SQL101: Introduction to SQL for Data Analysis

Introduction
SQL (Structured Query language) is an essentially powerful tool for Data Analysts. It is very helpful in instances where one is working with data held in relational databases.
SQL is useful in accessing and manipulating data allowing Data Analysts to extract valuable insights and make informed resolutions. Techniques in SQL for data analysis include; retrieving data, filtering data, joining tables, aggregating data, and creating tables.

Data Analysis

Data analysis is an undertaking that involves inspection, cleaning, transforming, and modeling data to obtain useful insights for effective decision-making. Various techniques and methods are employed to identify patterns, trends, and relationships in the data.

Relational databases

This is a type of database that stores and sorts data in a collection of related tables. It consists of one or more tables, each with a unique name, and each table consists of columns and rows.

*What is SQL? *

Pronounced as "Sequel", is a special-purpose programming language that is used to manage and manipulate data in relational databases.

History of SQL

SQL was first developed in the early 1970s by Raymond F. Boyce and Donald D. Chamberlin, who were IBM researchers. They had been working on a project referred to as System R, which was an illustration of a relational database management system (RDBMS). the goal was to build a new type of database that would be more user-friendly and flexible than existing databases.
SQL was created with the user in mind, allowing both technical and non-technical users to interact with relational databases. It was based on the principles of relational algebra and set theory, and was created to be a declarative language, which means users were required to specify what they wanted the database to do rather than how to do it.
In the 1980s, SQL was adopted as the language for accessing and manipulating data in relational databases. In 1986, the first official SQL standard was published by ANSI (American National Standards Institute) and later adopted as an international standard by ISO (International Organization for Standardization).
Why SQL for data analysis and no other technologies
SQL has grown in its popularity as an effective data analysis tool for various reasons. These are;

1. Flexibility – SQL offers a high rate of flexibility in data analysis. It is used to extract data, filter data aggregate data, and join data from multiple tables.
2. Efficient querying – SQL allows for efficient querying of large datasets enabling analysts to extract relevant data needed for analysis quickly.
3. Reproducibility – SQL scripts can be saved and reused making it easier to reproduce analyses and ensure that the results are consistent over time.
4. Standardization – SQL is a standardized language used by many different database management systems
5. Scalability – SQL is suitable to work with large datasets which are increasingly vital as the data volume being generated keeps growing.
6. Data manipulation – SQL allows for data manipulation such as adding, updating, or deleting data in a database. This offers an efficient way to clean and prepare data for analysis.
7. Accessibility – SQL tools are easily available for free to any user.

Components of SQL

SQL (Structured Query Language) is a domain-specific programming language used for managing and manipulating relational databases. It is comprised of several components, including:

1. Data Definition Language (DDL): DDL is used to define and manage the structure of the database, including tables, views, indexes, and other database objects. Common DDL statements include CREATE, ALTER, and DROP.
2. Data Manipulation Language (DML): DML is used to manipulate data within the database, including inserting, updating, and deleting records. Common DML statements include INSERT, UPDATE, and DELETE.
3. Data Query Language (DQL): DQL is used to query the database and retrieve data from one or more tables. The most common DQL statement is SELECT.
4. Transaction Control Language (TCL): TCL is used to manage transactions within the database. Common TCL statements include COMMIT, ROLLBACK, and SAVEPOINT.
5. Data Control Language (DCL): DCL is used to control access to the database and its objects. Common DCL statements include GRANT, REVOKE, and DENY.
6. Data Administration Language (DAL): DAL is used to manage the security, backup, and recovery of the database. Common DAL statements include BACKUP, RESTORE, and CREATE USER.

Each of these components plays a critical role in managing and manipulating data within a relational database. By leveraging these components, developers, data scientists, and analysts can work with data in a structured, efficient, and secure manner.

SQL Techniques used in Data Analysis

Understanding how SQL components work is an essential skill for any Data Analyst as well as Data scientist. To effectively analyze data in SQL, a variety of techniques are used. These are;

1. SELECT STATEMENTS This is the most basic SQL command that is used to retrieve data from a database. Users can specify which columns of data they want to receive and the table to retrieve from. Below is a code snippet of how the SELECT statement is used;

-- Select all columns from a table
SELECT * FROM customers;

-- Select specific columns from a table
SELECT customer_id, customer_name, email FROM customers;

-- Select a calculated column
SELECT order_id, order_date, total_amount, total_amount * 0.2 AS tax_amount FROM orders;

1. Aggregation SQL gives several functions that are used to summarize data such as COUNT, SUM, AVG, MAX, and MIN. These functions are used to group and analyze data based on the different conditions as per the below;

-- Count the number of rows in a table
SELECT COUNT(*) FROM orders;

-- Calculate the average order amount
SELECT AVG(total_amount) FROM orders;

-- Group orders by customer and calculate the total order amount for each customer
SELECT customer_id, SUM(total_amount) FROM orders GROUP BY customer_id;

1. Joins These are used to combine data from two or more tables based on identical columns or keys. There are several types of joins, these are INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN. An example of how to use joins during analysis is;

-- Inner join two tables
SELECT orders.order_id, customers.customer_name
FROM orders
INNER JOIN customers ON orders.customer_id = customers.customer_id;

-- Left join two tables
SELECT customers.customer_name, orders.total_amount
FROM customers
LEFT JOIN orders ON customers.customer_id = orders.customer_id;

-- Full outer join two tables
SELECT *
FROM customers
FULL OUTER JOIN orders ON customers.customer_id = orders.customer_id;

1. Subqueries This is a query within a query in a database. It allows a user to extract data from a table based on conditions from another table. For example;

-- Select all customers with orders in the past month
SELECT customer_name
FROM customers
WHERE customer_id IN (
SELECT customer_id
FROM orders
WHERE order_date > DATEADD(month, -1, GETDATE())
);

-- Select all orders with a total amount greater than the average order amount
SELECT order_id, total_amount
FROM orders
WHERE total_amount > (
SELECT AVG(total_amount)
FROM orders
);

1. Conditional Statements SQL offers many conditional statements that perform conditional calculations or manipulation of data. These conditions are IF, CASE, and COALESCE. For example;

-- Create a new column that indicates whether an order is a large order or a small order
SELECT order_id, total_amount,
CASE
WHEN total_amount > 1000 THEN 'Large Order'
ELSE 'Small Order'
END AS order_size
FROM orders;

-- Replace null values in a column with a default value
SELECT order_id, COALESCE(order_notes, 'No notes') AS order_notes
FROM orders;

1. Data cleansing SQL is used to clean and manipulate data using techniques such as trimming, filtering, and replacing data. For example;

-- Remove leading and trailing spaces from a column
SELECT TRIM(customer_name) FROM customers;

-- Filter out orders with a total amount of less than 10
SELECT * FROM orders WHERE total_amount >= 10;

-- Replace null values in a column with a specific value
SELECT order_id, REPLACE(ISNULL(order_notes, ''), 'N/A', 'No notes available') AS order_notes
FROM orders;

These are just a few techniques that an analyst can use to effectively analyze data to derive. useful insights from it.

Models Used in SQL for Data Analysis

To effectively derive insights from data, SQL models are used to structure data for efficient querying and perform calculations and aggregations. These models are;

• Relational Model: The relational model is the foundation of SQL, and it represents data as a set of tables with rows and columns. The tables are related to each other by key fields, and SQL can be used to join tables, filter data, and perform aggregations.
• Dimensional Model: The dimensional model is a specialized data model used in data warehousing. It represents data as facts and dimensions, with facts representing the measurable data (such as sales or revenue) and dimensions representing the categories or attributes that define the facts (such as time or product). SQL can be used to build and query dimensional models.
• OLAP (Online Analytical Processing) Model: The OLAP model is a data model used for multidimensional analysis, such as pivot tables or data cubes. It represents data as dimensions and measures, and SQL can be used to build and query OLAP models.
• Regression Model: Regression is a statistical model used to identify the relationship between one or more independent variables and a dependent variable. SQL can be used to build regression models, which can help to predict future outcomes based on historical data.
• Time Series Model: Time series analysis is a statistical technique used to analyze data that varies over time. SQL can be used to build time series models, which can help to identify patterns and trends in time-based data.
• Text Mining Model: Text mining is a process of extracting useful information from unstructured text data. SQL can be used to build text mining models, which can help to analyze text data and extract insights, such as sentiment analysis or topic modeling.

Importance of SQL in Data Analysis

SQL plays a very vital role in data analysis by providing a robust and standardized set of tools for retrieving, transforming, and summarizing data in relational databases. These roles are;

1. Data retrieval: SQL is used to retrieve data from databases. Analysts can use SQL to write queries that extract specific data from a database, which can then be analyzed and visualized using other tools.
2. Data transformation: SQL can be used to transform data, such as filtering, grouping, and aggregating data, to prepare it for analysis. SQL's capabilities for data transformation are essential for data cleaning and preparation, which is a crucial step in the data analysis process.
3. Data aggregation and summarization: SQL provides several functions for aggregating and summarizing data, such as COUNT, SUM, AVG, MAX, and MIN. These functions are essential for summarizing and understanding the characteristics of large datasets.
4. Joining multiple tables: SQL provides powerful join capabilities that enable analysts to combine data from multiple tables. Joining tables is a critical step in data analysis, especially for large datasets.
5. Data visualization: SQL can be used to retrieve and summarize data, which can then be visualized using other tools. Data analysts can use SQL to create the underlying data for charts, graphs, and other visualizations.

6. Data modeling: SQL can be used to create and manage data models, which define the structure and relationships of data in a database. Data modeling is an essential step in designing databases that are optimized for data analysis.
   From these roles, a list of use cases can be derived. Some real-world examples of how SQL in data analysis can be used are;

7. E-commerce analysis: E-commerce businesses use SQL to analyze customer behavior, such as purchase history, shopping cart behavior, and website navigation. This information is used to optimize the user experience, recommend products, and personalize marketing messages. For example, an e-commerce company may use SQL to analyze shopping cart data and identify the most commonly abandoned items, allowing them to adjust pricing or shipping costs to reduce cart abandonment rates.

8. Financial analysis: Financial institutions use SQL to analyze customer transactions, such as deposit and withdrawal history, credit card usage, and loan payment behavior. This information is used to identify potential fraud, assess credit risk, and optimize lending decisions. For example, a bank may use SQL to analyze customer transaction history and identify patterns of suspicious behavior, such as unusual transactions or high-risk purchases.

9. Healthcare analysis: Healthcare organizations use SQL to analyze patient data, such as medical history, treatment outcomes, and healthcare utilization. This information is used to improve patient care, optimize healthcare delivery, and manage costs. For example, a hospital may use SQL to analyze patient outcomes for a particular treatment, allowing them to adjust treatment protocols to improve patient outcomes and reduce costs.

10. Marketing analysis: Marketing teams use SQL to analyze customer demographics, behavior, and preferences. This information is used to optimize marketing campaigns, personalize messaging, and improve customer retention. For example, a marketing team may use SQL to analyze customer purchase history and identify patterns in product preferences, allowing them to create targeted campaigns and promotions for specific customer segments.

11. Supply chain analysis: Supply chain companies use SQL to analyze inventory levels, logistics data, and shipping history. This information is used to optimize operations, reduce costs, and improve delivery times. For example, a logistics company may use SQL to analyze shipping data and identify patterns in delivery times, allowing them to adjust routes and schedules to improve efficiency and reduce costs.
    Merits and Demerits of using SQL for data analysis
    While SQL is a very powerful tool for data analysis, it has its strengths and weaknesses. How effective SQL depends on how well it aligns with the needs of a company.

Merits:

1. Speed: SQL is a fast and efficient language for retrieving, transforming, and summarizing data, and it can quickly process large datasets.
2. Standardization: SQL is a standard language used across many different relational database management systems, which makes it easy to learn and widely applicable.
3. Data Integration: SQL provides powerful join capabilities that allow analysts to combine data from multiple tables or even multiple databases. This makes it easier to integrate and analyze data from multiple sources.
4. Data Transformation: SQL provides a range of functions for data transformation, which can be used to clean and prepare data for analysis. These functions make it easier to standardize data and prepare it for analysis.
5. Security: SQL provides robust security features, including user authentication and access control, which help to protect sensitive data from unauthorized access.

Demerits:

1. Limited to Relational Databases: SQL is designed to work with relational databases and cannot be used to analyze data stored in other types of databases or data sources.
2. Limited Functionality: Although SQL provides a wide range of functions for data manipulation, it may not have all the functionality required for complex data analysis.
3. Complexity: SQL can be a complex language to learn, especially for those who are new to programming or have limited experience working with databases.
4. Maintenance: Maintaining a database and ensuring that it is up to date can be time-consuming and resource-intensive.
5. Requires Technical Expertise: To use SQL effectively, analysts require technical expertise in database design, data modeling, and SQL query writing.

Conclusion
SQL is a powerful tool for a data analyst, its merits outweigh its demerits.

References

• SQL for Data Analysis by Cathy Tanimura
• Introducing SQL: A Foundation of Data Analytics by Robb Sombach, University of Alberta, Alberta School of Business