What You’ll Learn: AI Data Infrastructure
You’ll go from understanding basic data workflows to designing and managing scalable, efficient data pipelines that power large-scale AI systems.
ETL Process Design
Build robust Extract, Transform, Load pipelines for various data sources.
Apache Spark
Process massive datasets efficiently using Spark for distributed computing.
Data Storage (Lake/Warehouse)
Choose and implement appropriate storage solutions for ML data.
Pipeline Monitoring & Quality
Implement checks and alerts to ensure data integrity and pipeline health.
Who Is This Course For?
Ideal for data engineers, analysts, and ML practitioners ready to specialize in the data infrastructure supporting AI systems.
- Data engineers looking to specialize in ML pipelines
- Data scientists wanting to understand the data layer
- Developers building data-intensive AI applications
Hands-On Projects
Sales Data ETL Pipeline
Build an ETL pipeline to ingest, clean, and aggregate large sales datasets.
Spark ML Data Preprocessor
Use Spark to process a large dataset and prepare it for machine learning.
Capstone
End-to-End ML Data Pipeline
Design and implement a complete pipeline from raw data to model-ready features.
4-Week Data Eng Syllabus
~48 hours total • Lifetime LMS access • 1:1 mentor support
Week 1: ETL Fundamentals
- Introduction to ETL concepts and tools
- Data ingestion from various sources (APIs, DBs, files)
- Basic data transformation with Python and Pandas
- Simple data quality checks and validation
Week 2: Spark & Distributed Processing
- Introduction to Apache Spark and PySpark
- Resilient Distributed Datasets (RDDs) and DataFrames
- Complex transformations and joins with Spark
- Optimizing Spark jobs for performance
Week 3: Data Storage & Warehousing
- Data Lake vs. Data Warehouse concepts
- File formats (Parquet, Delta Lake)
- Partitioning and indexing strategies
- Cloud storage options (S3, GCS, ADLS)
Week 4: Orchestration & Monitoring
- Pipeline orchestration with Airflow or Prefect
- Data lineage and metadata tracking
- Implementing data quality monitors
- Capstone project: Full ML data pipeline
NSTC‑Accredited Certificate
Share your verified credential on LinkedIn, resumes, and portfolios.
Frequently Asked Questions
No, prior experience with tools like Spark isn’t required. However, a solid understanding of Python, Pandas, and basic SQL is essential. Familiarity with cloud platforms (AWS, GCP, Azure) is beneficial.
Yes! You will work with large, real-world datasets using Apache Spark to perform distributed data processing and ETL operations.
