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Advanced Professional Diploma in Data Engineering

 

Department of Information Technology — Data Engineering and Software Systems

 

1. Program Overview:

An advanced and comprehensive professional program extending over 6 months (160 hours of approved study load), aimed at preparing and qualifying professional data engineers capable of designing, building, and managing large-scale data processing and flow pipelines (Data Pipelines & Big Data Systems). The program covers Python for data engineering, relational and non-relational databases (SQL & NoSQL), Extract, Transform, and Load processes (ETL), Data Warehousing, distributed processing using Apache Spark, cloud computing, and data governance and security.

2. Key Objectives:

  • Mastering advanced programming and databases: using Python in data engineering, and designing and modeling relational (SQL) and non-relational (NoSQL) databases.
  • Building and managing data pipelines (Data Pipelines): mastering Extract, Transform, and Load processes (ETL/ELT) and developing automated and reliable processing pipelines.
  • Working with Big Data and Data Warehouses: mastering Data Warehousing architecture, Big Data processing, and distributed analytics using Spark.
  • Managing, governing, and securing cloud data: applying data quality and governance standards, and managing and securing data flows in cloud environments (Cloud Data Engineering).

3. Target Audience:

  • Software developers and data analysts wishing to transition to a Data Engineer career path.
  • Database Administrators (DBAs) seeking to master Big Data technologies and data pipeline engineering.
  • Students and graduates of Information Technology, Computer Science, and Software Engineering programs.
  • Specialists and engineers working on Business Intelligence, Machine Learning, and data infrastructure projects.

4. Study Plan Modules (15 Interactive and Applied Modules):

1. Introduction to Data Engineering.

2. Python for Data Engineering.

3. Data Structures & Algorithms.

4. Relational Databases and SQL.

5. Database Design and Modeling.

6. Non-Relational Databases (NoSQL).

7. Extract, Transform, and Load (ETL/ELT).

8. Building and Managing Data Pipelines.

9. Data Warehousing.

10. Big Data Processing.

11. Distributed Data Processing Using Spark.

12. Data Quality & Governance.

13. Cloud Data Platforms.

14. Data Security & Management.

15. Final Data Engineering Project.

5. Program Data and Organizational Assessment:

  • Field and subfield: Information Technology / Data Engineering and Software Systems.
  • Duration and sessions: 6 months (evening period) | 50 lectures (two lectures per week, two hours per lecture = 100 direct training hours).
  • Distribution of credit hours (160 total hours):

100 hours: interactive live lectures (remote / online).

40 hours: required readings and open resources.

15 hours: chapter questions and self-assessment.

5 hours: final review and completion examination.

  • Attendance and assessment requirements: an attendance rate of no less than 75% of the interactive lectures, in addition to assignments and practical projects.
  • Final project: an online assessment test (25 multiple-choice questions + 5 practical case questions), in addition to building and developing an integrated data pipeline and Big Data processing system as an applied graduation project.

 

 

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