The class is designed to help IT professionals prepare for the Google Certified Professional—Data Engineer Certification Exam.
This Track Includes:
1. Google Cloud Big Data and Machine Learning Fundamentals – 1 Day
2. Data Engineering on Google Cloud Platform – 4 Days
Duration
5 Days
Description
1. Google Cloud Platform Fundamentals: Big Data & Machine Learning
This one-day instructor-led course introduces participants to the big data capabilities of Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants get an overview of the Google Cloud platform and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud Platform.
2. Data Engineering on Google Cloud Platform
This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data.
Objectives
1. Google Cloud Platform Fundamentals: Big Data & Machine Learning
This course teaches participants the following skills:
- Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform.
- Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform.
- Employ BigQuery and Cloud Datalab to carry out interactive data analysis.
- Train and use a neural network using TensorFlow.
- Employ ML APIs.
- Choose between different data processing products on the Google Cloud Platform.
2. Data Engineering on Google Cloud Platform
This course teaches participants the following Skills:
Design and build data processing systems on Google Cloud Platform
- Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow
- Derive business insights from extremely large datasets using Google BigQuery
- Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML
- Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
- Enable instant insights from streaming data.
Course Outline
1. Google Cloud Platform Fundamentals: Big Data & Machine Learning
Module 1: Google Cloud Platform Google Platform Fundamentals Overview. Google Cloud Platform Big Data Products.
Module 2: and Storage Fundamentals CPUs on demand (Compute Engine). A global filesystem (Cloud Storage). CloudShell. Lab: Set up a Ingest-Transform-Publish data processing pipeline.
Module 3: Analytics on the Cloud Stepping-stones to the cloud. Cloud SQL: your SQL database on the cloud. Lab: Importing data into CloudSQL and running queries. Spark on Dataproc. Lab: Machine Learning Recommendations with Spark on Dataproc.
Module 4: Data Analysis Fast random access. Datalab. BigQuery. Lab: Build machine learning dataset.
Module 5: Learning Machine Learning with TensorFlow. Lab: Carry out ML with TensorFlow Pre-built models for common needs. Lab: Employ ML APIs.
Module 6: Processing Architectures Message-oriented architectures with Pub/Sub. Creating pipelines with Dataflow. Reference architecture for real-time and batch data processing.
Module 7: Why GCP? Where to go from here Additional Resources
2. Data Engineering on Google Cloud Platform
Module 1: Google Cloud Dataproc Overview
Module 2: Running Dataproc Jobs
Module 3: Integrating Dataproc with Google Cloud Platform
Module 4: Making Sense of Unstructured Data with Google’s Machine Learning APIs
Module 5: Serverless data analysis with BigQuery
Module 6: Serverless, autoscaling data pipelines with Dataflow
Module 7: Getting started with Machine Learning
Module 8: Building ML models with Tensorflow
Module 9: Scaling ML models with CloudML
Module 10: Feature Engineering
Module 11: Architecture of streaming analytics pipelines
Module 12: Ingesting Variable Volumes
Module 13: Implementing streaming pipelines
Module 14: Streaming analytics and dashboards
Module 15: High throughput and low-latency with Bigtable
Audience
This course is intended for the following Participants:
Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform.
- Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports.
- Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists
Extracting, Loading, Transforming, cleaning, and validating data
- Designing pipelines and architectures for data processing
- Creating and maintaining machine learning and statistical models
- Querying datasets, visualizing query results and creating reports
Prerequisites
To get the most out of this course, participants should:
Basic proficiency with common query language such as SQL.
- Experience with data modeling, extract, transform, load activities.
- Developing applications using a common programming language such Python.
- Familiarity with machine learning and/or statistics.
Completed Google Cloud Fundamentals: Big Data & Machine Learning course OR have equivalent experience
- Basic proficiency with common query language such as SQL
- Experience with data modeling, extract, transform, load activities
- Developing applications using a common programming language such as Python
- Familiarity with Machine Learning and/or statistics
Certification
Google Cloud Certified – Professional Data Engineer
Below is a Course Schedule for this:
2024
| Jan | Feb | Mar | Apr | May | Jun |
|---|---|---|---|---|---|
| 22,23,24,25,26 | 19,20,21,22,23 | 19,20,21,22,23 | 23,24,25,26,27 | 27,28,29,30,31 | 24,25,26,27,28 |
| July | Aug | Sep | Oct | Nov | Dec |
|---|---|---|---|---|---|
| 17,18,19,20,21 | 21,22,23,24,25 | 25,26,27,28,29 | 23,24,25,26,27 | 20,21,22,23,24 | 18,19,20,21,22 |
Duration: 4 Days
Course Fee
| Course Fee | $3750.00 |
| SME (Company Sponsored) – All Singaporean and Permanent Resident Employee | $ |
| Singapore Citizens aged 40 years old and above | $ |
| Singapore Citizen and Permanent Resident aged 21 years old and above | $ |
