Data Alchemist Mentorship Program

This course transforms motivated learners into job-ready Data Engineers in 6 months. It covers fundamentals, pipelines, Big data, streaming and cloud, with hands-on projects every month.

Learners graduate with a portfolio of 4 projects showcasing real-world Data Engineering skills.

Data Engineering in 6 Months: Course Syllabus

Month 1 – Foundations of Data Engineering

Week 1: Intro & Python Basics – Role of DE, Python essentials, CSV/JSON, Open table formats.

Week 2: Python for Data Handling – Pandas, NumPy, APIs, Parquet

Week 3: SQL Mastery I – Joins, Subqueries, Window functions

Week 4: SQL Mastery II & Data Modeling – Schema design, NoSQL, Mini Project 1

Month 2 – Data Pipelines & Big Data

Week 5: Data Ingestion – APIs, files, cloud storage (S3)

Week 6: PySpark Basics I – RDDs, DataFrames, Actions

Week 7: PySpark Basics II – UDFs, Partitioning, Optimizations

Week 8: Workflow Orchestration – Airflow DAGs, Scheduling, Mini Project 2

Month 3 – Advanced Data Engineering

Week 9: Data Warehousing – OLTP vs OLAP, DWH tools, Query optimization

Week 10: Streaming with Kafka – Producers, Consumers

Week 11: Spark Structured Streaming – Real-time pipelines

Week 12: Data Lakes & Delta Lake – ACID, Time Travel, Mini Project 3

Month 4 – Cloud, DevOps & Capstone

Week 13: Cloud DE (AWS) – S3, EC2, EMR, Glue, Athena

Week 14: CI/CD & DataOps – Git, Testing, Monitoring

Week 15: Capstone Prep – Design end-to-end pipeline

Week 16: Capstone Project & Portfolio – Batch + Streaming pipeline, BI dashboard.

Month 5,6 - Interview preparation, Job assistance, Profile Optimisation

Final Deliverables

3 Mini Projects

1 Capstone Project

Resume-ready GitHub Portfolio

Hands-on experience with Python, SQL, Spark, Airflow, Kafka, AWS