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Updated in [July 26th, 2023]

This program prepares individuals for a career in the high-growth field of data engineering. In less than five months, participants will learn in-demand skills such as Python, SQL, and databases to become job-ready. Data engineering involves building systems to gather, process, and organize raw data into usable information, and manage data. The work data engineers do provides the foundational information that data scientists and business intelligence (BI) analysts use to make recommendations and decisions. This program will teach participants the foundational data engineering skills employers are seeking for entry level data engineering roles, including Python, one of the most widely used programming languages. Participants will also master SQL, RDBMS, ETL, Data Warehousing, NoSQL, Big Data, and Spark with hands-on labs and projects. They will learn to use Python programming language and Linux/UNIX shell scripts to extract, transform and load (ETL) data. They will also work with Relational Databases (RDBMS) and query data using SQL statements and use NoSQL databases as well as unstructured data. Upon completion of the program, participants will receive a Professional Certificate from IBM, an IBM Digital badge, and access to career resources to help with job search, including mock interviews and resume support. This program is ACE® recommended, and participants can earn up to 12 college credits. Throughout the program, participants will complete hands-on labs and projects to gain practical experience with Python, SQL, relational databases, NoSQL databases, Apache Spark, building data pipelines, managing databases, and working with data warehouses. Projects include designing a relational database to help a coffee franchise improve operations, using SQL to query census, crime, and school demographic data sets, writing a Bash shell script on Linux that backups changed files, setting up, testing, and optimizing a data platform that contains MySQL, PostgreSQL, and IBM Db2 databases, analyzing road traffic data to perform ETL and create a pipeline using Airflow and Kafka, designing and implementing a data warehouse for a solid-waste management company, moving, querying, and analyzing data in MongoDB, Cassandra, and Cloudant NoSQL databases, training a machine learning model by creating an Apache Spark application, and designing, deploying, and managing an end-to-end data engineering platform.