Google Cloud - Data Engineering, Big Data, and Machine Learning on GCP Specialization
4.5 /5- Offered byCoursera
- Public/Government Institute
Data Engineering, Big Data, and Machine Learning on GCP Specialization at Coursera Overview
Duration | 1 month |
Total fee | ₹8,405 |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Go to Website  |
Credential | Certificate |
Data Engineering, Big Data, and Machine Learning on GCP Specialization at Coursera Highlights
- Earn a certificate of completion of the course
- Financial aid available
- Projects for real world exposure
- EMI payment option available
Data Engineering, Big Data, and Machine Learning on GCP Specialization at Coursera Course details
Google Cloud Platform
Data Management
Big Data
This professional certificate incorporates hands-on labs using Qwiklabs platform
These hands on components will let you apply the skills you learn
Projects incorporate Google Cloud products used within Qwiklabs
You will gain practical hands-on experience with the concepts explained throughout the modules
Data Engineering, Big Data, and Machine Learning on GCP Specialization at Coursera Curriculum
Google Cloud Big Data and Machine Learning Fundamentals
Identify the data-to-AI lifecycle on Google Cloud and the major products of big data and machine learning.
Design streaming pipelines with Dataflow and Pub/Sub and dDesign streaming pipelines with Dataflow and Pub/Sub.
Modernizing Data Lakes and Data Warehouses with Google Cloud
Differentiate between data lakes and data warehouses.
Explore use-cases for each type of storage and the available data lake and warehouse solutions on Google Cloud.
Discuss the role of a data engineer and the benefits of a successful data pipeline to business operations.
Building Batch Data Pipelines on Google Cloud
Review different methods of data loading: EL, ELT and ETL and when to use what
Run Hadoop on Dataproc, leverage Cloud Storage, and optimize Dataproc jobs
Build your data processing pipelines using Dataflow
Manage data pipelines with Data Fusion and Cloud Composer
Building Resilient Streaming Analytics Systems on Google Cloud
Interpret use-cases for real-time streaming analytics.
Manage data events using the Pub/Sub asynchronous messaging service.
Write streaming pipelines and run transformations where necessary.
Smart Analytics, Machine Learning, and AI on GCP
Differentiate between ML, AI and deep learning.
Discuss the use of ML API’s on unstructured data.
Execute BigQuery commands from notebooks.
Create ML models by using SQL syntax in BigQuery and without coding using Vertex AI AutoML.
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