Data Mining vs Data Science: The Key Differences for Data Analysts

When choosing between two very similar fields, it can be difficult to determine which one is best for you. In the case of data mining vs data science, it can be difficult to even find the differences between the two. In such similar fields, making a decision becomes even trickier than it already was.

In this guide, you will learn what exactly data mining and data science are, as well as some of the biggest differences between the two. Hopefully, after learning all of this important information, you will have an easier time deciding between the two fields.

What Is Data Mining?

Data mining is the term coined for the process of converting raw data into useful and more easily understandable information. Many companies use data mining to find and analyze patterns within their marketing, revenue, expenditure, and sales data. This type of information is then used to make important decisions regarding marketing strategies and financial management.

The data mining process begins with companies collecting data and loading it into data warehouses, where it is stored and managed. Oftentimes, it is uploaded to a cloud for storage as well. Then, business analysts are brought in to study the data and decide on the best way to organize and display it. This information is conveyed to software designed to sort the data, and it is then sorted and displayed in some sort of chart, graph, or table.

What Is Data Science?

Data science is the study of data. Data science is a bit more complex than data mining, as it encapsulates programming skills, mathematical knowledge, statistics, and domain expertise. These fields all work together to help data scientists pull useful information and insights from sets of data.

Data scientists do quite a bit of work to find these useful insights. Typically, they are tasked with finding the right data sets and variables to study, collecting both structured and unstructured data, analyzing and interpreting data, and explaining their findings to stakeholders in ways that are easy to understand.

The Key Differences Between Data Mining and Data Science

What They Are

The biggest difference between data mining and data science is simply what they are. While data science is a broad field of science, data mining is only a technique used in the field. This means data science encompasses a vaster range of studies and techniques, while data mining focuses solely on collecting and converting data through one process.

Focus

Data mining is typically used as part of a business analyzing process. Generally, you will not see data mining being used outside of a business setting, as it is designed specifically to help businesses collect and understand their data. Data science, on the other hand, is a scientific study. Data scientists use this study to create predictive models, perform experiments, and perform social analyses, among other things.

Goal

The primary goal of data mining is to make a business’s data easily understandable and therefore, usable. Data science has the goal of making scientific advances and creating data-centric products for various organizations to use. Overall, data mining has a much more specific goal than data science.

Professionals in the Field

In data mining, professionals are only expected to understand how to harvest, organize, understand, and accurately portray data. Data scientists, on the other hand, are expected to be at least somewhat skilled in many different areas along the lines of AI research, data engineering, data analysis, programming, and domain knowledge. To use data mining, you will need to possess some of the knowledge and skills data scientists have, but not nearly as many.

Type of Data

Typically, data mining focuses on structured data only, though it is possible for unstructured data to be used as well. For data scientists, it is a normal day’s work to use structured data, unstructured data, and semi-structured data. Data mining is a bit simpler in this aspect, as professionals are not expected to know how to work with all types of data, while data scientists will most certainly have to have knowledge of all different types.

Purpose

The entire purpose of data mining is to look at and organize a company’s data and discover previously unknown trends within it. For data science, the purpose is broader, as it is meant to discover unknown facts about data, build predictive models, conduct social analyses, and so much more. Data mining has a much more specific purpose than data science.

Should You Work in Data Analytics Through Data Mining or Data Science?

Both data mining and data science are valuable and interesting fields to work in. Choosing which one is best for you comes down to personal preference and what your desired work goals and purposes are. When determining which you want to go into, consider how narrow or broad you want your field of work to be, what type of work you wish to do, and how long you want to spend studying.

If you would prefer working in a field with a more specific goal and purpose, and you do not wish to study for a very long time, data mining may be the better option for you. You would have the set goal and purpose of collecting, organizing, and presenting data in order to find patterns within it. Working in data mining can be done as a business analyst or contracted worker, and you would not have to spend years studying to become a scientist. 

On the other hand, if you want a less specific goal and purpose, and you don’t mind studying data science for longer, it can be a very rewarding field. Being able to perform experiments simply to find unknown facts while working toward the goal of creating data-centric products allows for more creativity in the job, and there are many interesting topics you will study.

Whichever you choose to go into, be sure to research your desired position thoroughly before beginning your education. Different companies have different requirements to meet, and you will want to be sure you can check all of the boxes.