Embark on a transformative journey into the cutting-edge world of data engineering with the LICQual Level 5 Diploma in Data and AI – Data Engineer, a globally recognized qualification designed to empower you as a skilled data engineer. This course is tailored for professionals and aspiring tech experts eager to master the art of designing, building, and maintaining data infrastructure that powers AI and business solutions.
Whether you’re transitioning into data engineering or aiming to elevate your technical expertise, this diploma offers a dynamic blend of advanced technical skills and practical applications to thrive in the data-driven era. Start your path to becoming a pivotal player in data and AI innovation today!
The LICQual Level 5 Diploma equips you with the expertise to develop robust data pipelines, optimize data systems, and leverage AI technologies across industries like technology, finance, healthcare, and more. You’ll learn to manage large-scale data architectures, ensure data quality, and support AI-driven analytics, preparing you for high-demand roles in data engineering.
Delivered through flexible, engaging modules, this course accommodates busy professionals with both online and in-person learning options, as emphasized in previous requests for accessible learning formats. By enrolling, you’re investing in a qualification that positions you at the forefront of the data and AI revolution.
The LICQual Level 5 Diploma in Data and AI – Data Engineer is a comprehensive program crafted to empower learners with advanced skills in designing, building, and managing data infrastructure to support business and AI applications. Ideal for those aiming to excel as data engineers, this qualification combines deep technical knowledge with practical, hands-on experience to prepare you for roles in data-intensive industries. Through real-world projects and case studies, you’ll gain the confidence to create scalable data systems and drive organizational success.
The curriculum covers critical topics such as data pipeline development, database management, cloud computing, and AI integration. You’ll master industry-standard tools like Python, SQL, Apache Spark, and cloud platforms (e.g., AWS, Azure), enabling you to build efficient data architectures and ensure seamless data flow.
Practical exercises focus on optimizing data systems, ensuring data quality, and supporting AI-driven insights, aligning with the need for technical expertise in modern data roles. This course fosters problem-solving, scalability, and collaboration skills, preparing you to meet the demands of complex data environments.
Designed for flexibility, the LICQual Level 5 Diploma offers accessible learning options, making it perfect for professionals balancing work and study. Upon completion, you’ll earn an internationally accredited qualification from LICQual, opening doors to roles such as data engineer, big data specialist, or AI infrastructure developer. This diploma not only enhances your technical proficiency but also positions you for career advancement in the rapidly growing data and AI sectors.
Course Overview
Qualification Title
LICQual Level 5 Diploma in Data and Al – Data Engineer
Total Units
6
Total Credits
60
GLH
360
Qualification #
LICQ2200567
Qualification Specification
To enroll in the LICQual Level 5 Diploma in Data and Al – Data Engineer, applicants must meet the following criteria:
|
Qualification# |
Unit Title |
Credits |
GLH |
|---|---|---|---|
|
LICQ2200567-1 |
Data Engineering Fundamentals |
10 |
60 |
|
LICQ2200567-2 |
Database Systems and Data Warehousing |
10 |
60 |
|
LICQ2200567-3 |
Data Pipelines and ETL Processes |
10 |
60 |
|
LICQ2200567-4 |
Cloud Platforms and Big Data Technologies |
10 |
60 |
|
LICQ2200567-5 |
Machine Learning for Data Engineers |
10 |
60 |
|
LICQ2200567-6 |
Capstone Project: Designing Scalable Data Solutions |
10 |
60 |
By the end of this course, learners will be able to:
Data Engineering Fundamentals
- Explain the core principles of data engineering, including data ingestion, storage, processing, and integration with AI systems.
- Analyze the role of a data engineer in designing and maintaining scalable data architectures for business and AI applications.
- Apply foundational data engineering techniques to ensure data quality, accessibility, and performance in organizational contexts.
- Evaluate the impact of data engineering practices on business efficiency and the success of data-driven initiatives.
Database Systems and Data Warehousing
- Design and implement relational and non-relational database systems to support efficient data storage and retrieval.
- Develop data warehousing solutions to enable large-scale data aggregation and analytics for business insights.
- Apply optimization techniques to enhance database performance and scalability in data-intensive environments.
- Assess the suitability of different database and warehousing solutions for specific business and AI use cases.
Data Pipelines and ETL Processes
- Design and build robust data pipelines to facilitate the extraction, transformation, and loading (ETL) of data from diverse sources.
- Implement ETL processes using industry-standard tools to ensure data consistency, accuracy, and availability.
- Optimize data pipelines for performance, scalability, and fault tolerance in real-world data workflows.
- Evaluate the effectiveness of ETL processes in supporting downstream analytics and AI applications.
Cloud Platforms and Big Data Technologies
- Utilize cloud platforms, such as AWS, Azure, or Google Cloud, to deploy and manage scalable data infrastructure.
- Apply big data technologies, such as Apache Hadoop, Spark, or Kafka, to process and analyze large-scale datasets.
- Configure cloud-based data solutions to ensure cost-efficiency, security, and performance in data engineering projects.
- Assess the advantages and limitations of cloud and big data technologies for specific organizational data needs.
Machine Learning for Data Engineers
- Explain the role of data engineering in supporting machine learning workflows, including data preparation and feature engineering.
- Implement data pipelines tailored for machine learning models, ensuring compatibility with AI frameworks like TensorFlow or PyTorch.
- Apply basic machine learning concepts to preprocess and transform data for predictive and analytical applications.
- Evaluate the impact of data engineering practices on the performance and accuracy of machine learning models.
Capstone Project: Designing Scalable Data Solutions
- Design a comprehensive, scalable data solution to address a real-world business or AI-driven challenge.
- Integrate data engineering tools, pipelines, and cloud technologies to create an end-to-end data architecture.
- Collaborate with stakeholders to ensure the solution meets business requirements and supports strategic objectives.
- Evaluate the performance, scalability, and impact of the capstone project, identifying areas for improvement and optimization.
This course is ideal for:
- Aspiring Data Engineers who want to build foundational and intermediate skills in data architecture, ETL, and big data technologies.
- Graduates in Computer Science, IT, or Engineering looking to specialize in data engineering and AI infrastructure.
- IT Professionals or Developers who wish to transition into data engineering roles within tech-driven organizations.
- Data Analysts seeking to upgrade their technical skills and move into data pipeline and infrastructure development.
- Business Intelligence (BI) Specialists who want to understand data engineering workflows and data automation.
- Cloud Computing Learners aiming to integrate big data and machine learning with AWS, Azure, or Google Cloud.
- Database Administrators looking to expand into modern data warehousing and data pipeline automation.
- Freelancers and Consultants involved in data solutions who want a formal qualification to validate their expertise.
- Entrepreneurs and Start-up Founders seeking to develop in-house data engineering capabilities to scale their businesses.
- Technical Project Managers overseeing AI, data science, or cloud migration projects who need in-depth technical understanding.
Assessment and Verification
All units within this qualification are subject to internal assessment by the approved centre and external verification by LICQual. The qualification follows a criterion-referenced assessment approach, ensuring that learners meet all specified learning outcomes.
To achieve a ‘Pass’ in any unit, learners must provide valid, sufficient, and authentic evidence demonstrating their attainment of all learning outcomes and compliance with the prescribed assessment criteria. The Assessor is responsible for evaluating the evidence and determining whether the learner has successfully met the required standards.
Assessors must maintain a clear and comprehensive audit trail, documenting the basis for their assessment decisions to ensure transparency, consistency, and compliance with quality assurance requirements.
