Data Engineering, Big Data, and Machine Learning on GCP

Google Cloud and Google via Coursera Specialization

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Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Duration & workload

9 weeks, 10 hours a week

Found in

Overview

Class Central Tips

This five-week, accelerated online specialization 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. This course teaches the following skills: • Design and build data processing systems on Google Cloud Platform • Leverage unstructured data using Spark and ML APIs on Cloud Dataproc • 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 • Enable instant insights from streaming data This class is intended for developers who are responsible for: • 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 >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service

Syllabus

Course 1: Google Cloud Platform Big Data and Machine Learning Fundamentals
- This course introduces participants to the big data capabilities of Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants get an overview of Google Cloud 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.

Course 2: Leveraging Unstructured Data with Cloud Dataproc on Google Cloud Platform
- NOTE: This course will be ending soon and the last day for enrollment will be January 21, 2020. This 1-week, accelerated course builds upon previous courses in the Data Engineering on Google Cloud Platform specialization. Through a combination of video lectures, demonstrations, and hands-on labs, you'll learn how to create and manage computing clusters to run Hadoop, Spark, Pig and/or Hive jobs on Google Cloud Platform. You will also learn how to access various cloud storage options from their compute clusters and integrate Google’s machine learning capabilities into their analytics programs. In the hands-on labs, you will create and manage Dataproc Clusters using the Web Console and the CLI, and use cluster to run Spark and Pig jobs. You will then create iPython notebooks that integrate with BigQuery and storage and utilize Spark. Finally, you integrate the machine learning APIs into your data analysis. Pre-requisites • Google Cloud Platform Big Data & Machine Learning Fundamentals (or equivalent experience) • Some knowledge of Python NOTE: This course will be ending soon and the last day for enrollment will be January 22, 2020

Course 3: Serverless Data Analysis with Google BigQuery and Cloud Dataflow
- NOTE: This course will be ending soon and the last day for enrollment will be January 21, 2020. This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of instructor-led presentations, demonstrations, and hands-on labs, students learn how to carry out no-ops data warehousing, analysis and pipeline processing. Prerequisites: • Google Cloud Platform Big Data and Machine Learning Fundamentals • Experience using a SQL-like query language to analyze data • Knowledge of either Python or Java Google Account Notes: • Google services are currently unavailable in China. NOTE: This course will be ending soon and the last day for enrollment will be January 22, 2020

Course 4: Serverless Machine Learning with Tensorflow on Google Cloud Platform
- NOTE: This course will be ending soon and the last day for enrollment will be January 21, 2020 This one-week accelerated on-demand course provides participants a a hands-on introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn machine learning (ML) and TensorFlow concepts, and develop hands-on skills in developing, evaluating, and productionizing ML models. OBJECTIVES This course teaches participants the following skills: ● Identify use cases for machine learning ● Build an ML model using TensorFlow ● Build scalable, deployable ML models using Cloud ML ● Know the importance of preprocessing and combining features ● Incorporate advanced ML concepts into their models ● Productionize trained ML models PREREQUISITES To get the most of out of this course, participants should have: ● Completed Google Cloud Fundamentals- Big Data and 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 Python ● Familiarity with Machine Learning and/or statistics Google Account Notes: • Google services are currently unavailable in China.

Course 5: Building Resilient Streaming Systems on Google Cloud Platform
- NOTE: This course will be ending soon and the last day for enrollment will be January 21, 2020. This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of video lectures, demonstrations, and hands-on labs, you'll learn how to build streaming data pipelines using Google Cloud Pub/Sub and Dataflow to enable real-time decision making. You will also learn how to build dashboards to render tailored output for various stakeholder audience. Prerequisites: • Google Cloud Platform Big Data and Machine Learning Fundamentals (or equivalent experience) • Some knowledge of Java Objectives: • Understand use-cases for real-time streaming analytics • Use Google Cloud PubSub asynchronous messaging service to manage data events • Write streaming pipelines and run transformations where necessary • Get familiar with both sides of a streaming pipeline: production and consumption • Interoperate Dataflow, BigQuery and Cloud Pub/Sub for real-time streaming and analysis

Courses

5 reviews
9 hours 29 minutes
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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
0 reviews
1 week
View details

This one-week accelerated on-demand course provides participants a a hands-on introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn machine learning (ML) and TensorFlow concepts, and develop hands-on skills in developing, evaluating, and productionizing ML models.

OBJECTIVES

This course teaches participants the following skills:

● Identify use cases for machine learning

● Build an ML model using TensorFlow

● Build scalable, deployable ML models using Cloud ML

● Know the importance of preprocessing and combining features

● Incorporate advanced ML concepts into their models

● Productionize trained ML models

PREREQUISITES

To get the most of out of this course, participants should have:

● Completed Google Cloud Fundamentals- Big Data and 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 Python

● Familiarity with Machine Learning and/or statistics

Google Account Notes:
• Google services are currently unavailable in China.
0 reviews
1 week, 6-8 hours a week, 6-8 hours a week
View details

This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of video lectures, demonstrations, and hands-on labs, you'll learn how to build streaming data pipelines using Google Cloud Pub/Sub and Dataflow to enable real-time decision making. You will also learn how to build dashboards to render tailored output for various stakeholder audience.

Prerequisites:
• Google Cloud Platform Big Data and Machine Learning Fundamentals (or equivalent experience)
• Some knowledge of Java

Objectives:
• Understand use-cases for real-time streaming analytics
• Use Google Cloud PubSub asynchronous messaging service to manage data events
• Write streaming pipelines and run transformations where necessary
• Get familiar with both sides of a streaming pipeline: production and consumption
• Interoperate Dataflow, BigQuery and Cloud Pub/Sub for real-time streaming and analysis
0 reviews
1 week, 6-8 hours a week, 6-8 hours a week
View details

This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of instructor-led presentations, demonstrations, and hands-on labs, students learn how to carry out no-ops data warehousing, analysis and pipeline processing.

Prerequisites:
• Google Cloud Platform Big Data and Machine Learning Fundamentals
• Experience using a SQL-like query language to analyze data
• Knowledge of either Python or Java

Google Account Notes:
• Google services are currently unavailable in China.
0 reviews
1 week, 5-7 hours a week, 5-7 hours a week
View details

This 1-week, accelerated course builds upon previous courses in the Data Engineering on Google Cloud Platform specialization. Through a combination of video lectures, demonstrations, and hands-on labs, you'll learn how to create and manage computing clusters to run Hadoop, Spark, Pig and/or Hive jobs on Google Cloud Platform. You will also learn how to access various cloud storage options from their compute clusters and integrate Google’s machine learning capabilities into their analytics programs.

In the hands-on labs, you will create and manage Dataproc Clusters using the Web Console and the CLI, and use cluster to run Spark and Pig jobs. You will then create iPython notebooks that integrate with BigQuery and storage and utilize Spark. Finally, you integrate the machine learning APIs into your data analysis.

Pre-requisites
• Google Cloud Platform Big Data & Machine Learning Fundamentals (or equivalent experience)
• Some knowledge of Python