In this era of big data, data science tools and analytics grow apace with the increasing volumes of data collection. Consequently, a growing need exists for data scientists and analysts who can work with machines to translate data into actionable insights. Data mining is one of many methods used to clean, organize, and decipher data for collection, storage, analysis, or visualization. Data mining opportunities for beginner data scientists are plentiful, with most geared toward research, marketing, business, and investing.

What is Data Mining?

Any type of mining process, e.g., for coal or gold, involves digging through a lot of extraneous material to find bits of treasure. Data mining is no different, only you’re digging through data instead of earth to find usable information, patterns, trends, inconsistencies, etc. In the past, data mining required data science professionals to go through the painstaking process of searching for these patterns manually or with relatively unsophisticated data collection and storage techniques. But today, the use of automation and machine learning applies data science tools and artificial intelligence software to the data mining process.

These data science tools include machine learning models, data science libraries, database management systems, business intelligence tools, and other data analytics technologies. By identifying patterns and trends in a dataset, data scientists can also use these tools for predictive analytics, i.e., to make predictions about future trends using past data. For example, a database management tool like Microsoft SQL Server can be paired with a business intelligence tool like Microsoft Power BI to learn more about a dataset. These data mining tools clean data, identify metadata, and organize datasets, among other tasks. Data mining has several methods and applications within and outside the data science industry.

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Introduction to Data Mining Methods

The form of data mining varies depending on the data type and data science life cycle stage. Generally, data mining is applied in the initial data collection, storage, and analysis stages. For example, in the collection and storage stage, data mining includes using an algorithm, artificial intelligence, database management, or organizational tool to find patterns in the dataset. During these initial stages, data mining finds anomalies in the dataset that are either removed manually by the data scientist or automatically by the data science tool.

Different methods of collecting and storing data must be done before data mining occurs. Data is categorized as either structured or unstructured, and each type of data requires different data mining methods. Data mining tends to be simpler for structured datasets as data scientists can use the SQL programming language and compatible relational databases to query a dataset and make discoveries. In comparison, unstructured data requires more complex data mining methods because this data is usually not numerical or quantitative. Data mining for unstructured data changes depending on the data type.

When using data mining for analysis, data scientists commonly use either statistical analysis or tools for predictive analytics. These tools rely on statistical formulas and theories to parse past data. Statistical models like linear regression or algorithmic models like decision trees are used to sort through data and make predictions. Data mining is also helpful in the final stages of presenting data through visualization tools. After discovering specific patterns or anomalies in a dataset, the process of data mining can be visualized through diagrams or models. For example, a data visualization that depicts a network of clusters can show the relationships and patterns discovered through data mining.

Typical Applications of Data Mining by Industry

Data science tools and data mining techniques respond to the needs of specific industries. Data mining turns raw business data into business analytics information and insights. It relies on business intelligence tools that use artificial intelligence to make data-informed decisions. Everyday business decisions based on data mining outcomes include determining the number of employees to hire during a busy season and the amount of product needed to meet consumer demand. 

In advertising and marketing, businesses use data mining to parse user and consumer data so they can develop targeted advertisements and campaigns. Advertising professionals use data mining essentially for research to discover the patterns behind consumer purchases, such as the relationship between advertisements and online engagement. Data mining in the finance industry analyzes historical data for patterns or changes in economic trends. For example, investors can mine financial data to track stocks on the rise or a potential collapse in a volatile market.

Data mining is also done in fields outside of business, marketing, and finance. In academic or governmental research, data mining can be used to analyze societal trends, such as demographics, politics, crime, and public health. Web designers and developers use data mining to analyze websites. For example, developers might use it to test for bugs or software/technology glitches, while web designers mine web traffic data and search criteria to improve the platform design and user experience.

Need to Learn More About Data Mining?

Data mining has uses and applications across industries and is an essential skill for all levels of data scientists and analysts. Noble Desktop’s data science classes offer students and professionals hands-on experience with data mining tools and techniques. In the Python Data Science and Machine Learning Bootcamp, students learn to clean and prepare data using the Python programming language. The Python for Automation course covers data mining for websites and teaches web scraping and data cleaning and organization process automation.