Structured Query Language (SQL) is a programming language used to manage and manipulate relational databases. SQL is used by data analysts and data scientists for extracting, transforming, and analyzing data stored in databases. In this blog, we will discuss the most commonly used SQL functions that are used in real-world problems and can help solve up to 80% of the work required in data analysis. SELECT: SELECT is the most frequently used SQL function. It is used to retrieve data from one or more tables in a database. This function allows you to select specific columns, rows, or a combination of both from a table. The syntax for the SELECT statement is: SELECT column_name(s) FROM table_name WHERE: The WHERE function is used to filter data from a table based on a specific condition. It is used in combination with the SELECT function to retrieve specific data. The syntax for the WHERE statement is: SELECT column_name(s) FROM table_name WHERE condition GROUP BY: The GROUP BY function is ...

Ad hoc analysis is a type of data analysis that is done on an as-needed basis. It is often performed in response to a stakeholder's sudden request for information. It allows stakeholders to quickly obtain insights and make data-driven decisions based on current information. It is flexible and can be performed using various tools, depending on the data and the user's requirements Unlike traditional reporting methods, ad hoc analysis is flexible and dynamic, allowing analysts to quickly pivot and change their analysis as new questions arise or new data becomes available. This enables businesses to gain insights and make data-driven decisions in real time, helping them stay ahead of the competition and adapt to changing market conditions. In this article, we will explore what ad hoc analysis is, its benefits, and how it can help businesses make better decisions. What is Ad Hoc Analysis and Reporting? Ad hoc analysis is a type of business intelligence process that involves explorin...

Cross-tabulation analysis is also called contingency table analysis. It is a statistical method used to study the relationship between two categorical variables. This method helps us to determine if there is a significant association between the two variables and if so, the strength and direction of that association. In this post, we'll go over the basics of cross tabulation analysis, including how to create a contingency table, calculate expected frequencies, and interpret the results. Subtopics Covered What is Cross-Tabulation Analysis? Creating a Contingency Table Analyzing the Data What are the expected frequencies? Interpreting the Results Cross Tabulation using Pandas What is Cross-Tabulation Analysis? Cross-tabulation analysis is a statistical technique that helps us to understand the relationship between two categorical variables. In simpler terms, it helps us understand how two different categories might be related to each other. Categorical variables are variables that ta...