## Professional Certificate – 16-Course Series Prepare for a career in the emerging field of data engineering. In this program, you can learn essential skills like Python, SQL, and databases to be **job-ready in less than 5 months**. Data engineering is about building systems to collect, process, and **organize raw data into useful information**. Data engineers provide the foundational knowledge that data scientists and business intelligence analysts need to make decisions. This program will teach you the fundamentals of data engineering that employers are looking for in industry roles, including Python, one of the most widely used programming languages. In addition, you will master **SQL, RDBMS, ETL, data warehouses, NoSQL, big data, and Spark** through hands-on labs and projects. **You will learn to use the Python programming language** and Linux/UNIX scripts to extract, transform, and load (ETL) data. You’ll work with relational databases (RDBMS) and query data using SQL commands, as well as NoSQL databases and unstructured data. You’ll also learn how generative AI tools and techniques are used in data engineering. Upon completion of the program, you’ll have a **project portfolio** and a professional certificate from IBM to showcase your expertise. Additionally, you’ll earn a digital badge from IBM and **gain access to career resources** to help you with your job search, including mock interviews and resume support. This program is recommended by ACE®—when you graduate, **you can earn up to 12 academic credits**. ## Hands-on Learning Project During this professional certificate, you’ll complete labs and hands-on projects to gain hands-on experience in Python, SQL, relational databases, NoSQL databases, Apache Spark, building data pipelines, managing databases, and working with data warehouses. ### Projects:
– Design a relational database to help a coffee chain improve its operations. – Use SQL to query demographic data on population, crime, and school surveys. – Write a Linux Bash script that backs up changed files. – Set up, test, and optimize a data platform containing MySQL, PostgreSQL, and IBM Db2 databases. – Analyze road traffic data to perform ETL and create pipelines using Airflow and Kafka. – Design and build a data warehouse for a solid waste management company. – Transfer, query, and analyze data in MongoDB, Cassandra, and Cloudant NoSQL databases. – Train a machine learning model by creating an Apache Spark application. – Design, install, and manage an end-to-end data engineering platform.
