A Comprehensive Guide of SQL for Data Analysis

Structured Query Language (SQL) has been around for decades. It is a programming language used for managing the data held in relational databases. SQL is used all around the world by a majority of big companies. A data analyst can use SQL to access, read, manipulate, and analyze the data stored in a database and generate useful insights to drive an informed decision-making process. SQL is a powerful programming language that helps data analysts interact with data stored in Relational databases.

This article provides you with a comprehensive overview of the importance of SQL for Data Analysis. It also explains the types of Data Analysis and the potential of SQL queries with databases, along with the limitations it possesses.

Data Analysis

Data analysis is a process of inspecting, cleansing, transforming, and modeling data to discover useful information, informing conclusions, and support decision-making. To enhance customer satisfaction, organizations use data analytics to improve their products and services. The process of Data Analysis involves collecting and organizing big data to extract useful information, as it helps in making critical decisions to prosper in business.

A simple example of data analysis can be seen whenever we decide on our daily lives by evaluating what has happened in the past or what will happen if we make that decision. This is the process of analyzing the past or future and making a decision based on that analysis.

Types of Data Analysis

Four types of data analysis that are in use across all industries to improve decision making.

• Descriptive Analysis

• Diagnostic Analysis

• Predictive Analysis

• Prescriptive Analysis

Descriptive Analysis: Descriptive Analysis is the simplest type of analytics and the foundation on which the other types are built. It allows you to pull trends from raw data and describe what happened or is currently happening. It is the simplest and most common use of data in business today. Descriptive analysis answers the "what happened" by summarizing past data, usually in the form of dashboards.

Diagnostic Analysis: After asking the main question of "what happened", the next step is to dive deeper and ask "why did it happen"? This is where diagnostic analysis comes in. The diagnostic analysis takes the insights found from descriptive analytics and drills down to find the causes of those outcomes. Organizations make use of this type of analytics as it creates more connections between data and identifies patterns of behavior.

A critical aspect of the diagnostic analysis is creating detailed information. When new problems arise, you may have already collected certain data related to that issue. By already having the data, it becomes easy and fasts to solve the problem without reworking.

Predictive Analytics:This type of analysis is another step up from descriptive and diagnostic analyses.PredictiveAnalysisis used to make predictions about future trends or events and answers the question, “What might happen in the future?” By analyzing historical data in tandem with industry trends, we can make informed predictions about what the future could hold for the company.

Prescriptive Analytics: Prescriptive Analytics answers the question, “What should we do next?” The Prescriptive analysis combines insights from the above three analyses to formulate a plan of action for the organization. With Prescriptive analysis, organizations can address the possible issues with ML models that are trained with prior knowledge of strategies. While manual prescriptive analysis is doable and accessible, machine-learning algorithms are often employed to help parse through large volumes of data to recommend the optimal next step.

Benefits of SQL for Data Analysis

• SQL for Data Analysis is easy to understand and learn, thereby making it a user-friendly language.

• SQL for Data Analysis is efficient at fast query processing and helps in retrieving big data from multiple databases efficiently.

• SQL for Data Analysis supports exceptional handling as it provides standard documentation to users.

• SQL syntax used by most of the databases is standardized, we don’t need to learn a language specific to each database. We can learn SQL once and then make small adjustments as we work with multiple databases.

Understanding SQL for Data Analysis

• Group By Clause: The GROUP BY clause is used to get the summary data based on one or more groups. The groups can be formed on one or more columns. For example, the GROUP BY query will be used to count the number of employees in each department or to get the department-wise total salaries.

• Aggregation Functions: An aggregate function performs a calculation on a set of values, and returns a single value. Except for COUNT(*) , aggregate functions ignore null values. Aggregate functions are often used with the GROUP BY clause of the SELECT statement.

• String Functions and Operations: The string operators in SQL are used to perform important operations such as pattern matching, concatenation, converting string to uppercase, matching a regular expression, etc.

• Date and Time operations: SQL Server provides several types of date and time functions such as SYSUTCDATETIME(), CURRENT_TIMESTAMP, GETDATE(), DAY(), MONTH(), YEAR(), DATEFROMPARTS (), DATETIME2FROMPARTS(), TIMEFROMPARTS (), DATEDIFF(), DATEADD(), ISDATE(), etc. These functions are used to perform operations on date and time input.

• Joins: The SQL join clause is used to combine different tables in databases, where JOIN is made using a Primary and Foreign key. There are four major joins which include inner, left, right, and full join used in combination with the ‘from’ clause.

• Nested Queries: A nested query in SQL contains a query inside another query. The result of the inner query will be used by the outer query.

• Views and Indexing: Indexes are great because they speed up the performance and with an index on a view it should really speed up the performance because the index is stored in the database. Indexing both views and tables is one of the most efficient ways to improve the performance of queries and applications using them.

• Temporary Tables: It is a great feature that lets you store and process intermediate results by using the same selection, update, and join capabilities.

• Windowing Functions: Window functions operate on a set of rows and return a single value for each row from the underlying query. They reduce the complexity of queries.

Different tools Used to Perform Data Analysis

Microsoft Excel: Microsoft Excel is the world’s best-known spreadsheet software. It features calculations and graphing functions that are ideal for data analysis.

Python: A programming language with a wide range of uses, Python is a must-have for any data analyst. Unlike more complex languages, it focuses on readability, and its general popularity in the tech field means many programmers are already familiar with it. Python is also extremely versatile; it has a huge range of resource libraries suited to a variety of different data analytics tasks.

R: R, like Python, is a popular open-source programming language. It is commonly used to create statistical/data analysis software. R’s syntax is more complex than Python and the learning curve is steeper.

Tableau: Tableau is one of the best commercial data analysis tools available to create interactive visualizations and dashboards without extensive coding expertise. The suite handles large amounts of data better than many other BI tools, and it is very simple to use.

SQL: It is the standard programming language used to communicate with Relational databases. SQL helps in retrieving the required information through simple queries and helps in decision-making.

Conclusion

The article discusses the importance of SQL for data analysis. The article provides an overview of SQL and how it facilitates the analysis of data in business processes. Thanks for reading.