Artificial intelligence and machine learning are changing how we think and act in this age of innovation. Technological advancements control our daily life as well as businesses.
Businesses use data mining to comprehend large data to help make significant decisions. It helps streamline operations, increase ROI and predict sales forecasts accurately. A data science and machine learning course can help interested candidates explore the potential of data mining and learn to harness it.
Let’s take a walk through the key definition of data mining and its uses in the industry.
What is data mining?
In data mining, a large set of data is sorted to point out crucial patterns aiding in the decision-making process of a business. It is an integral part of data analysis. It turns unstructured, raw data into insightful ones that an organisation can use for marketing, sales and other crucial areas.
Some of the industries that use data mining are:
- Retail industries for marketing mostly
- Banking and financial services to detect fraud
- Insurance sector for pricing policies
- Streaming services for watching or listening patterns
- And many more
What are the advantages of data mining?
Data mining can assist businesses in earning profits through insightful information. Some of the benefits of data mining are:
● Effective in finding data
With the help of data mining, gathering required information is far easier. It also helps in extracting useful information from a pool of data.
● Faster in making decisions
Data mining automates decision-making, reducing the time frame significantly. Sometimes software can complete a whole process without the need for human intervention.
● Efficient
Data mining is efficient in finding out the information required. Also, it can work with new systems as well as older ones.
● Improved customer service
Gathering customer data from various sources becomes easy with data mining. It provides valuable information on customer behaviour, preferences and much more. These pieces of information can help improve customer service.
● Increased ROI
Data mining is more cost-effective compared to other data applications. It can help predict marketing trends, thus helping create accurate audience segments and launching tailor-made promotions. This will, in turn, lead to higher revenue.
What are the different steps in the data mining procedure?
1. Understanding business
The first step in data mining is to understand what are the project's goals, the company's existing status and what constitutes its achievements.
2. Understanding data
In this step, different data sets go through several checks to ensure their appropriateness. For example, if revenue is the goal of data mining, then the number of customers is one of the crucial sets of information.
Also, various data integration procedures ensure minimal errors in the process. A search for the properties of all the data acquired is also a part of this step.
3. Preparing data
This stage requires a great deal of patience and time. It includes data clean-up, removing duplicate data and sometimes finding missing data.
For example, if the data prepared is on increasing revenue, then the age of the customers is a crucial factor. If the value of some of these data is missing, finding the missing value, i.e. the age of customers, is essential.
4. Transforming data
The next step is transforming the prepared data into a more usable one. It includes multiple processes like data smoothing, aggregation, generalisation and more.
Data aggregation is a procedure that compiles data. For example, while working on a set of data on revenue, compiling weekly sales data to calculate monthly sales is of the essence.
5. Modelling
In this stage, mathematical algorithms, artificial intelligence and machine learning are used to determine, categorise and cluster data. In-depth knowledge of machine learning with Python is crucial in this stage.
6. Evaluation
The next step concerns evaluation. The identified data patterns acquired after data modelling go through an evaluation procedure to meet the objectives set by the business. If the model fails to meet the set goals, it will require re-modelling.
7. Deployment
The last stage involves presenting the final data to the stakeholders of the business in an easily understandable manner.
And finally, the preparation of a project report is crucial as it will help in further decision-making.
Conclusion
If you want to learn data mining, consider a career in data science. As expertise in data science is in demand among employers, you can land lucrative job offers.
Check out the IIT data science course brought to you by Imarticus and created in collaboration with IIT Roorkee. The programme also covers machine learning and offers mentorship to prospective entrepreneurs.
