Best Data Engineering Courses Online (2020)

The demand for Data Engineers is on the rise. This is no surprise as modern-day companies have intensified their competition to beat their rivals in the market. It is therefore important to state that any 21st-century business operating in any free-market environment may risk losing their market share to their competitors, hence, the services for good Data Engineers. The Data Engineering profession is new in the industry, which is why it is yet to garner much academic credence as only few universities take it as a course of study. In this article, we are listing some top-performing online platforms anybody can sign up for an online course on Data Engineering

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Best Cybersecurity Courses Online
Rating 4.5 out of 5
4.5/5

Udacity.com has always been popular for its numerous online courses, and Data Engineering is one of them. Course participants are expected to go through topics like designing data models, a Capstone Project, Data Pipelines with Airflow, Spark and Data Lakes, and Cloud Data Warehouses. Interested participants in this program are expected to have an intermediate knowledge of Python and SQL. This course has 2000+ projects reviewed with 5 courses making up the syllabus. It has 1506 students currently participating in this course.

Key Learnings:

  • This course is guaranteed to teach you how to create a rational NoSQL data model that can solve different needs of data customers. It takes students on how to use ETL to create a database in Apache Cassandra, and PostgreSQL respectively.
  • This course is meant to expand your knowledge on data warehousing skills, as well as build your understanding of data infrastructure. Again, participants can develop a cloud-based data warehouse system on the Amazon Web Services (AWS) platform.
  • Students can automate, monitor data pipelines, and schedule with the help of Apache Airflow. You will also be able to track data, run a data quality check, and comfortably apply data pipelines in production.

Course Duration: 5 Months

Rating: 4.5 out of 5

Rating 4.6 out of 5
4.6/5

Here you will learn the skills needed to process big data for analytics and machine learning. Other skills you are expected to learn from this course include Tensorflow, BigQuery, and Google Cloud.

This course already has 15,644 participants and has gotten individual ratings on each of the 6 topics that made up the syllabus.

Those topics include: Google Cloud Platform Big Data and Machine Learning Fundamentals, Modernizing Data Lakes and Data Warehouses with GCP, Building Batch Data Pipelines on GCP, Building Resilient Streaming Analytics Systems on GCP, Smart Analytics, Machine Learning, and AI on GCP, and Preparing for the Google Cloud Professional Data Engineer Exam.

Key Learnings:

  • You will get to master the necessary skills needed to be successful as a data engineer.
  • The program prepares you for a professional Data engineering certification.
  • You will master the infrastructure and other services by Google Cloud Platform.
  • Get acquainted with the process involved in analyzing big data and machine learning.
  • Gets you prepared for the Google Cloud Professional Data Engineer Exam.

Course Duration: 4 Months (4 hours/week)

Rating: 4.6 out of 5

Rating 4.6 out of 5
4.6/5

This is a platform solely designed to teach users how to become a data engineer by creating data pipelines and make it work with large data. Unlike other platforms, this one teaches you data engineering using programs like Python and Pandas and how to load them in a Postgres database.

Prospective users are required to undertake 13 topics that make up the course syllabus. At the moment, 1803 students are currently taking the course. These 13 topics include Python Fundamentals, Python Intermediate, Programming Concepts with Python, Algorithm Complexity, SQL Fundamentals, Intermediate SQL for Data Analysis, Postgres for Data Engineers, Optimizing Postgres Databases, Processing Large Datasets in Pandas, Optimizing Code Performance on Large Datasets, Algorithms & Data Structures, Recursion & Trees, and Building a Data Pipeline.

Key Learnings:

  • More convenient ways to work with a production database.
  • It will guide you through some major computer science concepts e.g. recursion, algorithms, data structures.
  • It guides you through better ways of handling big sets of data using Pandas and Python and loading them into a Postgres database.
  • You will get to learn how to build a Data pipeline.

Course Duration: 12 Months (13 syllabus).

Rating: 4.6 out of 5

Rating 4.6 out of 5
4.6/5

This online course was designed to help participants with 10 different courses that set students on the path of becoming a professional Data engineer. Those courses include Foundations, Learning NoSQL Databases, HBase Essential Training, Architecting Big Data Applications, Data Applications: Batch .jMode, SQL: Data Reporting and Analysis (2016), Advanced NoSQL for Data Science, SQL Tips, Tricks, & Techniques, and NoSQL for SQL Professionals. This course has 1507 people participating in it already.

Key Learnings:

  • This program presents you with the opportunity to build a strong foundation in both data science DevOps, and Data engineering.
  • Discover more techniques applied in the field of Data engineering.
  • Explore more intricate skills in data application programming.

Course Duration: 12 months (16 hours, 11 mins of content play)

Rating: 4.6 out of 5

Rating 4.5 out of 5
4.5/5

This Data Engineering program is a bit different from the previous one offered on same platform. Unlike the other, it comes with other complementary courses like the Big Data, and Machine Learning on GCP Specialization. Already, there are 8,320 students signed up for this course. Some of the topics expected while on this course include: Google Cloud Platform Big Data and Machine Learning Fundamentals, Modernizing Data Lakes and Data Warehouses with GCP, Building Batch Data Pipelines on GCP, Building Resilient Streaming Analytics Systems on GCP, Smart Analytics, Machine Learning, and AI on GCP.

Key Learnings:

  • Ability to process large data for machine learning and analytics
  • Basic requirement for creating new machine learning models
  • Building a streaming data dashboards and pipeline

Course Duration: 3 months (5 hours/ week)

Rating: 4.5 out of 5

Conclusion

The above-listed platforms are considered the best places to learn about Data engineering, their tutors are considered the best in the industry, and the user reviews are higher than their counterparts. Join the (data) train and be the best you can. 

Happy learning!