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  • Data Engineering

Data Engineering Courses

Data engineering courses can help you learn data modeling, ETL (extract, transform, load) processes, and data warehousing techniques. You can build skills in data pipeline construction, database management, and ensuring data quality and integrity. Many courses introduce tools like Apache Spark, Hadoop, and SQL, that support processing large datasets and optimizing data workflows. You’ll also explore cloud platforms such as AWS and Azure, which facilitate scalable data solutions and enhance your ability to manage data in various environments.


More to explore:

Popular Data Engineering Courses and Certifications


  • D

    Duke University

    MySQL-for-Data-Engineering

    Skills you'll gain: MySQL, Bash (Scripting Language), Scripting, Database Management, Relational Databases, Command-Line Interface, SQL, Databases, Data Import/Export, Database Application, Data Pipelines, Data Manipulation, Python Programming, Linux

    4.2
    Rating, 4.2 out of 5 stars
    ·
    13 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • Status: Free
    Free
    C

    Coursera

    Data Science Challenge

    Skills you'll gain: Applied Machine Learning, Jupyter, Machine Learning Algorithms, Model Evaluation, Machine Learning, Predictive Modeling, Data Science, Python Programming, Predictive Analytics, Data Analysis, Technical Communication

    4.6
    Rating, 4.6 out of 5 stars
    ·
    210 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • C

    Coursera

    Python for Data Analysis: Pandas & NumPy

    Skills you'll gain: Pandas (Python Package), NumPy, Data Analysis, Data Science, Python Programming, Data Structures, Exploratory Data Analysis, Data Manipulation, Computer Programming

    4.5
    Rating, 4.5 out of 5 stars
    ·
    382 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • Status: Free
    Free
    C

    Coursera

    Data Modeling with Power BI: Advanced DAX Calculations

    Skills you'll gain: Data Analysis Expressions (DAX), Power BI, Business Analysis, Financial Data, Business Intelligence, Data Modeling, Financial Analysis, Time Series Analysis and Forecasting, Data Visualization, Data Import/Export

    4.5
    Rating, 4.5 out of 5 stars
    ·
    16 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • C

    Coursera

    Data Management with Databricks: Big Data with Delta Lakes

    Skills you'll gain: Databricks, Data Lakes, Data Pipelines, Data Integration, Dashboard, PySpark, SQL, Apache Spark, Data Management, Data Transformation, Version Control

    4.1
    Rating, 4.1 out of 5 stars
    ·
    32 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • Status: Free
    Free
    C

    Coursera

    PySpark Foundations: Process, analyze, and summarize data

    Skills you'll gain: PySpark, Apache Spark, Data Visualization Software, Data Analysis, Exploratory Data Analysis, Data Cleansing, Data Processing, Data Manipulation, Big Data, Jupyter, Pandas (Python Package), People Analytics

    Intermediate · Guided Project · Less Than 2 Hours

What brings you to Coursera today?

  • C

    Coursera

    Create Mapping Data Flows in Azure Data Factory

    Skills you'll gain: Microsoft Azure, Data Transformation, Data Pipelines, Data Flow Diagrams (DFDs), Extract, Transform, Load, Dataflow, Data Storage Technologies, Data Integration, Data Storage

    4.8
    Rating, 4.8 out of 5 stars
    ·
    8 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • C

    Coursera

    Presenting Data Using Charts with Canva

    Skills you'll gain: Presentations, Sales Presentations, Sales Presentation, Data Visualization, Productivity Software, Design

    4.3
    Rating, 4.3 out of 5 stars
    ·
    16 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • C

    Coursera

    Importing Data into R

    Skills you'll gain: Web Scraping, Relational Databases, Statistical Software, Data Import/Export, R Programming, MySQL, Data Capture, Microsoft Excel

    4.6
    Rating, 4.6 out of 5 stars
    ·
    19 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • Status: Free
    Free
    C

    Coursera

    Perform exploratory data analysis on retail data with Python

    Skills you'll gain: Data-Driven Decision-Making, Data Visualization, Business Analytics, Data Analysis, Data Cleansing, Statistical Analysis, Data Preprocessing, Exploratory Data Analysis, Matplotlib, Customer Analysis, Analytical Skills, Trend Analysis, Pandas (Python Package), Python Programming, Jupyter

    4.6
    Rating, 4.6 out of 5 stars
    ·
    21 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • C

    Coursera

    Crash Course on Interactive Data Visualization with Plotly

    Skills you'll gain: Plotly, Histogram, Interactive Data Visualization, Scatter Plots, Plot (Graphics), Data Visualization, Data Analysis, Python Programming

    4.8
    Rating, 4.8 out of 5 stars
    ·
    10 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • C

    Coursera

    Introduction to Data Analysis using Microsoft Excel

    Skills you'll gain: Microsoft Excel, Excel Formulas, Spreadsheet Software, Pivot Tables And Charts, Data Analysis, Data Manipulation, Microsoft Office, Data Mining

    4.7
    Rating, 4.7 out of 5 stars
    ·
    4.7K reviews

    Intermediate · Guided Project · Less Than 2 Hours

1234…71

In summary, here are 10 of our most popular data engineering courses

  • MySQL-for-Data-Engineering: Duke University
  • Data Science Challenge: Coursera
  • Python for Data Analysis: Pandas & NumPy: Coursera
  • Data Modeling with Power BI: Advanced DAX Calculations : Coursera
  • Data Management with Databricks: Big Data with Delta Lakes: Coursera
  • PySpark Foundations: Process, analyze, and summarize data: Coursera
  • Create Mapping Data Flows in Azure Data Factory: Coursera
  • Presenting Data Using Charts with Canva: Coursera
  • Importing Data into R: Coursera
  • Perform exploratory data analysis on retail data with Python: Coursera

Skills you can learn in Software Development

Programming Language (34)
Google (25)
Computer Program (21)
Software Testing (21)
Web (19)
Google Cloud Platform (18)
Application Programming Interfaces (17)
Data Structure (16)
Problem Solving (14)
Object-oriented Programming (13)
Kubernetes (10)
List & Label (10)

Frequently Asked Questions about Data Engineering

Data engineering is the practice of designing, building, and maintaining the systems and architecture that enable organizations to collect, store, and analyze data effectively. It plays a crucial role in today's data-driven world, where businesses rely on data to make informed decisions, optimize operations, and enhance customer experiences. By ensuring that data is accessible, reliable, and secure, data engineers empower organizations to harness the full potential of their data assets.‎

In the field of data engineering, a variety of job roles are available, including Data Engineer, Data Architect, ETL Developer, and Data Warehouse Engineer. These positions often involve working with large datasets, developing data pipelines, and collaborating with data scientists and analysts to ensure that data is structured and available for analysis. With the growing demand for data professionals, opportunities in this field are expanding across industries such as finance, healthcare, technology, and retail.‎

To pursue a career in data engineering, you should focus on developing a range of technical skills. Key competencies include proficiency in programming languages such as Python and SQL, knowledge of data warehousing solutions, and familiarity with cloud platforms like AWS or Google Cloud. Additionally, understanding data modeling, ETL processes, and big data technologies like Hadoop and Spark can be beneficial. Soft skills such as problem-solving and effective communication are also important for collaborating with cross-functional teams.‎

There are several excellent online courses available for those interested in data engineering. Notable options include the DeepLearning.AI Data Engineering Professional Certificate and the IBM Data Engineering Professional Certificate. These programs provide a comprehensive curriculum that covers essential skills and tools needed in the field, making them great choices for learners at various stages of their careers.‎

Yes. You can start learning data engineering on Coursera for free in two ways:

  1. Preview the first module of many data engineering courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

If you want to keep learning, earn a certificate in data engineering, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

To learn data engineering effectively, start by identifying your current skill level and areas for improvement. Begin with foundational courses that cover programming and database management. Gradually progress to more specialized topics such as data warehousing and cloud technologies. Engage in hands-on projects to apply what you learn, and consider joining online communities or forums to connect with other learners and professionals in the field.‎

Data engineering courses typically cover a range of topics, including data modeling, ETL (Extract, Transform, Load) processes, data warehousing, and big data technologies. You may also explore cloud computing platforms, data pipeline design, and data governance. Courses often include practical exercises and projects to help reinforce your understanding and application of these concepts in real-world scenarios.‎

For training and upskilling employees in data engineering, programs like the IBM Data Warehouse Engineer Professional Certificate and the Snowflake Data Engineering Professional Certificate are excellent choices. These courses are designed to equip professionals with the necessary skills to manage and analyze data effectively, making them suitable for organizations looking to enhance their workforce's capabilities in data engineering.‎

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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