What Does a Data Analyst Do on a Normal Work Day?

-- Did yesterday's orders load? Compare with the previous 7 days
SELECT
  CAST(order_date AS DATE) AS day,
  COUNT(*)                 AS orders,
  SUM(order_amount)        AS revenue
FROM orders
WHERE order_date >= CURRENT_DATE - INTERVAL '8 days'
  AND order_date <  CURRENT_DATE
GROUP BY CAST(order_date AS DATE)
ORDER BY day;

-- If yesterday shows far fewer rows than other days,
-- check the pipeline before trusting any dashboard.

-- How many of yesterday's rows have missing key fields?
SELECT
  COUNT(*)                                          AS total_rows,
  SUM(CASE WHEN customer_id IS NULL THEN 1 ELSE 0 END) AS missing_customer,
  SUM(CASE WHEN region IS NULL THEN 1 ELSE 0 END)      AS missing_region
FROM orders
WHERE order_date >= CURRENT_DATE - INTERVAL '1 day'
  AND order_date <  CURRENT_DATE;

-- Return rate by category for last month (PostgreSQL)
SELECT
  p.category,
  COUNT(DISTINCT o.order_id)  AS orders,
  COUNT(DISTINCT r.order_id)  AS returned_orders,
  ROUND(100.0 * COUNT(DISTINCT r.order_id)
        / NULLIF(COUNT(DISTINCT o.order_id), 0), 2) AS return_rate_pct
FROM orders o
JOIN products p      ON p.product_id = o.product_id
LEFT JOIN returns r  ON r.order_id   = o.order_id
WHERE o.order_date >= DATE_TRUNC('month', CURRENT_DATE) - INTERVAL '1 month'
  AND o.order_date <  DATE_TRUNC('month', CURRENT_DATE)
GROUP BY p.category
ORDER BY return_rate_pct DESC;

import pandas as pd

df = pd.read_csv("returns_export.csv")

# Standardise text columns
df["region"] = df["region"].str.strip().str.title()

# Parse dates; unparseable values become NaT so you can inspect them
df["return_date"] = pd.to_datetime(df["return_date"], errors="coerce", dayfirst=True)
print("Bad dates:", df["return_date"].isna().sum())

# Remove exact duplicate rows and record how many
before = len(df)
df = df.drop_duplicates()
print("Duplicates removed:", before - len(df))

# Returns by region and reason
summary = (df.groupby(["region", "reason"]).size()
             .reset_index(name="returns")
             .sort_values("returns", ascending=False))
print(summary.head(10))

SUBJECT: Returns last month — main driver found

Hi team,

Key points:
1. Return rate rose compared with the month before.
2. Most of the increase came from the Apparel category.
3. 'Size does not fit' is the top reason in that category.

Suggested next step: review the size chart on the top
returned Apparel products.

Method: orders vs returned orders, last calendar month,
cancelled orders excluded. Query saved in shared folder.

Happy to walk through it in tomorrow's stand-up.

import matplotlib.pyplot as plt

# rates computed earlier, e.g. from the SQL result
categories = ["Apparel", "Footwear", "Electronics", "Home"]
rates      = [8.4, 5.1, 2.3, 1.9]   # example values for illustration

fig, ax = plt.subplots(figsize=(6, 3.5))
ax.barh(categories, rates)
ax.invert_yaxis()                 # largest on top
ax.set_xlabel("Return rate (%)")
ax.set_title("Return rate by category, last month")
for i, v in enumerate(rates):
    ax.text(v + 0.1, i, f"{v}%", va="center")
plt.tight_layout()
plt.savefig("return_rate_by_category.png", dpi=150)

DATA ANALYST DAILY LOG

Date: __________

MORNING CHECKS
[ ] Dashboards refreshed (time: ______)
[ ] Row counts normal for key tables
[ ] Anomalies spotted: _____________________

TODAY'S MAIN TASKS
1. ______________________  Requested by: ________
2. ______________________  Requested by: ________

QUERIES WRITTEN / SAVED
- __________________.sql   Purpose: ____________

LEARNED TODAY
- ________________________________________

ANALYSIS REQUEST INTAKE

Requested by   : ______________  Team: __________
Question       : ___________________________________
Decision it supports: ______________________________
Metric definition   : ______________________________
Time period    : From ________ To ________
Filters        : Region / product / channel: ________
Output needed  : [ ] Number  [ ] Table  [ ] Chart  [ ] Dashboard
Deadline       : __________  Priority: High / Medium / Low
Data source(s) : ___________________________________

END-OF-DAY HANDOVER NOTE

Done today:
- Returns analysis shared with operations (email sent)
- Fixed region filter on sales dashboard

In progress:
- Customer churn query — joins tested, numbers not yet
  reconciled with finance report

Blocked:
- Waiting for access to marketing spend table

Tomorrow first:
- Reconcile churn totals, then share draft