Self-assess at the end of each week. Bring unchecked items to your next consultation session.

Day 0 | Setup

• WSL2 (Windows) or Terminal (macOS) is working
• Docker Desktop is running and docker compose version works
• PostgreSQL container starts and I can connect with psql
• Python 3.11+ installed and pip works
• dbt --version shows 1.8+
• Airflow UI accessible at localhost:8080
• PySpark SparkSession creates without errors
• VS Code with all required extensions installed

Week 1 | Database Design

• I can explain the difference between OLTP and OLAP
• I can design a normalized ERD from a business scenario
• I can design a star schema with a fact table and dimension tables
• I understand when to use star schema vs snowflake schema
• I completed Project 1 and presented my ERD and star schema to instructors
• I submitted my design justification document

Week 2 | Core Tools

• I can write a bash script that downloads a file and handles errors
• I can load a CSV into PostgreSQL using PySpark
• I can clean a DataFrame with Pandas (nulls, deduplication, type casting)
• I can write window functions in SQL (RANK, LAG, LEAD)
• I completed Project 2 and demonstrated the end-to-end pipeline

Weeks 3–5 | Data Pipelines

• I understand the difference between ETL and ELT
• I can create a dbt project with staging, intermediate, and mart layers
• I use source() for raw tables and ref() for dbt models — never hardcoded table names
• I have unique and not_null tests on all primary keys
• I can build an Airflow DAG that runs a multi-step pipeline
• I completed Project 3 — dbt run and dbt test both pass with zero errors
• I completed Project 3 — Airflow DAG runs end to end with all tasks green

Weeks 6–7 | Power BI

• I can connect Power BI Desktop to a PostgreSQL database
• I can build a star schema data model in Power BI Model view
• I have written at least 5 DAX measures in a dedicated Measures table
• My dashboard answers at least 3 specific business questions
• I have written a data story that includes a recommended action
• I completed Project 4 and presented the dashboard to instructors

Weeks 8–12 | Capstone

• I have read the full capstone brief on Day 1 of Week 8
• I have an architecture diagram showing the full data flow
• Python classes and modular functions are used for extraction and transformation
• PySpark processes the data and writes to PostgreSQL
• dbt project has staging/intermediate/mart layers with passing tests
• Airflow DAG orchestrates the full pipeline on a schedule
• Power BI dashboard is connected to the mart tables
• I have submitted my capstone and delivered the final presentation