complete Google Cloud BigQuery tutorials
Show steps
Practice using Google Cloud BigQuery through hands-on tutorials to reinforce your understanding of the course content.
Browse courses on
Big Data
Show steps
Show steps
Show steps
- Work through the introductory BigQuery tutorial.
- Complete the tutorial on loading and querying data in BigQuery.
- Explore additional tutorials on BigQuery features relevant to the course.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow additional tutorials on Big Data analytics
Show steps
Expand your knowledge by exploring additional tutorials and resources on Big Data analytics to complement the course content.
Browse courses on
Big Data
Show steps
Show steps
Show steps
- Identify reputable sources for Big Data analytics tutorials.
- Select tutorials aligned with your learning goals and interests.
- Work through the tutorials and apply the concepts to your understanding of the course.
Build a data pipeline using Google Cloud services
Show steps
Apply your knowledge by building a data pipeline using Google Cloud services to solidify your understanding of the course concepts.
Browse courses on
Big Data
Show steps
Show steps
Show steps
- Design the data pipeline architecture.
- Select and configure the appropriate Google Cloud services for each stage of the pipeline.
- Implement the data pipeline and test its functionality.
Present your data pipeline project
Show steps
Demonstrate your understanding of the course concepts by presenting your data pipeline project to consolidate your knowledge.
Browse courses on
Big Data
Show steps
Show steps
Show steps
- Prepare a presentation that showcases the design, implementation, and results of your data pipeline project.
- Deliver your presentation to an audience, providing clear explanations and answering questions.
Contribute to open-source Big Data projects
Show steps
Deepen your understanding by actively contributing to open-source Big Data projects and collaborating with the community.
Browse courses on
Big Data
Show steps
Show steps
Show steps
- Identify open-source Big Data projects that align with your interests.
- Review the project documentation and select an area where you can contribute.
- Submit your code contributions and actively participate in discussions.
Career center
Learners who complete Introduction to Big Data Analytics on GCP will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts are at the heart of a data-driven operation. In addition to strong data analysis skills, many Data Analysts are required to have proficiency in database tools and data visualization software. This course helps build a foundation for these types of jobs, as you will learn to use Google Cloud technologies needed to run big data analytics on GCP. This includes building a modern data warehouse to visualize, analyze, and gain insights from data.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and manage the infrastructure needed to support data analytics. Many Data Engineers are required to be able to work in a cloud-computing environment, such as Google Cloud Platform (GCP). This course may be useful for you in getting started with understanding how to analyze data on GCP.
Data Scientist
Data Scientist
Data Scientists must be able to combine programming skills with a deep understanding of algorithms and statistical models to extract meaningful insights from data. They also are required to have proficiency in data visualization tools and techniques. This course may be useful for you in terms of learning the fundamentals of big data analytics on Google Cloud Platform (GCP).
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They are required to have a strong understanding of machine learning algorithms, as well as experience with big data technologies and tools. This course will give you a solid understanding of the Google Cloud technologies needed to run big data analytics on GCP.
Data Architect
Data Architect
Data Architects design and implement data management solutions that meet the needs of an organization. They are required to have a deep understanding of data management concepts and technologies, as well as experience with big data technologies and tools. This course will teach you how to use GCP technologies to analyze data of any size and scale. As a Data Architect, this will help you to design and implement data lakes and modern data warehouses.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts collect, analyze, interpret, and present data to help organizations make informed decisions. They are required to have a strong understanding of business intelligence tools and techniques, as well as experience with big data technologies and tools. This course may be useful for you in terms of learning the fundamentals of big data analytics on Google Cloud Platform (GCP).
Database Administrator
Database Administrator
Database Administrators are responsible for managing and maintaining databases. They are required to have a deep understanding of database management systems, as well as experience with big data technologies and tools. This course may be useful for you in terms of learning the fundamentals of big data analytics on Google Cloud Platform (GCP).
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They are required to have a strong understanding of software development principles and technologies, as well as experience with big data technologies and tools. This course may be useful for you in terms of learning the fundamentals of big data analytics on Google Cloud Platform (GCP).
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists are responsible for designing and creating visualizations that help organizations communicate data insights effectively. They are required to have a deep understanding of data visualization principles and tools, as well as experience with big data technologies and tools. This course will teach you how to use Google Cloud technologies to analyze data of any size and scale. As a Data Visualization Specialist, this will help you to design and implement modern data warehouses.
Data Governance Specialist
Data Governance Specialist
Data Governance Specialists are responsible for developing and implementing data governance policies and procedures. They are required to have a deep understanding of data governance principles and best practices, as well as experience with big data technologies and tools. This course may be useful for you in terms of learning the fundamentals of big data analytics on Google Cloud Platform (GCP).
Risk Manager
Risk Manager
Risk Managers are responsible for identifying and assessing risks to an organization. They are required to have a deep understanding of risk management principles and best practices, as well as experience with big data technologies and tools. This course may be useful for you in terms of learning the fundamentals of big data analytics on Google Cloud Platform (GCP).
Compliance Analyst
Compliance Analyst
Compliance Analysts are responsible for ensuring that an organization complies with applicable laws and regulations. They are required to have a deep understanding of compliance requirements, as well as experience with big data technologies and tools. This course may be useful for you in terms of learning the fundamentals of big data analytics on Google Cloud Platform (GCP).
IT Auditor
IT Auditor
IT Auditors are responsible for evaluating the effectiveness of an organization's IT systems and controls. They are required to have a deep understanding of IT auditing principles and best practices, as well as experience with big data technologies and tools. This course may be useful for you in terms of learning the fundamentals of big data analytics on Google Cloud Platform (GCP).
Information Security Analyst
Information Security Analyst
Information Security Analysts are responsible for protecting an organization's information assets from unauthorized access, use, disclosure, disruption, modification, or destruction. They are required to have a deep understanding of information security principles and best practices, as well as experience with big data technologies and tools. This course may be useful for you in terms of learning the fundamentals of big data analytics on Google Cloud Platform (GCP).
Enterprise Architect
Enterprise Architect
Enterprise Architects are responsible for designing and implementing an organization's IT infrastructure. They are required to have a deep understanding of enterprise architecture principles and best practices, as well as experience with big data technologies and tools. This course may be useful for you in terms of learning the fundamentals of big data analytics on Google Cloud Platform (GCP).
For more career information including salaries, visit:
OpenCourser.com/course/uby64g/introduction
Reading list
We've selected nine 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
Introduction to Big Data Analytics on GCP.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning, and provides hands-on examples of how to use probabilistic models for machine learning.
Save
Provides a comprehensive overview of deep learning. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks, and provides hands-on examples of how to use deep learning for different tasks.
Provides a comprehensive overview of Python for data analysis. It covers topics such as data cleaning, data manipulation, and data visualization, and provides hands-on examples of how to use Python for data analysis.
Save
Provides a comprehensive overview of R for data science. It covers topics such as data cleaning, data manipulation, and data visualization, and provides hands-on examples of how to use R for data science.
Provides a deep dive into advanced analytics with Spark. It covers topics such as machine learning, graph processing, and stream processing, and provides hands-on examples of how to use Spark for advanced analytics.
Provides a business-oriented introduction to data science. It covers topics such as data collection, analysis, and visualization, and provides real-world examples of how data science is being used in different industries.
Save
Provides a practical overview of big data in practice. It covers topics such as big data use cases, big data technologies, and big data best practices, and provides real-world examples of how big data is being used to transform businesses.
Save
Provides a practical introduction to data visualization. It covers topics such as data types, visual encodings, and chart types, and provides hands-on examples of how to create effective data visualizations.
Provides a comprehensive overview of cloud computing. It covers topics such as cloud computing models, cloud computing services, and cloud computing security, and provides hands-on examples of how to use cloud computing for different tasks.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/uby64g/introduction
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Time
615 hours
Level
Advanced
Via
Pluralsight
Instructor
Victor Dantas
Language
English
Good to know
Teaches how to design and implement a data lake, which is a core component of modern data analytics architectures
Suitable for beginners who want to learn about big data analytics on GCP
Provides hands-on labs and interactive materials for a more engaging learning experience
Taught by Victor Dantas, an experienced instructor in big data analytics
Covers the full spectrum of big data analytics on GCP, from data ingestion to visualization and insights
May be less relevant for experienced data analysts who are already familiar with big data analytics on GCP
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