About this course: This 1-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. At the end of this course, participants will be able to: • Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform • Use CloudSQL 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 • Choose between Cloud SQL, BigTable and Datastore • Train and use a neural network using TensorFlow • Choose between different data processing products on the Google Cloud Platform Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following: • A common query language such as SQL • Extract, transform, load activities • Data modeling • Machine learning and/or statistics • Programming in Python Google Account Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google is currently blocked in China). • There is a known issue with certain EU countries where individuals are not able to sign up, but you may sign up as "business" status and intend to see a potential economic benefit from the trial. More details at: https://support.google.com/cloud/answer/6090602 • More Google Cloud Platform free trial FAQs are available at: https://cloud.google.com/free-trial/
Who is this class for: This class is intended for Data analysts, Data scientists and Business analysts. It is also suitable for IT decision makers evaluating Google Cloud Platform for use by data scientists. This class is for people who do the following with big data: • Extracting, Loading, Transforming, cleaning, and validating data for use in analytics • Designing pipelines and architectures for data processing • Creating and maintaining machine learning and statistical models • Querying datasets, visualizing query results and creating reports
Taught by: Google Cloud Training
| Basic Info | Course 1 of 5 in the Data Engineering on Google Cloud Platform Specialization |
| Level | Intermediate |
| Commitment | 1 week of study, 6-10 hours/week |
| Language | English |
| Hardware Req | You'll need a Google Cloud Platform Free Trial account. Sign up at: https://cloud.google.com/free/ |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
- Video: Introduction to the Data and Machine Learning Specialization
- Reading: Please read me
- Video: Intro to Big Data and Machine Learning Fundamentals
- Video: Course Overview and Agenda
- Video: Meet Your Instructor
- Video: Welcome to Introduction to GCP and its Big Data Products
- Video: What is the Google Cloud Platform?
- Video: GCP Big Data Products
- Video: Usage Scenarios
- Video: How to do the Labs
- Reading: Sign Up for the Free Trial and Create a Project
- Video: Module 1 Resources
- Video: Welcome to Foundations of GCP Compute and Storage
- Video: CPUs On Demand
- Video: Lab 2a Overview
- Reading: Start Compute Engine Instance (Lab 2a)
- Video: Lab 2a Review
- Video: A Global Filesystem
- Video: Lab 2b Overview
- Reading: Interact with Cloud Storage
- Video: Lab 2b Review
- Video: Module 2 Review
- Video: Module 2 Resources
- Reading: Module 2 Resources
- Video: Intro to Managed Services for Common Use Cases
- Video: Stepping Stones to Transformation
- Video: Your SQL Database in the Cloud
- Video: Lab 3a Overview
- Reading: Setup rentals data in Cloud SQL
- Video: Lab 3a Review
- Video: Managed Hadoop in the Cloud
- Video: Lab 3b Overview
- Reading: Recommendations ML with Dataproc
- Video: Lab 3b Review
- Video: Module 3 Review
- Video: Module 3 Resources
- Video: Intro to Scaling Data Analysis: Change How You Compute with GCP
- Video: Fast Random Access
- Video: Interactive, Iterative Development & Demo
- Video: Warehouse and Interactively Query Petabytes
- Video: Lab 4a Overview
- Reading: Create ML Dataset with BigQuery (Lab 4a)
- Video: Lab 4a Review
- Video: Machine Learning with TensorFlow
- Video: Training and Creating a Neural Network Model
- Video: Lab 4b Overview
- Reading: Carry out ML with TensorFlow
- Video: Fully Build Machine Learning Models
- Video: Lab 4c Overview
- Reading: Machine Learning APIs (Lab 4c)
- Video: Module 4 Review
- Video: Module 4 Resources
- Reading: Module 4 Resources
- Video: Intro to Data Processing Architectures
- Video: Message-oriented Architectures
- Video: Serverless Data Pipelines
- Video: Module 5 Review
- Reading: Module 5 Resources
- Video: Summary of GCP, Big Data, and ML
- Video: Next Steps
- Video: Module 6 Resources
- Reading: Module 6 Resources
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
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| Purchase Course | |
|---|---|
| Access to course materials | Available |
| Access to graded materials | Available |
| Receive a final grade | Available |
| Earn a shareable Course Certificate | Available |
Exposition is really good--clear and concise.
It was nice and very interesting course.
Gets you up-to-speed quickly.
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LE
Was a very fantastic experience and the course manager was really good in delivering the course at a step by step and sometimes he will even repeat steps he has already covered in previous videos and for me this was really good for beginners