Data Mining vs Data Science: What’s the Difference?

Data Mining vs Data Science: Which is better? Whether data mining or data science is better depends on the purpose behind the process. A data scientist is more general in their approach, while a data miner can tailor their process depending on the purpose.

For example, if the objective of a data miner is to find commonalities among large data sets, a data scientist would work together with the data miner to come up with a solution to leverage that information in order to find hidden patterns.

Data Mining vs Data Science: Are they related? Data mining and data science are indeed related, but there are also many differences between them. The best way to learn more about their differences is to know both concepts in depth. Data Mining vs Data Science: What are their uses?

Data Mining Techniques

The basic process of data mining is to examine data and extract value from it using various extraction techniques. These techniques include: Filtering: Filtering is the technique of removing one or more unwanted parts of a dataset while preserving the most important parts.

Univariate Logistic Regression: You can use this technique to calculate the marginal value of each feature in the dataset and determine how much each feature should be included in the final analysis.

Multivariate Logistic Regression: You can use this technique to evaluate several independent variables or to evaluate the relationships between them. Regression Modeling: Regression modelling is the process of deriving conclusions based on the correlation of various variables in a dataset.

Data Mining Tools

Data mining is usually performed in large-scale data management systems such as Hadoop, Apache Spark, Pig, and Python, among others. So, before we get to tools that you can use, it’s worth mentioning that a data mining tool is a software tool that provides algorithms that can be used to run searches on a dataset.

Data mining tools are one of the tools that analysts can use in their jobs in big data environments. Here are some of the data mining tools that you can use, based on the languages that you are using to mine the data: Hadoop: Hadoop is a big-data processing framework that was designed to enable users to store, process, store in time, distribute, store in flat files, and cluster horizontally.

Conclusion

By mastering data science fundamentals in your product development and marketing process, you can learn how to use the right tools and technologies to discover insights in your data and derive key information to your customers to optimize their purchasing decisions and get better ROI. This article is published as part of the IDG Contributor Network. Want to Join?

Leave a Comment