GOOGLE CLOUD FUNDAMENTALS: BIG DATA AND MACHINE LEARNING
The Google Cloud Fundamentals: Big Data and Machine Learning course introduces learners to big data and machine learning concepts using Google Cloud Platform. This course provides a high-level understanding of how Google Cloud enables organizations to store, process, analyze, and derive insights from large datasets, as well as build and deploy machine learning solutions. Participants will explore Google Clouds data analytics and machine learning services, understand common use cases, and learn how data flows through analytics and ML pipelines on Google Cloud. Course Objectives By the end of this course, participants will be able to: Understand core big data and machine learning concepts Explain Google Clouds big data and ML service offerings Identify appropriate Google Cloud services for analytics workloads Understand batch vs streaming data processing Describe how machine learning models are built and deployed Recognize real-world use cases for data analytics and ML on Google Cloud
Course Curriculum
1
- What is big data?
- Characteristics of big data (volume, velocity, variety)
- Introduction to machine learning and AI
- Common business use cases
2
- Overview of Google Cloud analytics ecosystem
- Data processing pipelines
- Batch vs streaming analytics
3
- Cloud Storage as a data lake
- BigQuery for data warehousing
- Choosing the right storage solution
4
- BigQuery analytics and SQL
- Dataflow for stream and batch processing
- Dataproc for Apache Spark and Hadoop
- Use-case driven service selection
5
- Real-time data processing concepts
- Pub/Sub for event ingestion
- Streaming analytics patterns
6
- Machine learning workflow
- Supervised vs unsupervised learning
- Training, evaluation, and inference
7
- Vertex AI overview
- Pre-trained APIs (vision, language, speech)
- AutoML concepts and use cases
8
- BigQuery visualization options
- Looker and reporting concepts
- Turning data into business insights
9
- Data security and access control
- Governance best practices
- Cost management for analytics workloads
10
- End-to-end analytics and ML scenarios
- Mapping services to business problems
- Next steps in the Google Cloud data & ML journey
11
- Individuals new to data analytics and machine learning
- IT professionals exploring Google Cloud data services
- Data analysts and aspiring data engineers
- Developers interested in ML and AI
- Business professionals working with data teams
12
- Google Cloud Fundamentals: Core Infrastructure or equivalent knowledge
- No prior big data or ML experience required
13
- 1-2 days (Instructor-led)
- 8-16 hours of training
14
- Instructor-led conceptual sessions
- Demonstrations and guided walkthroughs
- Real-world examples and use cases
- Interactive discussions
15
- This course serves as a foundational prerequisite for advanced Google Cloud courses and certifications, including:
- Professional Data Engineer
- Professional Machine Learning Engineer
- Advanced Big Data and ML courses on Google Cloud
16
- Training slides and reference materials
- Demonstration guides
- Certificate of course completion
This course includes
- 16+ Activity Modules
- 40 hours + lessons
- Lifetime access
- Certificate of completion
- Available on desktop and mobile
What Our Participants Say
Hear from participants who have experienced our training.
The facilitator’s wealth of experience was both striking and beneficial. He really helped translate concepts to real life.
The practical session where step by step of the lecture is broken down in visual and practice
I enjoyed the interactivity. It was also very direct, pointing out to us the key things to look for in the bidding, tender and proposal administration process, while also ensuri...
The practical session of the class which helped in giving a live run through of the necessary bidding and tender platform
The atmosphere was very pleasant, as it was an interactive and engaging session, which I personally enjoyed.
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