The SQL Checklist for Junior Data Analysts in 2025

SQL is the backbone of data analysis, and every junior analyst in 2025 needs to know it well. This checklist will help you see where you stand, spot the skills you still need, and find the right resources to level up. Whether you’re just starting or preparing for your next interview, you’ll know exactly what to focus on.

Starting out as a data analyst can feel overwhelming. There’s so much to learn — tools, dashboards, business metrics — but at the center of it all is SQL.

SQL is the universal language of data. Whether you’re pulling reports for your manager, cleaning up messy datasets, or preparing for a job interview, you’ll use SQL almost every day. That’s why it helps to have a clear checklist: a way to see which skills you’ve already mastered and which ones you should focus on next.

This article gives you exactly that: a step-by-step SQL checklist for 2025. Use it to check your progress, spot gaps in your knowledge, and move forward with confidence. Each step comes with examples and resources you can use right away — whether you’re just starting out or polishing skills for your next role.

Make Sure You Can Handle the Basics

Every data analyst needs a solid grasp of SQL basics. No exceptions.

Even if you plan to use AI tools to help you write queries, you still need to understand what those queries are doing. Why? Because you’ll often need to spot mistakes, fix them, or explain them — and AI-generated queries are not always correct. Plus, during job interviews, you’ll almost certainly be quizzed on the fundamentals. There’s no way around it.

So what counts as the “basics” of SQL? Start with these core building blocks:

• SELECT – to pick the columns you want to see.
• WHERE – to filter your data.
• JOIN – to combine data from multiple tables.
• GROUP BY – to group your results.
• HAVING – to filter groups (a must-know that many beginners overlook!).
• ORDER BY – to sort your results.
• LIMIT – to control how many rows you get back.

Once you’re comfortable with these, you’ll be able to read and understand most everyday SQL queries.

Where to Learn and Practice

• Start with our SQL Basics course — it walks you through each concept with simple, real examples.
• Need to refresh specific topics? Jump into our focused practice courses:
  • SQL JOINs
  • SQL GROUP BY Practice
  • Subqueries Practice
• Want to practice everything? Explore the SQL Practice track — it’s packed with real-world exercises across different topics.
• And don’t forget to keep our SQL Basics Cheat Sheet handy whenever you need a quick refresher.

Learning SQL is like learning a new language — the more you use it, the more fluent you become. Master these basics, and you’ll be ready to move on to the more advanced skills on your checklist.

Have Data Cleaning Covered

Once you’ve mastered the basics, it’s time to polish your SQL skills — and that means learning how to clean messy data.

Real-world data is rarely neat and tidy. It’s often full of missing values, typos, inconsistent formatting, or outdated entries. As a junior data analyst, one of your most valuable skills will be turning that messy data into something reliable and usable.

To do that, you’ll need to be comfortable with two things:

• Changing data — knowing how to insert, update, or delete rows when needed.
• Fixing values — using SQL functions like TRIM(), REPLACE(), ROUND(), and others to standardize text, clean numbers, and handle errors.
  

Where to Learn and Practice

• Start with these hands-on courses:
  
  • How to INSERT, UPDATE, and DELETE Data in SQL — Learn how to safely modify data in a database. This course walks you through inserting new rows, updating existing records, and removing outdated ones — all essential for cleaning and maintaining datasets.
  • Standard SQL Functions — Discover the functions you’ll use every day as an analyst. You’ll practice trimming text, replacing values, rounding numbers, and working with dates and times. These functions are the backbone of cleaning messy data.
• The best way to practice is by restructuring real datasets — normalizing or denormalizing them to clean things up. This article shows how to do it step by step:
  • SQL Practice That Feels Like the Real Thing: Meet db
• And for a complete walkthrough of the cleaning process, check out:
  • Data Cleaning in SQ

Data cleaning might not sound glamorous, but it’s one of the most important daily tasks for analysts. Once you’re confident here, you’ll be ready to take on more advanced SQL challenges.

Get Comfortable with Complex Queries

Once you’re comfortable cleaning data, it’s time to level up: writing and managing long, complex queries.

As a junior data analyst, you’ll often need to answer questions that can’t be solved with a single SELECT or JOIN. Instead, you’ll have to build queries step by step, combining different techniques in a clear, logical way.

Here are a few key skills you’ll need:

• Subqueries – write queries inside other queries to break complex problems into smaller steps.
• CTEs (WITH clauses) – structure your queries like building blocks, making them easier to read and debug. Example: Use a CTE to calculate monthly revenue, then use the result to find year-over-year growth:
  

  WITH monthly_revenue AS (
   SELECT DATE_TRUNC('month', order_date) AS month,
   	SUM(amount) AS revenue
  	 FROM orders
  	 WHERE status = 'completed'
  	 GROUP BY 1
   )
   SELECT m1.month,
   	m1.revenue AS revenue_this_year,
  	m2.revenue AS revenue_last_year,
  	ROUND(
  	 	CASE WHEN m2.revenue IS NULL OR m2.revenue = 0 THEN NULL
  	 	ELSE (m1.revenue - m2.revenue) / m2.revenue * 100
  	END, 2) AS yoy_growth_pct
   FROM monthly_revenue m1
   LEFT JOIN monthly_revenue m2
   ON m2.month = m1.month - INTERVAL '1 year'
   ORDER BY m1.month;
  

• CASE WHEN – create your own categories inside queries. Example: Categorize orders into value buckets:
  

  SELECT order_id,
   amount,
   CASE
   	WHEN amount < 100 THEN 'Low'
  	WHEN amount < 1000 THEN 'Medium'
  	ELSE 'High'
   END AS value_bucket
   FROM orders
   WHERE status = 'completed';
  

Pivoting – turn rows into columns to build table-like reports directly in SQL.

Where to Learn and Practice

• Start with these courses to build your foundation:
  • Creating Basic SQL Reports — Learn how to turn raw query results into structured reports that answer business questions. This course teaches you how to group, filter, and present data in a way that’s clear and useful for stakeholders.
  • The Standard SQL Functions course includes a detailed section on using CASE
• For practical, real-world practice that involves long, multi-step queries:
  • SQL Revenue Trend Analysis — Build queries that track revenue over time, compare performance across periods, and uncover growth patterns. Perfect practice for structuring CTEs and handling date-based analysis.
  • SQL Customer Behavior Analysis — Analyze how customers interact with products over time. You’ll write multi-layered queries that combine joins, subqueries, and CASE statements to answer real business questions.

Mastering how to structure and organize complex SQL logic is what sets apart beginners from intermediate users — and makes your work easier to maintain and explain.

Check Off Working with Dates

Data analysis almost always involves time. Whether it’s tracking sales per month, analyzing user activity by day, or comparing year-over-year trends, you need to be comfortable working with dates in SQL.

Here are some must-know skills:

• Filtering by date ranges – select only the rows from a certain period.
  

  SELECT *
  FROM orders
  WHERE order_date >= CURRENT_DATE - INTERVAL '30 days';
  

• Extracting parts of a date – pull out the year, month, or day to group or filter by.
  

  SELECT order_id,
    EXTRACT(YEAR FROM order_date) AS order_year,
    EXTRACT(MONTH FROM order_date) AS order_month
  FROM orders;
  

• Rounding or grouping dates – aggregate by week, month, or quarter.
  

   SELECT DATE_TRUNC('month', order_date) AS month,
    SUM(amount) AS monthly_sales
  FROM orders
  WHERE status = 'completed'
  GROUP BY 1
  ORDER BY month;
  

These techniques are essential for reporting and will come up again and again in your day-to-day work.

Where to Learn and Practice

• The Standard SQL Functions course covers date and time functions in detail.
  
• You’ll also get plenty of real-world practice with dates in the SQL Revenue Trend Analysis and SQL Customer Behavior Analysis

Learning how to handle dates is what turns raw transactions into meaningful time-based insights — monthly revenue, user retention, churn analysis, and more.

Master Window Functions

Once you’ve mastered the basics and learned how to structure complex queries, it’s time to tackle one of the most powerful SQL features: window functions.

Why are they so important? Window functions let you perform calculations across sets of rows while still keeping the detail of each individual row. This makes them perfect for real-world data analysis tasks like:

• Ranking customers by revenue
• Calculating running totals or moving averages
• Comparing each row to group totals or averages

Examples of Window Functions

Rank customers by their total spend:

SELECT customer_id,
       SUM(amount) AS total_spent,
       RANK() OVER (ORDER BY SUM(amount) DESC) AS spend_rank
FROM orders
WHERE status = 'completed'
GROUP BY customer_id;

Calculate a running total of sales by date:

SELECT order_date,
       SUM(amount) AS daily_sales,
       SUM(SUM(amount)) OVER (ORDER BY order_date) AS running_total
FROM orders
WHERE status = 'completed'
GROUP BY order_date
ORDER BY order_date;

These queries show why window functions are a favorite tool among data analysts: they answer questions that would otherwise require multiple subqueries or very complex joins — all in a single step.

Where to Learn and Practice

• Start with the Window Functions course to learn the theory.
• Then sharpen your skills with the Window Functions Practice Set.
• Keep the Window Functions Cheat Sheet handy — it’s a great quick reference when you’re stuck.

Mastering window functions will give you an edge in analysis, letting you move beyond basic summaries to deeper insights into customer behavior, revenue patterns, and trends over time.

Checklist Review: Final Thoughts

SQL is one of those skills that pays off again and again. As a junior data analyst in 2025, your checklist should cover the essentials — from SELECT and JOINs, to cleaning messy data, structuring long queries, working with dates, and unlocking the power of window functions. The more you practice, the more natural it becomes, and the faster you’ll be able to turn raw data into real insights.

If you’re serious about building your career, the best way to keep learning (and stay ahead of the curve) is with our All Forever SQL Plan. It gives you unlimited access to all our courses — current and future — for a single one-time payment. No recurring subscription, no hidden costs, just lifetime access to everything you need to grow from beginner to advanced.

With this plan, you can:

• Start with SQL Basics and move up through advanced topics like Window Functions and Subqueries.
• Refresh specific skills whenever you need to.
• Keep practicing with real-world problem sets.
• Access every new course we release in the future.

Your SQL journey doesn’t have to stop here. With the All Forever SQL Plan, you’ll always have the tools to keep improving — and to stay one step ahead in your data career.

👉 Get lifetime access now