SQL first or Python first? Decide from the work you want

Choose by the first six months of the job

The "Unsure" row isn't a trick. SQL's small scope means that within about three weeks you can answer real questions about real-looking data, and that early success helps you keep going.

The same question in both

Here is one question: "For each city and ticket category with at least two tickets, what is the average time to resolve, slowest first?" It's answered below in SQL and in pandas on the same synthetic data. The script runs as written, and its final check confirms the two answers match.

import sqlite3
import pandas as pd

rows = [
    ("T1", "Noida",   "billing",  38),
    ("T2", "Noida",   "delivery", 95),
    ("T3", "Delhi",   "billing",  22),
    ("T4", "Delhi",   "delivery", 61),
    ("T5", "Delhi",   "delivery", 74),
    ("T6", "Noida",   "billing",  45),
]
cols = ["ticket_id", "city", "category", "minutes_to_resolve"]

# SQL version
con = sqlite3.connect(":memory:")
con.execute("CREATE TABLE tickets (ticket_id TEXT, city TEXT, category TEXT, minutes_to_resolve INTEGER)")
con.executemany("INSERT INTO tickets VALUES (?, ?, ?, ?)", rows)
sql_result = con.execute("""
SELECT city, category, COUNT(*) AS tickets, AVG(minutes_to_resolve) AS avg_minutes
FROM tickets
GROUP BY city, category
HAVING COUNT(*) >= 2
ORDER BY avg_minutes DESC;
""").fetchall()
print(sql_result)

# pandas version of the same question
df = pd.DataFrame(rows, columns=cols)
pd_result = (
    df.groupby(["city", "category"])
      .agg(tickets=("ticket_id", "count"), avg_minutes=("minutes_to_resolve", "mean"))
      .query("tickets >= 2")
      .sort_values("avg_minutes", ascending=False)
      .reset_index()
)
print(pd_result)

assert [tuple(r) for r in pd_result.itertuples(index=False)] == sql_result

Read both and notice your reaction. The SQL reads almost like a sentence: select, from, group, keep, sort. The pandas version chains method calls, and to write it you need to know about DataFrames, named aggregation and method chaining. If the SQL made sense straight away and the pandas felt like a wall, that's a good reason to start with SQL. If the pandas chain felt natural, perhaps because you've coded before, starting with Python costs you little.

What each first choice delays

Every order leaves something for later. Know what you're putting off.

SQL first delays: automation (scheduling a report, calling an API), statistics and modelling beyond simple aggregates, and working with files that aren't in a database. For an analyst, that's usually fine in the first two months.

Python first delays: thinking in sets, meaning joins, grain and deduplication. Python-first learners sometimes write slow loops over rows for jobs a single GROUP BY would do, and struggle in interviews that test SQL directly. If you start with Python, plan to begin SQL no later than week eight.

A 12-week order for each path

Both paths finish at the same place, a project that uses both languages. The only difference is which one you're more fluent in by then.

A test if you still cannot decide

Spend one evening on each. On the first, do the first four SQLBolt lessons. On the second, do the first two sections of the pandas introduction. The next morning, without looking, write down one query and one pandas line you remember. Start with whichever one you could reproduce. Having something you can recall after one evening is a better guide than anyone's opinion, this one included.

Free places to start either one

For SQL, SQLBolt has short interactive lessons, and the SQLite language reference is the authoritative guide to the syntax used above. For Python, the official tutorial followed by 10 minutes to pandas covers the Python-first weeks one to six.

The Data Analyst programme page lists the module order it uses, so you can compare it with the table above. If data science is the likelier target, data analytics vs data science will help you settle it first.