Top 5 Free Online Course For Data Engineers


A Data Engineer is a professional who designs, builds, and maintains the infrastructure to store, process, and analyze large and complex data sets. The role of a Data Engineer is crucial in helping organizations turn data into actionable insights and drive business decisions.

If you’re interested in becoming a Data Engineer, you can find a variety of free online courses that can help you build the necessary skills and knowledge. Here are some popular platforms that offer free courses


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1. Introduction to Data Engineering

This course introduces you to the basic concepts, processes, and tools you need to know to acquire basic data engineering skills. You will gain an understanding of modern data ecosystems and the roles data engineers, data scientists, and data analysts play in these ecosystems.

The data engineering ecosystem includes several distinct components. It includes a variety of data types, formats, and data sources. Data channels collect data from multiple sources, transform it into analysis-ready data, and make it available to data consumers for analysis and decision-making. Data warehouses such as relational and non-relational databases, data warehouses, data marts, data lakes, and big data warehouses process and store this data. The data integration platform combines disparate data into a unified view for data consumers. In this course, you will learn about all of these components. You will also learn about big data and how to use several tools to process big data.


  • List the essential skills required for entry-level data engineering roles.
  • Discuss the different phases and concepts in the data engineering life cycle.
  • Describe and give examples of data engineering technologies such as relational databases, NoSQL data stores, big data engines, and others.
  • Summarize the concepts of data security, governance, and compliance.

2. Python Project for Data Engineering

Show off your data engineering skills with this Python project! This mini-course aims to apply basic Python skills by applying various data collection and manipulation techniques. Assuming the role of a data engineer, you will extract data from multiple file formats, convert it to specific data types and then load it into a single source for analysis. Continue your course and test your knowledge of implementing web scraping and data mining with APIs, all with the help of some hands-on labs.

At the end of this project, you will demonstrate your knowledge with essential skills in Information and Extract, Transform, and Load (ETL) Engineering, Jupyter Notebook, and of course Python programming.

After completing this course, you’ll gain the confidence to start collecting large datasets, web scraping, using APIs, and performing ETL tasks to leverage valuable data management skills – all using Python.


  • Demonstrate your Python skills for data engineering assignments
  • Implement web scraping and use the Python data collection API
  • Take on the role of a data engineer working on a real project
  • Extract, convert, and load (ETL) data with Jupyter notebooks

3.Python for Data Science, AI & Development

Get started learning Python with a self-paced, beginner-friendly course led by an expert. Python is one of the most popular languages ​​in the world of programming and data science, and the demand for people who can implement Python has never been higher.

This introductory Python course will have you programming in Python from scratch in just a few hours – no programming experience required! You will learn the basics of Python and the different data types. You’ll be introduced to Python data structures such as lists and tuples, as well as logical concepts such as conditionals and branching. You’ll be using Python libraries like Pandas, Numpy, and Beautiful Soup. You’ll also use Python to perform tasks such as data collection and web scraping with the API.

You will practice and apply what you have learned in practical exercises with Jupyter Notebooks. By the end of this course, you’ll feel confident creating simple programs, working with data, and automating real-world tasks using Python.


  • Explain the basics of Python, including data types, expressions, variables, and data structures.
  • Implement Python programming logic using branches, loops, functions, objects, and classes.
  • Demonstrate proficiency in using Python libraries such as Pandas, Numpy, and Beautiful Soup.
  • Access web data using API and web scraping from Python in Jupyter Notebooks.

4. Databases and SQL for Data Science with Python

Working knowledge of SQL (or Structured Query Language) is a must for data professionals such as data scientists, data analysts, and data engineers. Most of the world’s data reside in databases. SQL is a powerful language used to communicate with and retrieve data from databases.

In this course, you’ll learn SQL from the ground up—from the basics of select statements to advanced concepts like JOINs.

You will:
-Write basic SQL statements such as SELECT, INSERT, UPDATE, and DELETE
-Filter result sets by WHERE, COUNT, DISTINCT, and LIMIT clauses
-distinguish between DML and DDL
-CREATE, ALTER, DROP, and load tables
-Use of patterns and ranges of strings; ORDER and GROUP result sets and built-in database capabilities
-Create subqueries and query data from multiple tables

  • Access databases as a data scientist using a Jupyter notebook with SQL and Python
  • Work with advanced concepts like stored procedures, views, ACID transactions, inner and outer joins


  • Analyze data in databases using SQL and Python.
  • Build relational databases in the cloud and work with tables.
  • Write SQL statements including SELECT, INSERT, UPDATE, and DELETE.
  • Build more powerful queries using advanced SQL techniques such as views, transactions, stored procedures, and joins.

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5. Data Engineering and Machine Learning using Spark

Businesses need skilled and visionary big data professionals who can apply their business and technical skills to unstructured data such as tweets, posts, photos, audio, video, sensor data, satellite imagery, and more to identify the behavior and preferences of potential customers. customers, competitors, etc.

In this short course, you will gain hands-on skills as you learn how to work with Apache Spark for data engineering and machine learning (ML) applications. You’ll work directly with Spark MLlib, Spark Structured Streaming, and others to perform the extraction, transformation, and loading (ETL) tasks, as well as regression, classification, and clustering.

The course ends with a project where you apply your Spark skills to an ETL for an ML workflow use case.


  • Explain how structured Spark streaming and data streaming enable machine learning and AI tasks.
  • Define graph theory, explain Apache Spark GraphFrames and identify suitable data for GraphFrames.
  • Describe how the ETL process works with Apache Spark and machine learning, and extend this knowledge to the capabilities and benefits of MLlib Spark.
  • Describe supervised learning, unsupervised learning, clustering, and how to use the k-means clustering algorithm with Spark MLlib
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