Data Mining vs. Data Science: Understanding the DifferencesData Mining vs. Data Science: Understanding the DifferencesData 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.
Data Mining vs. Data Science: What Are They?
At first glance, data mining and data science may appear similar. After all, data and its proper application are central to both, and each ultimately works to gain beneficial insight from sourced data. However, they are two distinct fields.
What Is Data Science?
Data science is a multidisciplinary field devoted to drawing actionable insights from large and evolving data sets. 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 data set 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. The value of these insights has put data scientists in high demand in several fields.
Data Science Methods
Data science relies on a number of different methods. Artificial intelligence, or AI, can assess huge data sets 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 data sets, ultimately analyzing data to discover useful information. Data mining has considerably improved organizational decision-making, both by describing target data sets and by predicting the outcomes of target data sets. 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 in massive data sets. Additionally, those who work in data mining depend on algorithms and data visualization tools, such as Apache Spark, to help manage huge data sets.
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 data sets provides a contrast with data mining’s process of finding key information in a data set.
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 doesn’t 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.
Data Mining vs. Data Science Curriculum
Because data mining and data science are so closely related, data mining is often offered as a specialization or postgraduate certificate rather than a separate master’s degree. These programs often focus on statistical learning, applied statistics, and developing predictive models.
Programming languages. A deep dive into the common languages used in data science, such as Python, SQL, SAS, and R. In some cases, each language may get its own focused course.
Machine learning. A study of the core elements of machine learning, including data mining. The course also covers concepts such as learning algorithms, learning theory, neural networks, and web data processing.
Predictive analytics. The exploitation of the components of building data models that support predictive analytics. By breaking down concepts such as clustering methods and principal components analysis, students gain a better understanding of creating predictive models.
Data visualization. A survey of the different methodologies behind transforming raw analyzed data into crucial information that can be effectively presented in a manner that optimizes audience comprehension.
The curricula in programs like Maryville’s online master’s in data science are commonly projected-based. This gives students the opportunity to apply their knowledge in a hands-on environment, honing their skills in a controlled setting.
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’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 data-related education today.