If you’re considering a career in data science, chances are you’ve come across the term ‘data mining’ at least a couple of times. For some, learning how to become a data scientist is a step toward becoming a data mining analyst. In addition to being a fascinating role, it holds enormous potential for driving a business forwards; thus, a mining project should never be taken for granted.
What is data mining in simple terms?
In its simplest form, data mining involves taking a set of data and pulling out patterns, anomalies, or correlations and using these to make predictions. Data mining projects include studying the relationship between data points and using AI and machine learning to create predictive models. Every type of business can benefit from data mining.
What is the main goal of data mining?
The main goal of data mining is to try to stay one step ahead at all times. Data is utterly useless if you don’t know what to do with it. Even worse, if misinterpreted, it can be dangerous to a company, which is why data sets should always be clean and approach methodical.
If you know how to find and harness the little nuggets of information from reams of data, you can make educated predictions (with the help of machine learning and AI) and even run models to see how company decisions could impact the business.
What are the benefits of data mining?
A business will ask an analyst to carry out data mining tasks for a myriad of reasons. These could be finding ways to:
- Cut costs
- Reduce risks
- Detect fraud
- Maintain a competitive advantage
- Increase revenue
- Attract new customers and tap into new markets
- Resolve customer experience issues
What is needed for data mining?
The tools you use to carry out your day-to-day tasks will vary depending on the company you work for and their preferences. Therefore, familiarising yourself with a variety of programming software and data analysis tools will increase your employability. That includes knowing how to use:
How is data mining done?
The most basic form of data mining includes pre-processing, data mining, and results validation. There are other variations, but you’ll be off to a great start if you know how to do these steps.
- Pre-processing – Firstly, you must ascertain the data sweet-spot; that is, have enough data that patterns can be teased out, but not so much that the time-cost associated with mining outweighs the benefits. Your data must also be cleaned to ensure your predictions are accurate.
- Data mining – The actual mining process can be broken down into six steps:
- Investigating anomalies in the data set
- Implementing association rule learning to source relationships between variables
- Identifying data structures or groups with similarities and clustering them together
- Applying new data to known structures
- Experimenting with models to estimate relationships between data sets
- Representing the data using visualisations and generating relevant reports
- Results validation – Once your data is mined, you can test any algorithm you build on additional data sets to ensure it makes correct predictions. If successful, the program can be run, and patterns analysed and used to guide business decisions. If the testing is unsuccessful, tweaks can be made until the desired outcome is achieved.
If you are interested in a career in data mining, we recommend studying data science as a starting point. From here, you can find employment opportunities in the field and continue your training, either as part of your professional development through your company or as part of your private study.