How much of a data analyst's day is spent writing SQL?

Most of it. On a typical day, 3 to 5 hours go into writing, testing, and fixing SQL queries. That's the bulk of the work, not a side skill. The queries aren't glamorous—you're pulling a table, checking if the numbers match what someone expects, joining it to another table, and formatting it so a non-technical person can read it.

Do data analysts use Python every day?

Not at the entry level, and often not later either. SQL and a spreadsheet tool cover most entry-level and mid-level analyst work. Python shows up more in data engineering, machine learning, or advanced analytics roles, not in the daily grind of a standard analyst seat.

What meetings does a data analyst attend?

A typical week includes a team standup, one or two stakeholder meetings where you present or explain a number, and the occasional call where someone wants to understand a dashboard they can't read on their own. The skill that matters most in these meetings is explaining a number in one sentence to someone who doesn't care how you got it.

Is being a data analyst boring?

Some of it, yes. Most days include stretches of repetitive query writing and data cleanup that nobody would call exciting. The interesting part shows up when a chart finally answers a question someone's been asking for weeks, or when you catch an error before it reaches a director's inbox.

What's the difference between a junior and senior analyst's day?

A junior analyst spends more time on execution: writing the queries, building the dashboard, fixing the data someone else flagged. A senior analyst spends more time deciding what should get built in the first place and pushing back when a request doesn't make sense. Both still write SQL most days. The senior version just spends less time on syntax and more time on judgment.

What tools does a data analyst use daily?

In order of frequency: SQL editor, a BI tool like Tableau or Looker, spreadsheets like Excel or Google Sheets, Slack or email, and Python (occasionally). SQL editor is used most, while Python ranks lower than many expect, especially at the entry level.

SQL for Data Analysts: The Complete Guide

SQL is the skill to learn first. It shows up on more analyst job postings than any BI tool, it is tested in most interviews, and it is what you will use daily once you are hired. This guide puts all 18 SQL posts in one place, in an order that makes sense for learning them.

My first analyst role ran on SQL and Google Sheets, with no Python. You need the SQL that shows up in real work, not every feature the language has.

How much SQL you need
Core concepts
Functions and logic you will use daily
Deeper cuts
Writing SQL that does not look junior
Practice
Where to go next

How much SQL you need

How Much SQL You Actually Need for an Entry-Level Data Analyst Role: The target to aim at before you start applying.
The SQL That Shows Up in Real Analyst Jobs: What day to day analyst queries actually look like.

Core concepts

SQL Joins Explained for People Who Keep Forgetting Them: The join types and when each one is the right call.
Window Functions and When You'd Reach for One: What window functions do and the problems they solve.
CTEs vs Subqueries and Which to Write: How to choose between the two for readable queries.
Aggregations and GROUP BY Traps: The mistakes that make a total come out wrong.

Functions and logic you will use daily

How to Use SQL CASE Statements for Conditional Logic in Data Analysis: Conditional logic inside a query.
SQL Date and Time Functions Every Data Analyst Should Know: Working with dates and times in reports.
SQL String Functions Analysts Actually Use at Work: The text functions worth knowing for cleaning data.
How to Handle NULL Values in SQL Without Breaking Your Queries: Keeping NULLs from silently changing your results.
SQL UNION vs UNION ALL: When to Use Each (With Examples): When stacking results needs one over the other.

Deeper cuts

SQL Self-Joins Explained: How to Join a Table to Itself (With Examples): When a table needs to be joined to itself.
RANK vs DENSE_RANK vs ROW_NUMBER in SQL: What's the Difference (With Examples): How the three ranking functions differ.

Writing SQL that does not look junior

SQL Habits That Make You Look Junior (and How to Fix Them): The habits reviewers notice first and how to replace them.
How to Write SQL Other People Can Read: Formatting and structure that teammates can follow.
Reading and Fixing SQL You Didn't Write: How to work through a query someone else wrote.

Practice

How to Practice SQL So It Sticks: A practice routine that builds recall instead of familiarity.
Where to Practice SQL for Free: Free places to write queries on real data.

Where to go next

This guide is part of Skills to Get Hired as a Data Analyst. Pair it with the Excel guide, put your queries to work in a portfolio project, and see how SQL gets tested in technical interviews. If you use AI to study, read how to use AI to learn SQL faster.

If you want a day by day plan that builds the SQL you need alongside projects and the job search, join Analyst Hive.