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Data Engineering Foundations Specialization

Build the Foundation for a Data Engineering Career. Develop hands-on experience with Python, SQL, and Relational Databases and master the fundamentals of the Data Engineering ecosystem.

Data engineering is one of the fastest-growing tech occupations, where the demand for skilled data engineers far outweighs the supply. The goal of data engineering is to make quality data available for fact-finding and data-driven decision making. This Specialization from IBM will help anyone interested in pursuing a career in data engineering by teaching fundamental skills to get started in this field. No prior data engineering experience is required to succeed in this Specialization.

The Specialization consists of 5 self-paced online courses covering skills required for data engineering, including the data engineering ecosystem and lifecycle, Python, SQL, and Relational Databases. You will learn these data engineering prerequisites through engaging videos and hands-on practice using real tools and real-world databases. You’ll develop your understanding of data engineering, gain skills that can be applied directly to a data career, and build the foundation of your data engineering career.

Upon successfully completing these courses, you will have the practical knowledge and experience to delve deeper into data engineering and work on more advanced data engineering projects.

Applied Learning Project

All courses in the Specialization contain multiple hands-on labs and assignments to help you gain practical experience and skills.

The projects range from working with data in multiple formats to transforming and loading that data into a single source to analyzing socio-economic data with SQL and working with advanced SQL techniques.

You will work hands-on with multiple real-world databases and tools including MySQL, PostgresSQL, IBM Db2, PhpMyAdmin, pgAdmin, IBM Cloud, Python, Jupyter notebooks, Watson Studio, etc.

Course Information

Estimated Time: Approximately 5 months to complete with the suggested pace of 3 hours/week



Course Information

Estimated Time: Approximately 5 months to complete with the suggested pace of 3 hours/week