Data Science is considered to be the most sought-after profession of the 21st century. With lucrative opportunities and large pay scales, this profession has been attracting IT professionals around the world. Various tools and techniques are used in Data science to handle data. This article talks about MySQL and how it is used in data science.
What is MySQL
In short words, MySQL is a Relational Database Management System or RDBMS that use Structured Query Language (SQL) to do so. MySQL is used for many applications, especially in web servers. Websites with pages that access data from databases use MySQL. These pages are known as “Dynamic Pages” since their contents are generated from the database as the page loads.
Using MySQL for Data Science
Data science requires data to be stored in an easily accessible and analyzable way. Even though there are various methods to store data, databases are considered to be the most convenient method for data science.
A database is a structured collection of data. It can contain anything from a simple shopping list to a huge chunk of data of a multinational corporation. In order to add, access and process the data stored in a database, we need a database management system. As mentioned MySQL is an open-source relational database management system with easier operations enabling us to carry out data analysis on a database.
We can use MySQL for collecting, Cleaning and visualizing the data. We will discuss how it is done.
1. Collecting the Data
The first part of any data science analysis is collecting the massive amount of data of data. The Sheer volume of data often causes some insights to be lost or overlooked. So, it is important to aggregate data from various sources to facilitate fruitful analysis. MySQL is capable of importing data to the database from various sources such as CSV, XLS, XML and many more. LOAD DATA INFILE and INTO TABLE are the statements mostly used for this purpose.
2. Clean the Tables
Once the data is loaded to the MySQL database, the cleaning process or correcting the inaccurate datasets can be done. Also deleting the dirty data is also part of this step. The dirty data are the incomplete or irrelevant parts of the data.
The following SQL functions can be used to clean the data.
- LIKE() – the simple pattern matching
- TRIM() – Removing the leading and trailing spaces.
- REPLACE() – To replace the specified string.
- CASE WHEN field is empty THEN xxx ELSE field END – To evaluate conditions and return value when the first one is met.
3. Analyze and visualize data
After the cleaning process, it is time to analyze and visualize the meaningful insights from the data. Using the standard SQL queries, you can find relevant answers to the specific questions.
Some analysis examples are given below:
- Using query with a DESC function, you can limit the results only to the top values.
- Display details of sales according to the country, gender or product.
- Calculate rates, evolution, growth and retention.
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