Data Analysis

LearnSQL.com Team

SQL for Data Analysis Cheat Sheet

Welcome to our SQL for Data Analysis Cheat Sheet — a must-have resource for anyone looking to harness the power of SQL in the realm of data analysis. Whether you're a data enthusiast, a business analyst, or a seasoned data professional, this cheat sheet is designed to empower you to extract valuable insights from your datasets. Inside, you'll find a carefully curated collection of SQL commands and tips that will elevate your data analysis game.

Kateryna Koidan

Roadmap to Becoming a Data Analyst

What is a data analyst? What are their daily duties, and what skills do they need? In this article, I discuss the role of data analysts and share a step-by-step guide on how to become one. As organizations start to realize the value of their data on their customers, target audience, competitors, suppliers, and other market players, the role of data analysts becomes more important. To become a successful data analyst who can bring real value to the organization, you need to possess a specific set of skills.

Maria Durkin

SQL for Data Analysis: 15 Practical Exercises with Solutions

Are you new to the world of SQL and eager to unlock the power of data analysis?. In this article, we'll improve our SQL skills through 15 practical, hands-on exercises designed specifically for beginners. Because when it comes to SQL, practice truly makes perfect! We’re going to shine a spotlight on the critical domain of data analysis, where SQL takes center stage. SQL, or Structured Query Language, plays a pivotal role in data analysis.

Nicole Darnley

Learn SQL for Data Analysis with LearnSQL.com

Discover how to learn SQL for data analysis and start making sense of your data today. Our guide provides a clear and simple SQL learning path for data analysts, helping you query databases and analyze data with ease. Whether you're starting from scratch or looking to polish your skills, we'll help you every step of the way. When I was in high school, I planned to go to college and major in interior design.

Maria Durkin

SQL Pivot Tables: A Step-by-Step Tutorial

Are you a data analyst looking to better your abilities? Or perhaps you want to discover what tools you need to succeed in this role? Today, we're going to look at one of the most important tools for data analysts: the SQL pivot table. An SQL pivot table is an essential tool for quickly arranging and summarizing data. In just a few lines of code, it helps analysts rapidly and effectively identify patterns and trends in data.

Jakub Romanowski

What Tools do I Need to Use SQL? A Guide For Beginners in Data Analysis

Welcome! In this guide, I’ll introduce you to the most essential SQL tools to help you start working with data quickly and efficiently. Whether you're new to SQL or looking to improve your skills, these tools will simplify your workflow and accelerate your progress. When I started learning SQL, I felt overwhelmed. So many databases, tools, and platforms were out there and I didn't know where to begin. But, after some time, I realized it’s not about knowing everything—it’s about understanding the right tools to help you along the way.

Gustavo du Mortier

How SQL Helps Optimize Healthcare Workflows

SQL is a universal language for healthcare data management and workflow optimization. In this article, we will see how to use SQL for everything from scheduling medical appointments to monitoring vital signs. Healthcare professionals must adhere to various processes to perform their duties. These processes, or workflows, are adapted to various situations – patient reception, diagnosis, monitoring, billing, clinical documentation, etc. While these workflows vary by type of care, setting (hospital, clinic, telemedicine), and the role of the practitioner, they all require the efficient and accurate handling of large volumes of critical information.

Radu Gheorghiu

Building Analytical Data Pipelines with SQL

If you’re trying to build an analytical data pipeline, then SQL is the perfect tool for the job. It will help your organization build a data analytics foundation that turns data into business value. Why should you care about building data pipelines with SQL? This might sound like a technical challenge, but a strong data pipeline is one of the most essential tools for turning raw data into actionable insights.

Jakub Romanowski

What’s the Benefit of SQL in Business Analysis? 5 Practical Examples

Why do you need SQL in business analysis? Well, the short answer is that it makes your work more effective and efficient. In this article, I’ll show you some simple, real-world examples of how SQL can help your daily business analysis work. Are you a business analyst, or do you frequently analyze business data? Or are you planning to get into this field? Then I’d recommend you learn SQL. SQL (Structured Query Language) isn’t just the language of relation databases – it’s also the language of data analysis.

Alexandre Bruffa

Top 5 Reasons PostgreSQL Works for Data Analysis (and Data Analysts!)

Why do analysts love PostgreSQL for data analysis? Learn why this database management system is so beloved of database professionals and data scientists alike! If you work with data, you know that data analysis requires the efficient storage, management, and retrieval of large datasets. Thus, data analysts prefer to use relational databases that are well-known for their robustness, efficiency, and stability. Relational databases work with a database management system (DBMS), which allows the creation, management, and manipulation of relational databases.

Michał Wasiluk

An Introduction to Spatial Databases and Geospatial Data

Discover how spatial databases and geospatial data enhance spatial queries, support GIS integrations, and enable advanced data analysis. Spatial databases are specialized database systems designed to store, manage, and query geospatial data efficiently. These databases provide support for spatial data types and spatial indexing, allowing for complex spatial queries and analysis. Geospatial data, stored within these databases, represents information about the physical world tied to specific locations on Earth.

Alexandre Bruffa

Integrating SQL with Looker for Data Analysis

You may have heard the term “data-driven”. All big companies nowadays have a data-driven approach; it simply means they make strategic decisions based on data analysis and interpretation. To streamline the process and stay competitive, companies rely heavily on data analysis and Business Intelligence (BI) tools. One of the most famous powerful BI platforms is Looker. Now owned by Google, Looker is well known for its data exploration, visualization, and reporting capabilities.

Tihomir Babic

SQL for Financial Reporting

SQL should be part of every modern financial reporting process. It can deal with gigabytes of data and perform financial calculations in a split second. It’s time to transition from Abacus (also known as Excel!) and start using SQL in financial reporting.    Data has taken a central part in business decision making. This applies even more to financial data. The main goal of businesses is to make a profit, so it’s no wonder that financial data plays a crucial role in the strategic decision-making process of any modern business.

Kamila Ostrowska

Mastering MySQL for Data Analysis

Mastering MySQL can significantly benefit anyone looking to advance in data science or database management. If you develop your data analysis skills in MySQL, you’re on the right path to positively influence your career. In this article, you’ll learn how to practice MySQL effectively and what kind of learning sources to choose. You’ll see for yourself that MySQL is not rocket science and that using it can be easy and intuitive.

Radu Gheorghiu

Integrating External Data Sources with SQL Databases

In today's world, data is the new currency and organizations are swimming in vast oceans of information from various sources. Trying to make sense of all this disparate data is a challenge; one of the most common ways to solve that challenge is by integrating external data sources with SQL. In this article, I'll guide you through strategies and techniques for seamlessly blending data from APIs, flat files, NoSQL databases, and other external sources into your SQL databases.

Nicole Darnley

Data Visualization Using SQL: A Guide for Data Analysts

Start exploring data visualization using SQL with our practical guide. You’ll learn how to harness the powerful features of SQL to create effective visualizations that enhance your data analysis – and make it more understandable and impactful. SQL is a specialized programming language designed for managing and manipulating relational databases. It provides a standardized framework for querying, updating, and manipulating data. It is simple and yet offers a robust set of commands and functions to interact with data.

Maria Durkin

The Data Analysis Lifecycle: From Dataset to Presentation

In today's fast-paced decision-making climate, data is an invaluable resource. Raw data is like a rough diamond: it has great potential, but it must be processed to reveal its true brilliance. Achieving this brilliance is where the data analysis lifecycle comes in. The data analysis lifecycle maps out a structured journey of five steps: Collection Cleaning Analysis Interpretation Presentation Mastering the right tools for each step is key, and that's where SQL proves indispensable.

LearnSQL.com Team

Does Data Analytics Require Coding

Thinking about a future in data analytics? You might be pondering the need for coding skills. Is it necessary to be good at programming? As the worlds of data analytics, technology, and data science merge, the importance of coding becomes clearer. It’s crucial to understand which programming languages are key and how much expertise you need, making the journey into this vibrant field less daunting. In this article, we're going to focus on the importance of coding, particularly SQL, for data analytics.

Nicole Darnley

Efficient Data Analysis: Leveraging SQL with R

This article delves into the nuances of using SQL with R in data analysis. It offers insights and practical examples that demonstrate the effectiveness of this combination. By integrating SQL with R, analysts can leverage the strengths of both languages. This helps them perform comprehensive data analyses, going from initial data retrieval to complex statistical modeling and visualization. To kickstart our exploration of the dynamic duo of SQL and R in data analysis, it's essential to grasp how these two powerful tools can revolutionize the way we handle, manipulate, and interpret vast datasets.

Jill Thornhill

Integrating SQL with Python for Data Analysis

Integrating SQL with Python isn’t difficult. The two tools work together to combine the information-processing power of relational databases with the flexibility of a programming language. In this article, I will discuss the benefits of data analysis using SQL and Python, with real-world coding examples. Why do most of the best data analysis tools on the market – such as Tableau and Power BI – include both SQL and Python in their toolboxes?

Jakub Romanowski

Analyzing Social Media Data with SQL

Diving into the world of SQL social media analysis? This guide will help you start using SQL to analyze and interpret data from social media platforms. Get ready to transform your approach to digital data and unlock new possibilities in social media analytics! Welcome to the exciting world of SQL and social media analysis! If you're new to this field, you're about to discover how powerful a tool SQL can be in understanding the vast sea of data generated on social media platforms every day.

Maria Durkin

SQL and Power BI: Transforming Data into Insights

The need for powerful tools that enable efficient data analysis and visualization has never been greater. Enter SQL and Power BI, a potent duo that enables businesses to maximize the value of their data assets. First, let’s explain what SQL and Power BI are; then we’ll discuss how and why they make such a great pair. SQL, or Structured Query Language, allows you to communicate with databases, making it the foundation of data management.

Alexandre Bruffa

Python vs. SQL for Data Analysis

You surely have heard about SQL and Python. Maybe you’ve even worked with one of those languages. Both have strengths and weaknesses. When it comes to data analysis, which should you use? This article will demonstrate how Python and SQL are useful for data analysis and how knowing both languages can boost your data analysis journey. Decided to get into data analytics? Great! An increasing number of companies are looking for people who can analyze data and draw conclusions from it.

Maria Durkin

How AI & SQL Can Accelerate Your Data Analysis Workflow

Did you know that using AI for data analysis can be a total game changer for SQL developers and data analysts? In this article, we’ll learn about how AI can help you level up your data analysis skills and why it’s a must-have tool in every data professional’s toolkit. Artificial Intelligence (AI) has emerged as a game-changer in the workforce, transforming industries and redefining how professionals deal with data. Specifically, the use of AI for data analysis is becoming increasingly critical in making informed decisions.

Jakub Romanowski

Best SQL Books for Data Analysis

In the world of data analysis, SQL is a powerful tool. It's a language that helps professionals find, organize, and analyze the data stored in databases. If you're looking to learn this important skill, you've come to the right place. This article will guide you through the best SQL books for data analysis, whether you're just starting out or already have some experience. SQL (Structured Query Language) is a standard language used for working with databases.

Maria Durkin

SQL for Data Analysis: What Should I Learn?

You may already be aware of SQL's importance in data analytics. But what features of SQL are important to data analysis and where can you learn them? In this article, we will answer your questions. Before we talk about using SQL for data analysis, it helps to know what we’re talking about. So, let’s define these two things. Structured Query Language (SQL) is a programming language that is used to manage data in relational databases.

Jill Thornhill

Google BigQuery SQL Syntax: A Comprehensive Guide

What is Google BigQuery? What can it do for your organization? Is BigQuery SQL a valuable skill? Where can you learn it, and where can you get some practice? Very few organizations today are not computerized. Many processes now make use of the IoT (Internet of Things), where all kinds of devices are networked and continually feed real time data into computer systems. The result is a vast amount of data available for decision-making.

Nicole Darnley

Top 7 Advanced SQL Queries for Data Analysis

Explore essential advanced SQL queries for data analysis. Structured Query Language, or SQL, is an indispensable tool for data analysts. Most people pick up the language relatively quickly and can begin in-depth data analysis after just a few lessons. Because of this, many data analysts tend to stay in the beginner/intermediate level of using SQL. This level of understanding allows you to “get the job done”, but it might not be the most efficient way to write a query.

Nicole Darnley

Data Cleaning in SQL

Data cleaning is an important part of any data analysis. Here we’ll discuss techniques you can use to do data cleaning in SQL. I find it nearly impossible to focus on work when my desk is a mess. If it’s cluttered with paper, coffee mugs, or random toys my daughter has somehow snuck into my office, there is no chance I will be able to get anything done until my desk is back in order.

Kateryna Koidan

How to Learn SQL for Marketing Analytics With LearnSQL.com

SQL is a valuable tool for any experts wanting to make data-driven decisions. Marketers are no exception. Let's explore how to design an effective learning path for SQL in marketing. SQL for marketing analytics is used to understand customers better and to increase the effectiveness of marketing campaigns. If you work in marketing and want to join other marketing experts who have already benefited from this tool, you need to learn SQL.

András Novoszáth

How to Get More from Your BI Tools with SQL

Are you frustrated with the limitations of your business analytics tool? Do you wonder if there are other features you could tap into? Would you like to know how SQL compliments your work with Power BI and Tableau? Read this article to learn more! If you are a data analyst or if data analysis is part of your everyday job, Business Intelligence (BI) tools are crucial for your work. You also know that most databases run SQL queries in the background of such tools.

Kateryna Koidan

Use SQL for Data Analysis With Google Analytics 4

Google Analytics 4 offers data analysts powerful new capabilities, and learning how to maximize its potential is essential. In this article, I'll explore how using SQL with Google Analytics 4 can enhance your data analysis and lead to more insightful results. Google Analytics 4 is a next-generation analytical solution designed for cross-platform measurement and analysis, helping data analysts and marketers understand their audience more effectively. The quality of your data analysis with Google Analytics 4 depends heavily on your ability to work with tools like Data Studio and BigQuery, and thus, proficiency in SQL is crucial.

Himanshu Kathuria

These SQL Queries Will Help You With Data Analysis

Need to streamline your everyday data analysis tasks? This article explains how basic SQL queries can help. If you are planning a corporate career or any field that involves working with data, you must have come across SQL, or Structured Query Language. SQL was first developed in the 1970s and it continues to be the industry standard for database interactions. Many of you may already have decided to learn this language, given how powerful it is.

András Novoszáth

The Recent Rise of DBT and the Analytics Engineer

If you’re interested in data engineering or data analytics, you should know about DBT. And you should also learn about the role of an analytics engineer. We cover both in this article. The rise of Cloud data providers has led to new challenges and possibilities. If you are thinking about entering the data analytics space, it is important to be aware of them; it can help you decide what roles to aim for and what skills to learn.

András Novoszáth

Here’s Why You Should Use SQL for Sales Analytics

Would you like to understand how your sales efforts play out so that you know where to put your focus? Do you want to empower your sales teams with sales analytics? This article shows you how you can use SQL to use your sales data to your advantage. If you work in sales, understanding the activities that produce results has financial consequences for you, your team, and your company. This is where sales analytics can really help you.

Kateryna Koidan

Still Using Excel for Data Analysis? See Why SQL Is Better!

Are you working with data and still using spreadsheets? Many people think that only programmers or professional data analysts can leverage SQL for data analysis. In this article, I’ll show how people with no IT background can use SQL for their daily work tasks. Not all people working with data have the corresponding job titles like data scientist, data analyst, or data engineer. Following the data democratization trend, almost all office workers do something with data as part of their daily duties – marketers analyze advertising campaigns, HR specialists analyze employee dynamics, auditors analyze credit portfolios, etc.

András Novoszáth

Read These 5 Data Analysis Books to Jump-Start Your Career

Do you want to pivot into a data analyst role, but you are unsure where to start? Do you do analytics in your job and would like to learn more about the big picture of data? Are you looking for a great data analysis book to read? In this article, you will find our selection of the best books on data analysis! If you are a business professional thinking about picking up data analytics skills, you may be wondering how you should start.

Himanshu Kathuria

Top 10 Tools for Business Analytics

Business Analytics is one of today’s hottest career fields. However, success as a business analyst requires that you learn the right skills and software programs. Let’s see what tools you should know if you want to be a business analyst. With the world generating immense amounts of data – and having systems in place to capture and manipulate that data to fuel business growth – job opportunities for business analysts are only going to grow.

András Novoszáth

Why You Should Use SQL in Marketing Analytics

Have you been wondering how you could use SQL in your marketing data analysis work? Would you like to create SQL reports to guide your marketing decisions? In this article, you will learn how to use SQL for marketing through plenty of use cases. Are you a marketing professional wanting to better understand the results of your marketing efforts? Do you want better means to prove your point and demonstrate the value you create?

Michal Stín

How to Use SQL to Calculate Customer Lifetime Value (LTV)

Working with data is an essential skill for marketers in today's data-driven world. As a marketer myself, I decided to create a series of articles about calculating key performance indicator (KPI) metrics for SaaS (Software as a Service) companies. We’ll start by calculating customer lifetime value (LTV) using SQL. Data is one of today’s most valuable resources. If you work in a SaaS company, you are flooded with a huge amount of data every day.

Tihomir Babic

Business SQL: How to Calculate Revenue Growth in SQL

Can you use SQL in business? Of course you can! For example, you can calculate revenue growth in SQL – here’s how. As someone who works with data, you already know that SQL is the most used tool in data analysis. But how is SQL helpful in business use? Well, the answer is obvious: analyze business data using SQL! However, there are some specifics with business data that can be tricky if you’re not used to it.

Himanshu Kathuria

Analyze Small Datasets with SQL

If you think SQL data analysis is useful only when a business is generating millions of rows of data – that SQL cannot help you if you are generating a limited dataset – think again and read on! There is so much talk about how SQL is useful in analyzing Big Data that sometimes the applications of SQL data analysis for small datasets can go unnoticed. Having worked with early-stage businesses – which have just started generating data through daily transactions – as well as big established clients generating terabytes of data, I can tell you for sure that SQL can be an extremely handy tool for just about any business.

Tihomir Babic

6 Advanced SQL Queries for Analyzing Financial Data

Are you an advanced SQL user who’s new to finance? Do you want to learn how to use your knowledge to analyze financial data? The article will give you some guidance. You probably know that SQL is a potent tool for analyzing all sorts of data. The bigger and more complex data, the more beneficial SQL becomes. In certain situations, financial data can be very complicated; producing a sophisticated analysis requires sophisticated tools.

Adrian Więch

Google Analytics and SQL

Google Analytics is a very popular tool among website and blog owners alike. With a simple and quick setup, it lets you gather data about your web page visitors easily. However, did you know that you can export data from Google Analytics to create your own SQL reports? Read this article to find out how. Google Analytics is an extremely popular and powerful solution that lets you collect and analyze various kinds of information about your website.

Tihomir Babic

5 Advanced SQL Constructions Every Data Analyst Should Learn

Does your job involve data analysis? Here are five examples of how advanced SQL can help you in your daily work. I’m a data analyst and I have to say – data analysts can be quite strange. Other people are usually afraid of immense amounts of data; we enjoy it. The deeper we get into data, the happier we are. An important tool for getting into data (and thus being a happier data analyst) is SQL.

Dejan Sarka

Statistics in SQL: Dependencies Between Discrete Variables

In my previous article, we looked at how you can calculate linear dependencies between two continuous variables with covariance and correlation. Both methods use the means of the two variables in their calculations. However, mean values and other population moments make no sense for categorical (nominal) variables. For instance, if you denote "Clerical" as 1 and "Professional" as 2 for an occupation variable, what does the average of 1.5 signify?

Kacper Skawina

Analyzing Survey Data: Explore Results Using SQL

Excel is a powerful beast that lets you analyze complex data. Yet, operating on big chunks of data can sometimes be a daunting task. Let's take a look at how SQL can help. Today, we'll tackle a common problem with importing data to an SQL database, using a real-life example. Suppose your company conducted a survey on the most popular programming trends and preferences, striving to meet the expectations of its users.

Aldo Zelen

SQL Reporting Guide for Business Professionals

Working with the financial aspects of large and small enterprises can be a daunting task for a business professional. In this article, we'll look at several ways of constructing the perfect SQL report. You've probably already heard about SQL from your colleagues or in other areas of your career. If you're here, you've likely concluded that learning SQL will make your professional life easier – and you're right!

Dejan Sarka

Statistics in SQL: Calculating Frequencies & Histograms

Database and Business Intelligence (BI) developers create huge numbers of reports on a daily basis, and data analyses are an integral part of them. If you wonder whether you can perform statistical analysis in SQL, the answer is ‘yes’. Read my article to learn how to do this! Statistics are very useful as an initial stage of a more in-depth analysis, i.e. for data overview and data quality assessment. However, SQL statistical analysis possibilities are somewhat limited as there are not many statistical functions in SQL Server.