One of the most talked-about topics within the world of science and technology is the use of automation and machine learning. Within the field of data science, machine learning has the potential to make many of the most complex and difficult tasks both easier and faster through programming a computer or system to perform repetitive tasks. While it is common to use languages such as Python and R for machine learning, the SQL programming language is also useful for automating tasks. Especially when working with database management systems, machine learning can help simplify and streamline the process of data cleaning and organization as well as the analysis and visualization of a dataset. Any data science student or professional can benefit from using SQL for machine learning!
Machine Learning and the SQL Programming Language
Machine learning is defined as a way of using algorithms to analyze and gather knowledge from individuals and/or a system. As a subset of artificial intelligence (AI), machine learning, and the algorithms and models it relies on, can be used to make predictions, create recommendations, or even to replicate tasks that are usually done by humans. Through the design and deployment of machine learning models, data scientists are able to automate many of the tasks that need to be performed within the process of collecting, cleaning, and analyzing data. But despite the fact that SQL is one of the more popular languages in data science, it is not always the first programming language that comes to mind when thinking about machine learning.
The realms of machine learning, automation, and artificial intelligence offer resources for data scientists that are working in programming languages like R and Python. When it comes to the SQL programming language, there is significantly less instruction on using SQL for machine learning. The primary reason for this is that SQL is defined as a querying language, therefore it is known for searching, managing, and communicating with a database. However, as machine learning has become an even more important part of data science and database design, more database management systems now include features and functions to automate and deploy machine learning models.
Using Machine Learning in SQL Databases
Machine learning is commonly employed within SQL databases for the purposes of automating tasks and working with business intelligence tools. Data science professionals can use the SQL programming language in conjunction with machine learning models for data cleaning, analysis, visualizations, and the training and deployment of machine learning models. In discussing each of these uses, there are also specific examples of how you can use machine learning and SQL within specific database management systems.
Data Cleaning, Collection, and Cleansing
One of the most tedious processes in the data science life cycle is the collection and organization of information and data. This process is especially time-consuming because most of the labor of organizing a dataset includes repetitive, or more manual, tasks. But, when working with machine learning models and algorithms, data science professionals are able to speed up the data collection and cleaning process by handing over certain tasks that would usually be done by the data scientist.
For example, instead of going through a dataset and searching for missing values or incorrect metadata, machine learning models can be used to parse through and identify outliers in the dataset that need to be removed. In some SQL databases, like SQL Server, there are also computer-assisted data cleansing features that clean data through intuitive programming features which can pick up on inconsistencies within the dataset.
Exploratory Data Analysis and Data Mining
Once a certain amount of data has been collected and cleaned, it is ready for analysis, and machine learning models are especially useful in the process of exploratory data analysis (EDA). As a step that comes before, or in the beginning stages of data analysis, exploratory data analysis is a method of running statistical models on a dataset in order to identify patterns and emerging trends that might be useful. Exploratory data analysis can also be used as a check on the data cleaning process by drawing out any inconsistencies that might still be present in the database and therefore having an effect on the analyses being returned.
When using a database system such as Oracle MySQL, allow data scientists to explore the database through incorporating machine learning into the process of data mining. Similar to EDA, data mining is also focused on identifying patterns and trends in a dataset, and within MySQL, this can be accomplished through Oracle Machine Learning, which offers features for beginners and more advanced data science professionals to incorporate more automation and efficiency into their database management system.
Data Visualization and Predictive Analytics
Following the exploration and analysis of a dataset, you can also use machine learning to visualize the dataset through machine learning models or predictive analytics. Within data science, visualizing machine learning models helps to ensure that an audience can understand how a model was programmed to make decisions, as well as the learning process. The visualizations work well with predictive analytics, as researchers and stakeholders can also use these charts as a visual analysis or representation of the predictions made by the algorithm.
In order to perform a data visualization with SQL there are multiple data visualization tools that are compatible with SQL databases. Tableau and Microsoft Power BI can both be used to turn SQL queries into aesthetically pleasing visualizations. Tableau includes its own AI Analytics which can be used to analyze and visualize a dataset, especially for businesses and individuals that need to model trends and forecasts.
Training and Deployment of Machine Learning Models
For databases that have additional tools that can be used in conjunction with the primary database management system, machine learning models can also be trained and deployed within a database using SQL. For example, products such as Microsoft SQL Server include several features which are useful for model deployment through its business intelligence tools as well as the SQL Server Machine Learning Services. Additionally, the SQL Server Machine Learning Services can be used to incorporate both R and Python into a SQL database management system. By incorporating these other programming languages, data scientists can also gain access to the machine learning libraries that are compatible with these languages.
Want to learn more about using SQL for Machine Learning?
In using SQL for machine learning, there are several data science tools that aid in the preparation of a dataset and the design and management of database systems. SQL databases employ machine learning models and tools to organize, analyze, and visualize datasets. Data science students and professionals that are interested in learning how to use SQL for machine learning can take one of Noble Desktop’s SQL courses or bootcamps to expand their knowledge in this query-based programming language.
Under the heading of SQL courses, bootcamps like the SQL Server Bootcamp not only incorporate instruction in the SQL programming language and relational databases but also how to automate tasks when working within a dataset. In addition to the SQL courses and bootcamps, Noble Desktop also administers certificate programs that teach the basics of machine learning and automation. The Data Science Certificate incorporates hands-on experience in creating and deploying machine learning models, with a focus on querying databases using SQL.