Data Mining vs. Data Science: Understanding the Differences

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The advent of digital technology has made amassing large sets of data, from financial trends to customer preferences, easier than ever. The field of big data analytics involves the collection and analysis of large swaths of information and has had a deep impact on a number of industries, including healthcare, business, and finance.

For those who have an interest in data analytics, a number of career paths are available. Two fields that allow professionals to leverage data on behalf of organizational outcomes include data mining and data science. Before settling on a career path, students and graduates alike benefit from reviewing the distinctions in data mining vs. data science and considering the benefits of a formal education in a data-related field.

A data scientist works on a desktop computer in a home office.

What Is Data Science?

Data science is a multidisciplinary field devoted to drawing actionable insights from large and evolving datasets. By aggregating and developing raw data into usable information, data science can yield important observations, trends, or forecasts regarding a specific field.

Typically, data science involves preparing data for analysis, which means cleansing, aggregating, and manipulating different data types so they can be more easily processed. That is, a dataset must be reviewed for any redundancies or errors, grouped and organized, and then converted into a format that facilitates use. The field also involves advanced data analysis. A data scientist’s job concludes with the presentation of actionable insights that are relevant to their business or organization, as gleaned from a large amount of data.

Data Science Methods

Data science relies on a number of different methods. Artificial intelligence, or AI, can assess huge datasets with both greater accuracy and efficiency than a human. Additionally, data scientists may develop algorithms or other analytic models to help analyze large batches of information, model trends, or spot inconsistencies.

Using Software in Data Science

Success in data science requires an advanced understanding of computer software, including everything from algorithmic modeling tools to machine learning. Database software, including SQL-based products, is also important for managing large batches of information. Programming languages such as R can provide statistical analysis and data visualization. Another common programming language is Python, which is more generalized.

What Is Data Mining?

Data mining is a subset of data science that refers to the process of discovering patterns and other key information from massive datasets, ultimately analyzing data to discover useful information. Data mining has considerably improved organizational decision-making, both by describing target datasets and predicting the outcomes of target datasets. Those who work in data mining spend much of their time organizing and filtering data, surfacing compelling information such as user behaviors, security breaches, production bottlenecks, and other notable anomalies.

Data Mining Methods

As with data science, data mining employs a number of tools and methodologies, some of which overlap. Like data science, data mining uses machine learning to more efficiently identify trends within massive sets of data. Additionally, those who work in data mining depend on algorithms and data visualization tools, such as Apache Spark, to help manage huge datasets.

Data mining is also closely associated with predictive analytics — the use of statistics and modeling to make well-informed predictions about future performance or outcomes. In a business setting, predictive analytics can be used to set expectations for shareholders.

Using Software in Data Mining

Like data science, data mining employs a number of software tools, including programming languages. Python, which is one of the most adaptable programming languages, is especially helpful, though data mining also relies on statistical analysis languages such as R, as well as SQL and SAS.

Data Mining vs. Data Science: Differences

While the two fields do overlap, they also have important points of distinction.

One of the most important areas of differentiation is in scope. Data science’s broad scope of capturing and building datasets provides a contrast with data mining’s process of finding key information within a dataset.

Data mining exists as a subset of data science. If data science is about creating and scaling huge bodies of data, data mining takes a deeper dive into those bodies of data in search of narrower, more specific insights. In other words, data mining is really not possible without data science laying the groundwork. Similarly, data science does not provide its full value until it’s combined with data mining.

Those who are interested in either of these fields should note that the tools of data science are often used in data mining. As a result, familiarity with algorithms and machine learning can ultimately be beneficial in either.

Building the Future in the Present

Big data carries huge implications for business, finance, sales, education, and beyond. As you seek ways to leverage big data to provide real-world value, understanding the distinctions in data mining vs. data science is a great first step.

To further explore these fields and cultivate the skills needed to become a successful data professional, consider Maryville University’s online Master of Science in Data Science. In this program, you’ll master the tools and techniques needed to succeed in either role, including programming languages and machine learning, and become better positioned to pursue any professional goals you have in the realm of data science. Explore the options for a data-related education today.

Recommended Reading

Data Science vs. Data Analytics: Understanding the Differences

Interpreting Analytical Careers: The Difference Between Business Analyst and Data Analyst

Machine Learning Engineer vs. Data Scientist: Which Option Is for You?

Sources

Bloomberg, “The Increasing Importance of Data Management for Financial Firms of All Sizes”

Dovepress, “The Role of Medical Data in Efficient Patient Care Delivery: A Review”

Entrepreneur, “Why Business Leaders Need to Take Data Seriously in 2021”

IBM, Big Data Analytics

IBM, Data Mining

IBM, Data Science

Investopedia, Predictive Analytics