Course Summary

This Data Engineer course equips learners with the necessary skills to design, build, and maintain scalable data pipelines and architectures. Students will explore core data engineering concepts, including data modeling, ETL processes, data warehousing, big data technologies, and cloud-based solutions.

With a strong focus on hands-on practice, learners will work on real-world datasets and implement industry-relevant solutions. The course prepares participants for careers in data engineering, making them proficient in handling data infrastructure and ensuring efficient data workflows.

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Data Modeling & Database Design:
Learn to design efficient database systems, focusing on relational and non-relational databases, normalization, and schema design.
ETL (Extract, Transform, Load) Pipelines:
Master the development of ETL pipelines to clean, process, and transform raw data into structured formats.
Data Warehousing:
Understand the architecture of data warehouses and learn how to implement data lakes and data marts for effective storage and retrieval.
Big Data Technologies:
Gain proficiency in big data platforms such as Hadoop, Spark, and distributed computing for managing large-scale datasets.
Cloud Platforms & Infrastructure:
Explore cloud services like AWS, Google Cloud, and Azure to manage data pipelines, deploy solutions, and scale data storage.
Data Governance & Security:
Develop knowledge of data governance frameworks, ensuring data integrity, compliance, and security in data systems.

Quizzes & Assignments: Regular quizzes and assignments to test theoretical knowledge and practical understanding of data engineering concepts.
Capstone Project: A comprehensive, real-world project requiring learners to design and implement an end-to-end data pipeline solution.
Case Studies: Analyze real industry use cases and propose solutions based on the skills learned throughout the course.
Peer Reviews & Code Reviews: Collaborative reviews to provide feedback on code quality, design decisions, and problem-solving techniques.

By the end of the course, participants will be able to:

Design and Implement Data Pipelines: Build scalable ETL pipelines, transforming raw data into usable formats for analysis.
Master Database Technologies: Work with SQL and NoSQL databases, designing efficient schema and optimizing queries.
Develop Data Warehousing Solutions: Create and manage data warehouses for large-scale data storage and analytics.
Leverage Big Data Tools: Use big data technologies like Hadoop and Spark to process large datasets efficiently.
Deploy Solutions in the Cloud: Utilize cloud infrastructure to manage, scale, and monitor data workflows on platforms like AWS or Google Cloud.
Ensure Data Governance & Security: Implement data security best practices, ensure compliance, and handle sensitive data securely.

Regular & Intensive Learning Option

Class Schedule:
Morning Students – Monday to Thursday, 09:00 am – 03:00 pm
Afternoon Students – Monday to Thursday, 03:30 pm – 08:00 pm
Teaching Method: Online & Offline / Online only (eg. Google meet video call)
Course Duration: 6 sessions (18 months)
Session Length: 1 session = 3 months
DWTA Qualification: Academic Diploma
CTVET’s National TVET Qualification Framework (NTVETQF): Higher National Diploma
Tuition & Guidance Fees:
- Ghc 2600 per session or
- Ghc 900 per month

Slow-Paced & Relaxed Learning Option

Class Schedule:
Weekend Evening Students – Friday to Sunday, 06:00 pm – 08:00 pm
Teaching Method: Online & Offline / Online only (eg. Google meet video call)
Course Duration: 7 sessions (21 months)
Session Length: 1 session = 3 months
DWTA Qualification: Academic Diploma
CTVET’s National TVET Qualification Framework (NTVETQF): Higher National Diploma
Tuition & Guidance Fees:
- Ghc 2600 per session or
- Ghc 900 per month

Fill Application Form

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Data Engineer

Course Summary

Regular & Intensive Learning Option

Slow-Paced & Relaxed Learning Option

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