Online Course – Certified Professional Internship in Data Engineering, Big Data, and Machine Learning on Google Cloud

Data Engineering on the Google Cloud Platform. Professional development in data engineering using large data sets and machine learning.

Suggested by: Coursera (What is Coursera?)

Professional Certificate

Intermediate level

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Design and create data processing systems on the Google Cloud Platform
  • Leverage unstructured data with Spark and AA APIs in Cloud Dataproc
  • Process data in batches and in flow using an auto-adaptive data pipeline application in Cloud Dataflow
  • Generate business statistics from big data using Google BigQuery
  • Train, evaluate, and predict using machine learning models with Tensorflow and Cloud ML
  • Generate instant statistics from flow data

What you will learn in the course

Courses for which the course is suitable

  • Data processing systems developer
  • Data Analyst
  • Data Engineer
  • Data Pipeline Developer
  • Google Cloud Expert
  • Machine learning key
  • Data Project Manager
  • Business Analyst
  • Artificial Intelligence Developer
  • BigQuery Expert

Internship – a five-course course series

An accelerated five-week online internship where participants receive hands-on training in designing and building data processing systems on the Google Cloud Platform. Through a combination of presentations, demonstrations, and hands-on labs, participants will learn how to design data processing systems, build data pipelines from scratch, analyze data, and perform machine learning functions.

Skills learned:

  • Design and create data processing systems on the Google Cloud Platform
  • Leverage unstructured data with Spark and AA APIs in Cloud Dataproc
  • Process data in batches and in flow using an auto-adaptive data pipeline application in Cloud Dataflow
  • Generate business statistics from big data using Google BigQuery
  • Train, evaluate, and predict using machine learning models with Tensorflow and Cloud ML
  • Generate instant statistics from flow data

This course is designed for experienced developers responsible for managing large-scale data changes.

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Hands-on Learning Project

This internship includes hands-on labs. You must have a Google Account (you can use a Gmail account) and sign up for a free trial of Google Cloud Platform. The free trial is limited to 12 months or $300 in credits, whichever comes first. Therefore, our internship is designed to be completed within four weeks.

These hands-on components will allow you to apply the skills you learn during the recorded classes. Projects will include topics such as Google BigQuery, which is used and configured in code assignments. In addition, you will gain hands-on experience with the terms explained throughout the modules.

Details of the courses that make up the specialization

Google Cloud’s Big Data and Machine Learning

Course 1: 8 hours

4.7 (698 ratings)

  • Understand the data lifecycle in artificial intelligence processes on Google Cloud.
  • Design data pipelines with Dataflow and Pub/Sub.
  • Analyze big data at scale with BigQuery.
  • Learn about different options for creating machine learning solutions on Google Cloud.

Course 2: Upgrading Data Lakes and Data Warehouses with GCP – 8 hours

4.8 (93 ratings)

  • Differentiate between data lakes and data warehouses.
  • Explore the use cases of all types of storage.
  • Analyze the role of the data engineer.
  • Examine why data engineering should be performed in a cloud environment.

Course 3: Creating Data Pipelines in GCP – 17 hours

4.6 (44 ratings)

  • Go through different methods for uploading data: EL, ELT, and ETL.
  • Run Hadoop on Dataproc.
  • Build your own data processing pipelines with Dataflow.
  • Manage data pipelines with Data Fusion and Cloud Composer.

Course 4: Building Resilient Flow Analysis Systems in GCP – 10 hours

4.8 (35 ratings)

  • Interpret use cases for real-time stream analysis.
  • Manage data events with Pub/Sub.
  • Write stream pipes and perform conversions.
  • Collaborate between Dataflow, BigQuery, and Pub/Sub.

Course 5: Smart Analytics, Machine Learning, and AI on GCP – 6 hours

4.8 (41 ratings)

  • Distinguish between AI, IA, and machine learning.
  • Analyze the use of AI APIs on unstructured data.
  • Run BigQuery commands from notebooks.
  • Create AI models with SQL syntax in BigQuery.

Skills you will acquire

  • Category: Big Data
  • Category: BigQuery
  • Category: Machine Learning
  • Category: Google Cloud Platform