Training on Data Engineering for Data Science
Master data engineering for data science. Learn to build robust data pipelines, clean and transform data, and prepare it for analysis.
Next intake
20 Jul 2026 · Nakuru
Duration
10 days
Live instruction
Delivery
Physical + Virtual
Cohort based
Level
Intermediate
Working professionals
Certification
NITA reimbursable
For Kenyan cohorts
Language
English
All materials
About this programme
Data engineering has become the backbone of modern data-driven enterprises, especially in industries relying on data science and machine learning. In today's rapidly evolving digital landscape, the ability to design, build, and maintain scalable data architectures is critical. This course offers an excellent opportunity to master the skills needed to handle large data sets, automate data processing, and prepare data pipelines for efficient analysis.
Whether you are a data scientist, software engineer, or business analyst, understanding how to construct robust data pipelines and integrate them with data science workflows will give you a competitive edge in your career. You will learn to work with leading technologies in the industry such as SQL, Python, Apache Spark, and cloud-based solutions, thus empowering you to build a solid foundation for data analysis and machine learning applications.
Participants will also explore the integration of data engineering practices with data science, enabling them to provide the necessary data infrastructure for data scientists to conduct meaningful analysis. By the end of the course, participants will be adept at transforming raw data into actionable insights, enhancing their organization's data-driven decision-making process.
Duration
10 Days
Who Should Attend
- Data Engineers who want to improve their data management and pipeline development skills.
- Data Scientists seeking to deepen their understanding of data engineering to enhance collaboration.
- IT Professionals interested in transitioning into data engineering roles.
- Business Analysts and BI Professionals who want to learn more about data pipeline design and implementation.
- Software Engineers looking to expand their skill set into data science infrastructure.
What you'll walk away with
By the end of this course, participants will be able to:
- Understand the role of data engineering in the data science lifecycle.
- Develop, test, and deploy scalable data pipelines for large datasets.
- Implement ETL processes to clean, transform, and integrate data from multiple sources.
- Leverage cloud technologies and distributed computing frameworks (e.g., Hadoop, Spark) for data processing.
- Optimize database performance for data science applications.
- Collaborate effectively with data scientists and analysts to deliver high-quality data for insights.
- Apply best practices in data governance, security, and compliance.
What we cover, module by module
Module 1: Introduction to Data Engineering
- Role of data engineering in data-driven organizations
- Key components of data engineering architectures
- Understanding data pipelines and workflows
- Overview of structured, semi-structured, and unstructured data
- Introduction to modern data engineering tools and technologies
- Case Study: Building a scalable data infrastructure for business analytics
- Practical Activity: Mapping a simple organizational data pipeline
Module 2: Data Pipeline Design and Implementation
- Principles of scalable and reliable pipeline design
- Batch processing vs. stream processing
- Data ingestion techniques and APIs
- Data workflow orchestration concepts
- Pipeline monitoring and troubleshooting techniques
- Case Study: Implementing automated pipelines for operational reporting
- Practical Exercise: Designing a basic batch and streaming pipeline workflow
Module 3: ETL Processes and Data Transformation
- Fundamentals of Extract, Transform, Load (ETL)
- Data cleaning and validation techniques
- Data transformation and enrichment processes
- ETL automation tools and best practices
- Handling missing, inconsistent, and duplicate data
- Case Study: Improving reporting accuracy through ETL optimization
- Practical Activity: Creating a simple ETL workflow using sample datasets
Module 4: Data Storage and Database Management
- Relational databases (SQL) and NoSQL databases
- Data warehouse and data lake concepts
- Database design and indexing strategies
- Query optimization and performance tuning
- Backup and recovery strategies for databases
- Case Study: Transitioning from traditional databases to data lake architecture
- Practical Demo: Querying and managing datasets in SQL and NoSQL systems
Module 5: Distributed Computing and Big Data Technologies
- Introduction to distributed computing concepts
- Hadoop ecosystem and Spark fundamentals
- Parallel data processing and cluster computing
- Handling large-scale datasets efficiently
- Real-time analytics frameworks and tools
- Case Study: Big data processing for customer behavior analysis
- Practical Exercise: Running distributed data processing tasks using Spark concepts
Module 6: Cloud Data Engineering Platforms
- Overview of AWS, Microsoft Azure, and Google Cloud Platform
- Cloud-based storage and processing services
- Building cloud-native data pipelines
- Scalability and cost optimization in the cloud
- Managing cloud-based analytics environments
- Case Study: Migrating enterprise data operations to the cloud
- Practical Activity: Designing a cloud-based data engineering workflow
Module 7: Data Governance, Security, and Compliance
- Principles of data governance and stewardship
- Data privacy regulations and compliance requirements
- Data security and access control measures
- Ethical considerations in data management
- Risk management and audit readiness
- Case Study: Managing secure and compliant enterprise data systems
- Practical Exercise: Developing a data governance framework for an organization
Module 8: Workflow Automation and Real-Time Analytics
- Workflow automation and orchestration tools
- Data pipeline scheduling and monitoring
- Real-time analytics and event-driven processing
- Logging, monitoring, and alert systems
- Data versioning and reproducibility practices
- Case Study: Real-time analytics implementation for operational intelligence
- Practical Demo: Designing an automated workflow for continuous data processing
Module 9: Collaboration and Advanced Data Engineering Practices
- Collaboration between data engineers, analysts, and data scientists
- Preparing high-quality data for machine learning models
- Best practices for scalable data systems
- Agile methodologies in data engineering projects
- Emerging trends in AI-driven data engineering
- Case Study: Supporting machine learning initiatives through effective data engineering
- Practical Activity: Developing a collaborative data workflow strategy
Module 10: Capstone Project and Industry Applications
- Solving real-world data engineering challenges
- Designing production-ready data pipelines
- Integrating storage, processing, governance, and analytics
- Industry best practices and career pathways in data engineering
- Case Study: Enterprise-scale data engineering transformation project
- Final Project: Building and presenting a complete end-to-end data engineering solution
Where the change lands
Organizational Impact
-
Improve data reliability and quality for better insights and decision-making.
-
Increase operational efficiency by automating data pipelines.
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Build scalable data infrastructure to support big data and real-time analytics.
-
Ensure consistency and reduce risks through standardized data engineering practices.
Personal Impact
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Gain a highly sought-after skill bridging data science and IT operations.
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Advance into senior data science, data engineering, or BI roles.
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Contribute to organizational innovation and profitability through efficient data systems.
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Lead and champion data infrastructure projects with confidence.
Dates and locations
Upcoming intakes
Every intake is limited to a small cohort. Booking closes when a date fills or three weeks before the start, whichever comes first.
| City | Starts | Ends | Delivery | Book |
|---|---|---|---|---|
NakuruNext | 20 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Kigali | 20 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Accra | 20 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Kisumu | 27 Jul 2026 | 07 Aug 2026 | In-Person | Book |
Johannesburg | 27 Jul 2026 | 07 Aug 2026 | In-Person | Book |
Dakar | 27 Jul 2026 | 07 Aug 2026 | In-Person | Book |
- NakuruNext
20 Jul → 31 Jul·In-Person
Book this intake - Kigali
20 Jul → 31 Jul·In-Person
Book this intake - Accra
20 Jul → 31 Jul·In-Person
Book this intake - Kisumu
27 Jul → 07 Aug·In-Person
Book this intake - Johannesburg
27 Jul → 07 Aug·In-Person
Book this intake - Dakar
27 Jul → 07 Aug·In-Person
Book this intake
Common questions.
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For corporate teams
Training 10+ professionals?
We deliver Training on Data Engineering for Data Science in-house at your offices, at a venue we arrange, or fully virtual. Customise the curriculum against your KPIs, and get a bespoke price for the cohort size you need.