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Professional Certificate of Competency of Data Engineering Foundations

Course Duration
Duration
  • 3 Months
Course Study
Study Mode
  • Online
  • Online Data Coms and IT
Course Location
Location
  • Online
Course Code
Course Code
CDE
Course Intakes
Intakes
  • 8 July 2025
Course Type
Course Type
  • Professional Certificate
  • Data Comms & Industrial IT
  • UK
Course Fees
Fees

Course Overview

The Professional Certificate of Competency in Data Engineering Foundations equips engineers and technicians with the essential skills needed to excel in data engineering. This comprehensive course covers key topics such as data architecture, data modeling, ETL processes, and big data technologies. Participants will gain practical knowledge in data collection, storage, processing, and analysis, preparing them to implement robust data solutions across various industries.

The course provides insights into the latest industry practices, ensuring that participants are well-prepared to tackle real-world data challenges. By the end of the course, attendees will be proficient in designing and managing efficient data workflows and infrastructures, making them valuable assets in the ever-evolving field of data engineering.

Course Benefits

You will achieve the following benefits: 

  • A complete understanding of data engineering concepts, processes and tools and their proper use
  • Cognizance of data engineering practices, activities, and methods
  • You may be eligible to claim CPD points through your local engineering association
  • Receive a Certificate of Completion from EIT
  • Learn from well-known faculty and industry experts from around the globe
  • Flexibility of attending anytime from anywhere, even when you are working full-time
  • Interact with industry experts during the webinars and get the latest updates/announcements on the subject
  • Experience global learning with students from various backgrounds and experience which is a great networking opportunity

Course Details

Key takeaways:

  • Deep understanding of data engineering concepts, including architecture, modeling, and ETL processes
  • Extensive experience with data collection, storage, processing, and analysis
  • Up-to-date knowledge of industry practices and standards
  • Proficiency in essential tools such as Python, PostgreSQL, and Apache Airflow
  • Ability to design and manage efficient data workflows and infrastructures

The course is composed of 12 modules. These modules cover a range of aspects to provide you with maximum practical coverage in Data Analytics and Automation in all streams of Engineering.

Module 1: Introduction to Data Engineering

  • The modern data eco-system and its components
  • Development of data engineering
  • Skillsets of a data engineer
  • Career opportunities and learning path in data engineering

Module 2: Data Engineering Ecosystem

  • Different types of file formats
  • Data repositories
    • RDBMS
    • NoSQL
    • Data marts, data warehouses and data lakes
  • Selection criteria of a data repository
    • Data pipelines, etl and elt
    • Data integration platform

Module 3: Lifecycle of Data Engineering

  • Architecture of the data platform
  • Selection and design of data stores
  • Data security and its Importance
  • Other aspects of data engineering
    • Data import and export
    • Data munging and its process
    • Query and data analyses
    • Performance fine-tuning and governance

Module 4: Python for Data Science – 1

  • Introduction to Python
  • Python data structures
    • Lists and tuples
    • Dictionaries
    • Sets
  • Fundamentals of Python Programming
    • Objects and classes
    • Functions
    • Conditions and branching
    • Loops

Module 5: Python for Data Science – 2

  • Data in Python
    • Loading, working and saving data with Pandas
  • Numpy Arrays and Simple APIs
    • Numpy 1D, 2D Arrays
    • Simple API’s and its setup

Module 6: Web Data Collection – Web Scraping & API

  • API
  • REST APIs & HTTP request
  • Web scraping
  • Working with other formats

Module 7: Relational Database Concepts

  • Data fundamentals, information, and data model
  • Erd’s, relationship types and entities mapping to table
  • Data types and database architecture
  • Clustered databases and usage patterns
  • Introduction to relational database offerings – Db2, Mysql, Postgresql

Module 8: Using Relational Databases

  • Categories Of SQL statements
  • Creating DROP, ALTER and truncate tables
  • Data movement and loading data
  • Database objects and hierarchy
  • Main keys and foreign keys
  • Summary of indexes and normalization
  • Model of relational constraints 

Module 9: SQL and PostgreSQL

  • Database and tables in SQL
  • Keys and constraints of SQL
  • Introduction to Postgresql
  • Databases and data loading in Postgresql

Module 10: Linux

  • Introduction to Linux – architecture, terminal
  • Linux commands
    • Shell
    • File and directory navigation and management
  • Scripting in shell

Module 11: Data Conversion – Raw to Analytics

  • Techniques to process data
    • ETL
    • ETL vs ELT
  • ETL and data pipelines
  • Building data pipelines – Apache airflow
  • Building streaming pipelines – Apache kafka

Module 12: Data Warehousing and BI Analytics

  • Data – warehouse, marts, lakes
  • Data warehouse – design, model and implementation
  • Data analytics
    • Business intelligence tools
    • Cognos analytics

To obtain a certificate of completion for EITā€™s Professional Certificate of Competency, students must achieve a 65% attendance rate at the live, online fortnightly webinars.Ā  Detailed summaries/notes can be submitted in lieu of attendance.Ā  In addition, students must obtain a mark of 60% in the set assignments which could take the form of written assignments and practical assignments. Students must also obtain a mark of 100% in quizzes.Ā  If a student does not achieve the required score, they will be given an opportunity to resubmit the assignment to obtain the required score.

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Payment Methods

Learn more aboutĀ payment methods, including payment terms & conditions and additional non-tuition fees.

Learn aboutĀ our instructors.

You are expected to spend approximately 5-8 hours per week learning the course content. This includes attending fortnightly webinars that run for about 90 minutes to facilitate class discussion and allow you to ask questions. This program has a 65% attendance requirement in the live webinars in order to graduate from the program.Ā  If you are unable to attend the live webinars, you have the option of watching the recording of completed webinars and sending a summary of what you have learnt from the webinar to the Learning Support officer.Ā  The summaries go towards your attendance requirement for the program.

This program is run online on an intensive part-time basis and has been designed to fit around full-time work. It will take three months to complete.

We understand that sometimes work commitments and personal circumstances can get in the way of your studies, so if at any point you feel that you are struggling with the pace of the course or finding a particular module challenging, you are encouraged to contact your designated Learning Support Officer for assistance.

Registrations are open for our upcoming intakes. Please ensure you book your place at least one week before the start date of the program.

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Helpful Information

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