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Data Science and Analytics Lecturer / Staff Student Public

Data Engineering from Beginner to Advanced

#Data #Engineering #Data Engineering
University of Technology and Entrepreneurship, Phnom Penh 0 / 30 enrolled
About this programme

Course outline.

The Data Engineering from Beginner to Advanced course is a comprehensive program designed to equip learners with the skills required to design, build, and manage modern data engineering systems. Participants will learn how to collect, store, process, and analyze large-scale data using industry-standard tools and technologies. The course covers data warehouses, data lakes, distributed systems, real-time data streaming, machine learning integration, and business intelligence reporting.

What you'll get

Programme highlights.

Industry-led teaching

Live materials from practitioners working in the field today.

Hands-on exercises

You'll apply what you learn through structured workshops and case studies.

Mentor access

Personal contact with the instructor for questions and feedback.

UTE Certificate

A signed certificate of completion you can add to your CV.

Course outline

What we'll cover.

  1. 01

    Introduction to Data Engineering

    • • Role of Data Engineering in Data-Driven Organizations • Data Engineering vs. Data Science • Overview of Data Engineering Architecture
  2. 02

    Data Storage and Databases

    • • Relational Databases (MySQL
    • PostgreSQL) • NoSQL Databases Overview • Data Modeling for Analytics
  3. 03

    Data Warehousing Concepts

    • • Data Warehouse Architecture • Star and Snowflake Schemas • ETL Processes
  4. 04

    Data Lakes and Big Data Storage

    • • Data Lake Concepts • Structured vs. Unstructured Data • Data Lake Tools and Formats
  5. 05

    Big Data Processing with Hadoop

    • • Hadoop Ecosystem Overview • HDFS and MapReduce • Introduction to YARN
  6. 06

    Distributed Data Processing with PySpark

    • • Spark Architecture • DataFrames and Spark SQL • Performance Optimization
  7. 07

    Real-Time Data Streaming with Kafka

    • • Introduction to Apache Kafka • Producers
    • Consumers
    • and Topics • Stream Processing Concepts
  8. 08

    Machine Learning in Data Engineering

    • • Preparing Data for Machine Learning • Integrating ML Pipelines • Batch vs. Real-Time ML
  9. 09

    Data Visualization and Reporting

    • • Preparing Data for BI Tools • Power BI Integration • Building Analytical Dashboards
  10. 10

    Capstone Project

    • • End-to-End Data Engineering Pipeline • Final Project Presentation