What is a Data Mining?
Data mining is the process of uncovering patterns and finding anomalies and relationships in large datasets that can be used to make predictions about future trends. The main purpose of data mining is extracting valuable information from available data.
Data mining is considered an interdisciplinary field that joins the techniques of computer science and statistics. Note that the term “data mining” is a misnomer. It is primarily concerned with discovering patterns and anomalies within datasets, but it is not related to the extraction of the data itself.
Applications of Data Mining
Data mining offers many applications in business. For example, the establishment of proper data (mining) processes can help a company to decrease its costs, increase revenues, or derive insights from the behavior and practices of its customers. Certainly, it plays a vital role in the business decision-making process nowadays.
Data mining is also actively utilized in finance. For instance, relevant techniques allow users to determine and assess the factors that influence the price fluctuations of financial securities.
The field is rapidly evolving. New data emerges at enormously fast speeds while technological advancements allow for more efficient ways to solve existing problems. In addition, developments in the areas of artificial intelligence and machine learning provide new paths to the precision and efficiency in the field.
Data Mining Process
Generally, the process can be divided into the following steps:
- Define the problem: Determine the scope of the business problem and objectives of the data exploration project.
- Explore the data: The step includes the exploration and collection of data that will help solve the stated business problem.
- Prepare the data: Clean and organize collected data to prepare it for the further modeling procedures.
- Modeling: Create the model using data mining techniques that will help solve the stated problem.
- Interpretation and evaluation of results: Draw conclusions from the data model and assess its validity. Translate the results into a business decision.
Data Mining Techniques
The most commonly used techniques in the field include:
- Detection of anomalies: Identifying unusual values in a dataset.
- Dependency modeling: Discovering existing relationships within a dataset. It frequently involves regression analysis.
- Clustering: Identifying structures (clusters) in unstructured data.
- Classification: Generalizing the known structure and applying it to the data.
CFI offers the Financial Modeling & Valuation Analyst (FMVA)™ certification program for those looking to take their careers to the next level. To keep learning and advancing your career, the following resources will be helpful: