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Professional Certificate in
Data Engineering

What you will learn

  • Describe the core concepts, processes, tools and technologies in the field of data engineering.
  • Demonstrate your aptitude with RDBMS fundamentals including design & creation of databases, schemas, tables; DB administration, security & working with MySQL, PostgreSQL & IBM Db2.
  • Demonstrate your proficiency with SQL query language, SELECT, INSERT, UPDATE, DELETE statements, database functions, stored procs, working with multiple tables, JOINs, & transactions.
  • Explain NoSQL and big data concepts including practice with MongoDB, Cassandra, IBM Cloudant, Apache Hadoop, Apache Spark, SparkSQL, SparkML, Spark Streaming.
  • Describe ETL tools, data pipelines using Python, shell scripts with Linux, Apache Airflow and Apache Kafka.
  • Describe Data Lakes, Data Marts and Enterprise Data Warehouses (EDW) and design them using Star and Snowflake schemas.
  • Design and populate Data Warehouses and analyze their data with Business Intelligence (BI) tools like Cognos Analytics.

Organizations have more data at their disposal today than ever before. The vast amount of data that organizations are capturing, along with their desire to extract meaningful insights is driving an urgent demand for Data Engineers.

Data Engineers play a fundamental role in harnessing data that enable organizations to apply business intelligence for making informed decisions. Today’s Data Engineers require a broad set of skills to develop and optimize data systems and make data available to the organization for analysis.

This Professional Certificate provides you the job-ready skills you will need to launch your career as an entry level data engineer.

Upon completing this Professional Certificate, you will have extensive knowledge and practical experience with cloud-based relational databases (RDBMS) and NoSQL data repositories, working with Python, Bash and SQL, processing big data with Apache Hadoop and Apache Spark, using ETL (extract, transform and load) tools, creating data pipelines, using Apache Kafka and Airflow, designing, populating, and querying data warehouses and utilizing business intelligence tools.

Within each course, you’ll gain practical experience with hands-on labs and projects for building your portfolio. In the final Capstone project, you’ll apply your knowledge and skills attained throughout this program and demonstrate your ability to perform as a Data Engineer.

This program does not require any prior data engineering or programming experience.

A program subscription gives you full verified access to all courses and materials within the program you’ve enrolled in, for as long as your subscription is active. Monthly subscription pricing can help you manage your enrollment costs — instead of paying more up front, you pay a smaller amount per month for only as long as you need access. You can cancel your subscription at any time for no additional fee.

Expert instruction
14 skill-building courses
Progress at your own speed
1 year 2 months
3 - 4 hours per week
After 7-day free trial

Courses in this program

  1. IBM's Data Engineering Professional Certificate

  2. 9–10 hours per week, for 4 weeks

    Learn about data engineering concepts, ecosystem, and lifecycle. Also learn about the systems, processes, and tools you need as a Data Engineer in order to gather, transform, load, process, query, and manage data so that it can be leveraged by data consumers for operations, and decision-making.

  3. 4–10 hours per week, for 3 weeks

    This Python course provides a beginner-friendly introduction to Python for Data Science. Practice through lab exercises, and you'll be ready to create your first Python scripts on your own!

  4. 4–5 hours per week, for 1 weeks

    An opportunity to apply your foundational Python skills via a project, using various techniques to collect and work with data

  5. 2–3 hours per week, for 4 weeks

    This course teaches you the fundamental concepts of relational databases and Relational Database Management Systems (RDBMS) such as MySQL, PostgreSQL, and IBM Db2.

  6. 2–4 hours per week, for 4 weeks

    Learn how to use and apply the powerful language of SQL to better communicate and extract data from databases - a must for anyone working in the data science field.

  7. 4–5 hours per week, for 1 weeks

    In this short course you will learn additional SQL concepts such as views, stored procedures, transactions and joins.

  8. 3–4 hours per week, for 1 weeks

    This mini-course describes shell commands and how to use the advanced features of the Bash shell to automate complicated database tasks. For those not familiar with shell scripting, this course provides an overview of common Linux Shell Commands and shell scripting basics.

  9. 2–3 hours per week, for 8 weeks

    This course helps you develop the foundational skills required to perform the role of a Database Administrator (DBA) including designing, implementing, securing, maintaining, troubleshooting and automating databases such as MySQL, PostgreSQL and Db2.

  10. 2–4 hours per week, for 5 weeks

    This course provides you with practical skills to build and manage data pipelines and Extract, Transform, Load (ETL) processes using shell scripts, Airflow and Kafka.

  11. 2–3 hours per week, for 6 weeks

    This course introduces you to designing, implementing and populating a data warehouse and analyzing its data using SQL & Business Intelligence (BI) tools.

  12. 2–3 hours per week, for 5 weeks

    This course introduces you to the fundamentals of NoSQL, including the four key non-relational database categories. By the end of the course you will have hands-on skills for working with MongoDB, Cassandra and IBM Cloudant NoSQL databases.

  13. 2–3 hours per week, for 6 weeks

    This course provides foundational big data practitioner knowledge and analytical skills using popular big data tools, including Hadoop and Spark. Learn and practice your big data skills hands-on.

  14. 2–3 hours per week, for 3 weeks

    This short course introduces you to the fundamentals of Data Engineering and Machine Learning with Apache Spark, including Spark Structured Streaming, ETL for Machine Learning (ML) Pipelines, and Spark ML. By the end of the course, you will have hands-on experience applying Spark skills to ETL and ML workflows.

  15. 2–3 hours per week, for 6 weeks

    This Capstone Project is designed for you to apply and demonstrate your Data Engineering skills and knowledge in SQL, NoSQL, RDBMS, Bash, Python, ETL, Data Warehousing, BI tools and Big Data.

    • According to DICE Tech Job Report Data Engineer is the fastest growing occupation.
    • LinkedIn Emerging Job Report lists Data Engineer as one of the top 15 emerging jobs in the U.S. with a 33% annual growth rate.
    • Data Engineers command an average salary of $112,493 per year in the US, according to Glassdoor.

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$39/month USD

After a 7-day full access free trial. Cancel at any time.
This program subscription includes:
  • Immediate access to all 14 courses in this program
  • Course videos, lectures, and readings
  • Practice problems and assessments
  • Graded assignments and exams
  • edX learner support
  • Shareable verified certificates after successfully completing a course or program
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