-- A course with job-ready SQL depth teaches queries like this
WITH monthly AS (
  SELECT customer_id,
         DATE_TRUNC('month', order_date) AS month,
         SUM(amount) AS revenue
  FROM orders
  GROUP BY customer_id, DATE_TRUNC('month', order_date)
)
SELECT customer_id, month, revenue,
       RANK() OVER (PARTITION BY month ORDER BY revenue DESC) AS rank_in_month
FROM monthly;
-- (PostgreSQL syntax)

Excel skills to confirm in the syllabus
☐ XLOOKUP / INDEX-MATCH
☐ IF, IFS, COUNTIFS, SUMIFS
☐ Pivot tables and pivot charts
☐ Power Query: merge, unpivot, clean text
☐ Data validation and conditional formatting
☐ Dashboard sheet with slicers

Case Study: Retail Sales Review
Question: Why did revenue fall last quarter in two regions?
Data: orders, customers, products (CSV, with some messy values)
Tasks:
1. Clean dates, duplicates and missing regions
2. Compare revenue by region, month and category
3. Check if the drop is price, volume or mix
4. Build a one-page dashboard
5. Write 3 recommendations in plain English
Review: trainer feedback on logic and story

import pandas as pd

df = pd.read_csv("orders.csv")
df = df.drop_duplicates(subset="order_id")
df["order_date"] = pd.to_datetime(df["order_date"], errors="coerce")
df["region"] = df["region"].fillna("Unknown").str.title()
summary = (df.groupby([df["order_date"].dt.to_period("M"), "region"])["amount"]
             .sum().unstack())
print(summary.round(0))

Questions for the demo class
1. How many hours are spent on SQL joins and window functions?
2. Which BI tool do you teach, and do you cover data modelling?
3. How many case studies will I complete?
4. Are datasets messy and realistic?
5. Who reviews my analysis and how?
6. What career support is included, in writing?

By the end of a good course you should have:
☐ 1 Excel dashboard with Power Query cleaning
☐ 1 SQL case study with a written analysis
☐ 1 Power BI or Tableau dashboard with a data model
☐ 1 Python notebook cleaning a messy dataset
☐ A GitHub or portfolio page linking all four

Data Analytics Course Scorecard (1-5)
                     Course A   Course B   Course C
Excel depth            ___        ___        ___
SQL depth              ___        ___        ___
BI tool + modelling    ___        ___        ___
Python / pandas        ___        ___        ___
Case studies           ___        ___        ___
Feedback on insights   ___        ___        ___
Batch and support      ___        ___        ___
TOTAL                  ___        ___        ___

Insight write-up template
Question: ______________________
Data used: _____________________
What I found (with numbers from the data):
1.
2.
Why it matters to the business:
______________________
Recommendation:
______________________
Limitations of this analysis:
______________________

Weekly routine during the course
Daily: 30-45 min SQL practice
After each class: redo the exercise on a new dataset
Weekend: work on the current case study
Sunday: update portfolio and write one insight summary