Data mining and data analytics are both important components of any data-driven project. These two processes are often used together to guarantee the successful completion of a project. While the differences between these two processes can be minute, when studying both fields closely, differences emerge. This article will explore some of the main differences between data analytics and data mining, as well as when it is appropriate to use each.
What is Data Analytics?
Each day, an estimated 2.5 quintillion bytes of data are created. The past two years alone accounted for 90% of this data creation. However, until this data is sorted and analyzed, none of it is actionable. But what to do with it all? This is where data analytics comes in.
Data analytics is the set of techniques used to analyze raw data (unprocessed data) to extract relevant information, trends, and insights. This process includes collecting data, organizing it, and storing it, then performing statistical analysis on the data. Once the information is collected, patterns can be spotted, and conclusions can be drawn from it, which can be used for problem-solving, business processing, decision-making, and predictions that inform what a company’s next steps should be. This process relies on disciplines like mathematics, machine learning, statistics, artificial intelligence, and computer programming.
What is Data Mining?
Data mining is a deliberate cycle that involves uncovering relationships and patterns in large datasets, as well as uncovering correlations, patterns, or anomalies, which can be used to predict outcomes. This advanced form of data analysis draws from many techniques and tools, like artificial intelligence, statistics, and machine learning, to help users locate important information. Those who are skilled at data mining can offer their organization insights about customer needs, as well as suggestions for how to cut down on costs and increase revenue.
The current demand for efficient and effective methods for data mining is prevalent across industries. Data mining is commonly used in fields like healthcare and pharmaceuticals, as well as careers that use geographic mining, such as spacecraft design, asteroid mining, and GPS-powered navigation tools like Google Maps. The information gathered from data mining helps companies or organizations reduce cost and risk, increase profit, and offer better customer service options.
As more data is created, the need for data mining will likely continue to increase in the coming years. It’s predicted that the global market for data mining tools will increase from $552 million in 2018 to $1.31 billion by 2026.
How Does Data Analytics Differ From Data Mining?
Because the primary goal of data analytics is to extract insights from data, it has a broader scope than simply locating patterns within data warehouses. For this reason, the field of data analytics typically includes steps both before and after the data mining process. Even though data analytics and data mining overlap, there are a few key differences to be aware of:
- Data mining is considered to be a path toward knowledge discovery within databases. Data analytics tends to be a broader term, which can be broken down into several sub-fields, such as exploratory analytics, descriptive statistics, and confirmation analytics.
- Data mining is most effective when it focuses on well-structured data, but data analytics can draw from unstructured, semi-structured, or structured data.
- Unlike data mining, which doesn’t offer tools for visualization, data analytics is driven by the visualization that’s created from the results of the analytics process.
- The term “data science” has been around since the 1960s, but the phrase “data mining” originated decades later, in the 1990s.
- The primary function of data analytics is to test hypotheses. On the other hand, data mining doesn’t require a bias or preconceived idea of potential outcome before it interacts with the data.
- Those who mine data use scientific models and mathematics to find meaningful patterns or structures in the dataset. Data Analysts rely on business intelligence and analytics models to find explanations, perform tests, and offer hypotheses about the data.
- Data mining can be considered the first step in a larger process that combines both mining and analysis. The data mining portion pertains to using mathematical and scientific models to collect data and extract basic, essential insights. Then, Data Analysts incorporate business analytics tactics in order to take this information and the hypothesis derived from these basic insights in order to craft an analytics model.
Data mining and data analytics play vital roles in helping a company or business handle the ever-increasing amount of data that is being created. Both data tools can be used to spot future opportunities and help inform the decision-making process. Although data mining and data analytics can both be applied to help a company grow, these two processes differ in terms of the kinds of technology and tools used to carry out tasks, as well as the steps they incorporate to reach the desired outcome.
Hands-On Data Analytics Classes
The best way to learn about the current best practices, trends, and industry-standard software and tools in Data Analytics is to consider enrolling in one of Noble Desktop’s data analytics classes. Courses are offered in New York City, as well as in the live online format in topics like Python, Excel, and SQL.
In addition, more than 130 live online data analytics courses are also available from top providers. Topics offered include FinTech, Excel for Business, and Tableau, among others.
Courses range from three hours to six months and cost from $219 to $27,500.
Those who wish to study data mining or data analytics in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely, small-class instruction. Over 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python, among others.
Are you searching for a data analytics class near you? If so, Noble’s Data Analytics Classes Near Me tool provides an easy way to locate and browse approximately 400 data analytics classes currently offered in in-person and live online formats. Course lengths vary from three hours to 36 weeks and cost $119-$27,500.