Data Analyst-Focused SQL: Complete 10-Day Course

By the end of this lesson, students will be able to:

• Use SQL string functions like UPPER, SUBSTR, and TRIM

• Apply number functions such as ROUND, MOD, ABS, CEIL, and FLOOR

• Work with date calculations using DATE() and JULIANDAY()

• Summarize data using aggregate functions like COUNT, SUM, AVG, MIN, and MAX

• Group and filter data using GROUP BY and HAVING

• Practice SQL with a real-world sample dataset for hands-on learning

In this lesson, we dive into one of the most powerful SQL tools used in analytics — the GROUP BY and HAVING clauses.

You’ll start by creating a sample Orders table and inserting relevant data to practice on. From there, you’ll explore how to group records by region, category, customer, and even extract the year from date fields to analyze yearly trends.

We'll cover how to use key aggregate functions like:

• SUM() – total of a column

• COUNT() – number of records

• AVG() – average value

• MIN() / MAX() – smallest/largest values

Then you’ll learn the difference between WHERE and HAVING, and when to use each. WHERE filters raw data, while HAVING filters after grouping.

This lesson also covers how to:

• Group by multiple columns

• Use aliases to rename output columns

• Filter groups using HAVING with conditions

• Group using expressions like extracting year from a date

At the end, you’ll apply your knowledge through simple practice exercises that reinforce everything you’ve learned.

By mastering these concepts, you'll be ready to perform summary reports, group-level filters, and build insights that are key for real-world data analyst and business analyst roles.

In this module, you’ll master one of the most powerful features of SQL — combining data across multiple tables. Real-world databases are relational in nature, and knowing how to extract meaningful insights by linking data is an essential skill for every data analyst or business analyst.

We’ll cover all types of JOINs (INNER, LEFT, RIGHT, FULL OUTER, and CROSS), learn how to merge datasets with UNION and UNION ALL, and explore when and why to use each operation. With hands-on examples, you'll confidently work with multi-table queries in any professional environment.

By the end of this session, you'll be able to connect and analyze data like a pro!

In Day 6, you'll learn how to apply advanced SQL filtering methods such as BETWEEN, IN, LIKE, and handle missing values using IS NULL. You'll also dive into the powerful CASE statement to create custom classifications like labeling orders as Bulk, Medium, or Small. These techniques help you write cleaner, smarter queries that are essential for real-world data analysis tasks.

In this lesson, you will learn how to use subqueries in SELECT, WHERE, and FROM clauses to perform nested operations and filter or transform your data. You'll also explore CTEs (Common Table Expressions) using the WITH clause to write cleaner and more modular SQL queries. By the end of this session, you'll be able to solve multi-step problems efficiently using both subqueries and CTEs, including a quick introduction to recursive CTEs.

In this hands-on session, you'll master the essential skills of Data Cleaning and Transformation in SQL — a vital part of every Data Analyst’s toolkit.
Using real-world-style examples, we'll walk you through the most common data quality issues and how to fix them efficiently using SQL queries.

You'll learn how to handle NULL values, remove duplicates, clean up messy text fields, work with inconsistent date formats, and convert data types properly. We’ll also cover how to apply conditional logic using CASE WHEN to segment your data for better insights.

This lesson is structured with:

• Simple, step-by-step SQL queries

• Clear explanations

• Practice-friendly syntax using SQLite / DB Browser

By the end, you'll be ready to prepare clean, analysis-ready datasets and take your SQL skills to the next level.

In this lesson, you’ll explore time-series analysis using SQL. You'll learn to extract monthly trends, calculate running totals, use window functions like LAG, and compute YTD sales. These techniques are essential for analyzing business performance over time and are commonly used in data analyst and business intelligence roles.