Revisiting SQL After Decades: Reviewed This Much with SQLZOO

Table of Contents

1. Introduction
2. SQLZOO Learning Summary
3. Future Learning Plans

Introduction

I am an aspiring data analyst currently studying to transition into the field, with no prior professional experience as a data analyst. In the 1990s, I worked with Microsoft Access and SQL in a hands-on capacity and gained practical experience using databases for data extraction and manipulation. At that time, data analysis was not as common in the workplace, but I learned SQL and Access directly from my seniors, which provided me with a solid understanding of basic SQL syntax and database operations.

Later, I pursued studies abroad, where I had the opportunity to learn the basics of data analysis using Excel in 2017 and 2020. Professionally, I have experience in public relations and marketing communications, where I have come to realize the importance of data analysis in decision-making. Building on this background, I began more focused learning on data analysis in mid-February 2025, with a goal of transitioning into the role of a data analyst. To start, I revisited SQL through SQLZOO, where I reviewed both basic and practical content.

SQLZOO Learning Summary

Here are some of the key concepts I have learned through SQLZOO:

• SELECT Clause: Retrieve specific columns from a database.
• FROM Clause: Specify the table from which to fetch the data.
• WHERE Clause: Filter data based on specific conditions.
• LIKE Operator: Perform partial string matching to retrieve data matching a given pattern.
• IN Operator: Fetch data matching any value from a specified list.
• LENGTH Function: Calculate the length of a string.
• COUNT Function: Count the number of records that meet a specific condition.
• BETWEEN Operator: Extract data within a specified range.
• DISTINCT Operator: Eliminate duplicate data and retrieve unique results.
• ORDER BY Clause: Sort the results in ascending (ASC) or descending (DESC) order.
• GROUP BY Clause: Group data for aggregation purposes.
• HAVING Clause: Apply conditions to aggregated data.
• JOIN Syntax: Combine multiple tables to retrieve related data, including INNER JOIN, RIGHT JOIN, etc.
• COALESCE Function: Replace NULL values with alternative values.
• CASE WHEN: Return different values based on conditions.

By actively working through SQLZOO, I have deepened my understanding of SQL, especially in areas like table joins, aggregation with conditions (using HAVING), handling NULL values with COALESCE, and other essential features used in real-world applications.

Future Learning Plans

Moving forward, I plan to continue expanding my SQL knowledge using real-world datasets from platforms like Supabase and Kaggle, and to explore data analysis using Python in combination with SQL. Through these projects, I aim to improve my skills in data preprocessing, visualization, and modeling.

In addition, I plan to share my learning progress and project results on platforms like GitHub and Qiita.