IBM Data Engineering Professional Certificate review
Data Engineering introduced with plenty of breadth but requires discipline to complete
Course Details (Dec 2024)
Cost: Available via Coursera Plus subscription ($600 AUD annually)
Duration: ~6 months part time
No. of Subjects: 16
As a Data Analyst looking to transition into Data Engineering, I had been searching for some form of introductory education in the field for some time. Other options included Youtube, Datacamp & Udemy. I decided to proceed with IBM’s offfering on Coursera because it seemed vendor neutral (without favouring AWS/GCP/Azure or Snowflake/Databricks), had the most number of reviews (>50K) and the course structure of 16 subjects looked comprehensive but not intimidating since I already have several years experience working as a Data Analyst as well as a Masters degree in Data Analytics. In short this course satisfies the purpose of a certificate - getting an introduction in Data Engineering with plenty of breadth without going into too much detail in any one topic.
Who the course is for
I would recommend someone who is also in a similar situation as myself, where there is a genuine interest to learn Data Engineering who is also commited to finishing all 16 subjects. If you are not able to commit to the entire certificate in 6-12 months, then enrolling in individual subjects and completing them slowly would be a better option but this will mean a higher cost since access to the course & subjects is subscription based.
Course structure
The 16 subjects and corresponding tools used are:
Introduction to Data Engineering
Python for Data Science, AI & Development || Python
Python Project for Data Engineering || Python
Introduction to Relational Databases (RDBMS) || MySQL, PostgreSQL, IBM DB2
Databases and SQL for Data Science with Python || Python
Hands-on Introduction to Linux Commands and Shell Scripting || Shell
Relational Database Administration (DBA) || IBM DB2, MySQL, PostgreSQL
ETL and Data Pipelines || Shell, Apache Airflow, Apache Kafka
Data Warehouse Fundamentals || IBM DB2, PostgreSQL
BI Dashboards || IBM Cognos, Google Looker
Introduction to NoSQL Databases || MongoDB, Apache Cassandra
Introduction to Big Data with Spark and Hadoop || Apache Hadoop, Apache Spark
Machine Learning with Apache Spark || Apache Spark
Data Engineering Capstone Project || All of the above
Generative AI: Elevate your Data Engineering Career || IBM Watson
Data Engineering Career Guide and Interview Preparation
Although comprehensive, it does mean the time commitment to complete the certificate is longer.
Course Content
All subjects consist of pre-recorded, voiceover videos on theoretical concepts & several multiple choice questions all of which have a pass mark ranging from 60-80%. I found the the voice over to be monotone which doesnt help with active listening but the content and visuals presented succinctly covers the basics with some detail. The multiple choice questions are also not that difficult but does require full attention in order to pass.
Most subjects have hand-on practical labs where executable code from each of the tools listed above are provided. To pass the labs, the same code can be referenced with some slight amendments. This describes the level of complexity and critical thinking needed to complete this certificate (assuming the learner has basic experience with SQL and Python already). It doesnt inspire much creativity or depth but does give enough exposure to introduce the tool.
Final thoughts
This certificate provides a broad introduction to Data Engineering without going into too much depth. It’s like visiting an ice cream store & only tasting all the flavours without getting any scoops. You will get a sense of all ‘flavours’ of Data Engineering but you wont be fully experience any one of them.
Data Analysts wont have much issues but the most challenging part is the time needed to complete all 16 units. The course is also very greenfield and may not reflect the full reality of what its like working as a Data Engineer.
What I liked
Broad topics covered, not exactly vendor driven, plenty of tools used in industry
What I didnt like
Not enough depth in specific topics, grind to finish all 16 subjects.
3 / 5