LuxDevHQ Curriculum
Explore our core learning tracks for Data Science and AI, Data Analytics, and Data Engineering.
Data Science and AI Curriculum
- • Python for data analysis and machine learning
- • Statistics, feature engineering, and model evaluation
- • Supervised and unsupervised learning workflows
- • Real-world projects and portfolio development
Data Analytics Curriculum
- • SQL fundamentals to advanced analytical queries
- • Excel and Power BI for business reporting
- • KPI design, dashboard storytelling, and stakeholder communication
- • Case studies for operations, finance, and growth analytics
LuxDevHQ Data Engineering Curriculum (4 Months / 16 Weeks)
This 4-month course equips learners with production-ready skills in Python, SQL, Azure, AWS, Docker, dbt, Great Expectations, Apache Airflow, Kafka, Spark, Grafana, and modern lakehouse workflows.
Program Design Principles
- • 16-week baseline delivered across 4 intensive months.
- • Monday to Thursday: theory, guided practice, and implementation.
- • Friday: job shadowing, peer project work, reviews, or technical demos.
- • Saturday: hands-on labs and project-based learning.
- • Portfolio-first delivery with production-style artifacts and capstone outputs.
Career Outcomes by Graduation
- • Junior Data Engineer
- • Analytics Engineer (entry level)
- • BI/Data Pipeline Developer
- • Data Operations Engineer (entry level)
- • Ability to design, deploy, test, and monitor end-to-end data systems.
Week-by-Week Curriculum (Weeks 1-16)
Expanded Tool Stack
- • Languages: Python, SQL, Bash
- • Databases and warehouses: PostgreSQL, Snowflake, Redshift, BigQuery, Azure Synapse
- • Cloud and storage: Azure Blob Storage, Azure Data Lake Storage, AWS S3, GCP Cloud Storage, MinIO
- • Orchestration: Apache Airflow, Astronomer, Dagster concepts
- • Streaming and processing: Apache Kafka, Redpanda, Spark, PySpark, Spark Structured Streaming, Apache Flink
- • Lakehouse and formats: Delta Lake, Apache Iceberg concepts, Parquet
- • Transformation and quality: dbt, Great Expectations, Pandera, SQLFluff
- • DevOps and observability: Docker, Docker Compose, GitHub Actions, Terraform basics, Grafana, Prometheus, Loki
- • Governance and lineage: OpenMetadata/DataHub basics, OpenLineage/Marquez basics, IAM/RBAC, secrets management
Delivery Rhythm
- • Monday to Thursday: instructor-led theory, demos, and implementation practice.
- • Friday: job shadowing with senior engineers, peer reviews, or project work.
- • Saturday: labs, mini projects, capstone milestones, and portfolio polishing.
- • Every month ends with a build review focused on reliability, documentation, and employability.
Assessment and Portfolio Framework
Weekly check-ins include labs, mini-quizzes, and code reviews with practical build assessments.
Portfolio artifacts expected by Week 16:
- SQL star schema and optimization case study
- Python ETL pipeline with validation and tests
- Dockerized data engineering application
- Airflow-orchestrated batch workflow
- Kafka streaming pipeline with schema strategy
- Spark/PySpark big-data processing project
- Delta Lake/lakehouse implementation
- dbt transformation project with tests, docs, and lineage
- Great Expectations data-quality reports
- Grafana monitoring stack with Prometheus/Loki alerts
- Final capstone repository with CI/CD, architecture diagrams, runbook, and live demo