Phase 1 — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review goals for the day
9:00 AM — Deep work (3 hours)
   - Month 1: SQL (joins, aggregations, window functions)
   - Month 2: Python (pandas, APIs, file handling)
   - Month 3: Data modeling (star schema, normalization)
   - Month 4: Git, Linux, and automation
12:00 PM — Lunch break
1:00 PM — Hands-on practice (2 hours)
2:00 PM — Deep work (2 hours)
5:00 PM — Review progress, plan tomorrow
6:00 PM — Exercise, rest
8:00 PM — Reading / video course (45 min)
10:00 PM — Wind down

Month 1 Review:
□ Can I write JOINs, GROUP BY, and subqueries?
□ Do I understand window functions?
□ Have I practiced on a real dataset?

Month 2 Review:
□ Can I clean data with pandas?
□ Can I call an API and parse JSON?
□ Have I automated a repetitive task?

Month 3 Review:
□ Do I understand star vs snowflake schema?
□ Can I design fact and dimension tables?
□ Have I modeled a small warehouse?

Month 4 Review:
□ Am I comfortable on the Linux CLI?
□ Can I use Git and push to GitHub?
□ Have I automated a scheduled task with cron?

# Simple ETL: extract → transform → load
import pandas as pd
import requests
from sqlalchemy import create_engine

# Extract
response = requests.get("https://api.example.com/orders")
data = pd.DataFrame(response.json()["results"])

# Transform
data = data.dropna(subset=["order_id"])
data["order_date"] = pd.to_datetime(data["order_date"])
data["revenue"] = data["quantity"] * data["unit_price"]

# Load
engine = create_engine("postgresql://user:pass@localhost:5432/warehouse")
data.to_sql("orders", engine, if_exists="append", index=False)
print(f"Loaded {len(data)} rows")

# Airflow DAG: schedule the ETL daily
from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime, timedelta

def run_etl():
    # same ETL logic as above
    ...

with DAG(
    dag_id="daily_orders_etl",
    start_date=datetime(2026, 1, 1),
    schedule_interval="0 6 * * *",     # 6 AM daily
    catchup=False,
    default_args={"retries": 2, "retry_delay": timedelta(minutes=5)},
) as dag:
    etl_task = PythonOperator(
        task_id="run_orders_etl",
        python_callable=run_etl,
    )

Phase 3 — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review goals for the day
9:00 AM — Portfolio / project work (3 hours)
   - Month 9: Project 1 (end-to-end pipeline)
   - Month 10: Project 2 + resume
   - Month 11: Applications + networking
   - Month 12: Interviews + follow-ups
12:00 PM — Lunch break
1:00 PM — Applications + LinkedIn (1 hour)
2:00 PM — SQL practice and interview prep (2 hours)
5:00 PM — Review progress, plan tomorrow
6:00 PM — Exercise, rest
8:00 PM — Mock interview practice (45 min)
10:00 PM — Wind down

Month 9 Review:
□ Is Project 1 complete?
□ Does it have a README and architecture diagram?
□ Is it on GitHub and deployed?
□ Can I explain the pipeline end-to-end?

Month 10 Review:
□ Is Project 2 complete?
□ Is my resume updated with project links?
□ Does my LinkedIn reflect my new skills?

Month 11 Review:
□ Have I sent 100+ tailored applications?
□ Am I networking on LinkedIn daily?
□ Have I received any interview calls?

Month 12 Review:
□ Have I done 5+ mock interviews?
□ Can I solve SQL problems confidently?
□ Have I accepted an offer?