From the course: MySQL for Advanced Analytics: Tips, Tricks, & Techniques

Why MySQL for advanced analytics? - MySQL Tutorial

From the course: MySQL for Advanced Analytics: Tips, Tricks, & Techniques

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Why MySQL for advanced analytics?

- [Narrator] MySQL is an RDBMS that is traditionally used for online transaction processing applications. But it can also be a great tool in the hands of a data analyst or data scientist. How can a data scientist exploit the capabilities of MySQL to perform his or her analytics? I will show you in this course. What do data scientists do with data? Usually, they are doing cleansing, transformation, and analytics work. Typically, they use programming languages like R and Python to build a code that will do this work. It is a time consuming effort to build code, a lot of which are one-time use only. MySQL surprisingly provides quick and easy solutions to achieve these objectives. MySQL is great working database for a data scientist. You can create a temporary database to perform ad hoc analytics work. MySQL is easy to set up and can quickly scale to. Then you can export external data into MySQL. This is usually the data subset that you are interested in analyzing. Once this is set up, you can perform ad hoc analysis work using SQL. Finally, you can either archive or delete the database when you are done with your analytics. What kind of data wrangling activities can you do with MySQL? You can do data cleansing work like removing unwanted records, populating empty attributes, etc. You can do standardization of names, data formats, and also rounding off for values. In data science, creating indicator variables is a key step and you can use MySQL to do that. You can do binning of data into categories. Finally, you can also do centering and scaling of data before it is used for machine learning. When it comes to doing analytics work, MySQL can help you with the following. Perform aggregations of data using SQL, compute descriptive statistics, like mean and standard deviation. Finding peaks or concurrent usage. You can also do exploratory data analytics like segmentation and profiling. And then MySQL also has excellent integration with Microsoft Excel. Using this capability you can import records and summaries from MySQL into Excel. You can perform pivoting work to segment and profile data. You can create graphics and charts readily. You can also update data from Excel into MySQL. In this course, I will demonstrate how to perform these data wrangling, analytics, and excel integration activities with MySQL.

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