It seems like there is data everywhere, and it keeps increasing with every passing second. The IBM estimate pegged daily information generation at 2.5 billion gigabytes in 2012. People who are able to study and interpret data have the potential to transform the fundamental concepts underlying individual-enterprise interactions.
For this reason, it has become crucial for everyone to understand the terminology of data mining and data science. There are profound differences between the meanings of data Science and data mining.
Data Science is a field that utilizes technology and mathematics to find patterns within the massive volumes of raw data that we generate on a daily basis. A multidiscipline field, Data Science uses data to analyze qualitatively. With its aim of making smart decisions and accurate predictions, data science allows us to discover otherwise non-existent insights in those troves of data. Behavioral science, language processing, data visualization, data mining, statistics, and unstructured data are all components of Data Science.
Data mining is a subdivision of data science. The term “data mining” describes a variety of techniques within data science designed to extract information from a database that would not otherwise be visible or accessible. It is one of several steps in “knowledge discovery in databases” or KDD. It refers to the process of digging for something valuable within databases through the process of data mining. In addition to the above, data mining also includes steps such as data cleansing, statistical analyses, pattern recognition, as well as machine learning, data visualization, and data transformation.
Major differences between Data Science and Data Mining
- The term Data science has been around longer than data mining. Thus we need to understand the difference in terminology. Data science is a discipline that encompasses the capture, analysis, and interpretation of data. In contrast, data mining is a process/technique used to discover hidden patterns in a dataset.
- In addition, data science and data mining differ greatly in their disciplinary content; the former includes statistics, social sciences, data visualization, natural language processing, and data mining, while the latter is a subset of the former.
- Furthermore, data mining and data science have very different approaches to the types of data they use. A data scientist usually works with all kinds of data, whether structured, semi-structured, or unstructured. In contrast, data mining mainly involves structured data.
- A data scientist is mainly concerned with data science, whereas a data mining expert is primarily concerned with the process.
- Data science aims to build data-driven products for companies. On the other hand, Data mining aims to make data more vital and valuable, i.e., focusing on identifying only the important information within a data set.
- For the most part, data science is used for scientific research. Whereas Data mining primarily serves business needs.
- Tools used in data science include SAS, Apache Spark, Python, TensorFlow, etc. Tools used in Data Mining include Weka, RapidMiner, KNime, TeraData, etc.
A crucial point to keep in mind is that Data Science and Data Mining have no formal, precise definitions. While they do differ in many aspects, one massive and common link between the two is data.
However, the question is, do you want to clean up the huge structured data provided to you, extract what you can, analyze it, summarize it and visualize it as a scientist, or do you want to simply experience the thrill of finding anomalies and correlations in this enormous data set? Whatever your answer is, after reading the article you will know which way you have to go.