{"id":22888,"date":"2023-04-25T09:35:28","date_gmt":"2023-04-25T16:35:28","guid":{"rendered":"https:\/\/www.springboard.com\/blog\/?p=22888"},"modified":"2023-08-18T07:46:01","modified_gmt":"2023-08-18T14:46:01","slug":"data-mining-vs-machine-learning","status":"publish","type":"post","link":"https:\/\/www.springboard.com\/blog\/data-science\/data-mining-vs-machine-learning\/","title":{"rendered":"Data Mining vs. Machine Learning: A Comparative Analysis"},"content":{"rendered":"\n

If you\u2019re just starting to learn about the world of big data, it\u2019s easy to get lost in all the jargon. One of the most common misconceptions is the difference between the terms \u201cdata mining\u201d and \u201cmachine learning.\u201d Both are often used interchangeably. But in fact, they are two different (albeit related) fields. <\/p>\n\n\n\n

If you want to work with data in any capacity, then it\u2019s essential that you understand the differences between the two. That\u2019s why we\u2019ve written this guide. Below, we\u2019ll tell you all about the difference between data mining and machine learning, and how both are employed. <\/p>\n\n\n\n

Is There a Difference Between Data Mining and Machine Learning?<\/h2>\n\n\n\n

Yes. Data mining is part of the data analysis process, whereas machine learning is an entire field of study. Broadly speaking, data mining is the process of extracting information from a dataset, whereas machine learning is the process of \u201cteaching\u201d computers how to predict more accurate outcomes. <\/p>\n\n\n\n

Data Mining vs. Machine Learning: Discipline<\/h2>\n\n\n\n

Let\u2019s start our deep dive by taking a look at what data mining and machine learning are in a broad sense. <\/p>\n\n\n\n

What Is Data Mining?<\/h3>\n\n\n\n
\"data<\/figure>\n\n\n\n

Data mining<\/a> is a pretty self-explanatory term. It\u2019s the process by which you mine useful data from vast reserves of disorganized and largely useless data. <\/p>\n\n\n\n

The data that you mine using various mining methods can be subjected to data analysis techniques<\/a> to reveal hidden patterns. The mining process is thus the first step in using data to produce business intelligence and help make strong business decisions.  <\/p>\n\n\n\n

What Is Machine Learning?<\/h3>\n\n\n\n
\"data<\/figure>\n\n\n\n

Machine learning<\/a> is a subset of AI that focuses on training computers to learn from data so that they can derive their own insights with very little human intervention. <\/p>\n\n\n\n

The goal of machine learning is to take what used to be a manual process and turn it into an automated process. Earlier, programmers had to explicitly tell computers what to do at each step of a program. With the emergence of machine learning systems, we can put computers through a training process and have them independently conduct pattern analysis and other tasks. <\/p>\n\n\n\n

Data Mining vs. Machine Learning: How Do They Work?<\/h2>\n\n\n\n

Here\u2019s how machine learning and data mining are each deployed: <\/p>\n\n\n\n

How Does Data Mining Work?<\/h3>\n\n\n\n

The data mining process begins by taking stock of the business objectives of the project. Let\u2019s say that you have a product that can produce a medical diagnosis based on scans provided by a patient. Now you need high-quality data on which you can test this product to determine how accurate it is. <\/p>\n\n\n\n

A data analyst working on this project would then proceed to identify sources for the medical scan images. They would choose sources (such as open-access medical repositories) and use techniques like classification and clustering to sift through massive volumes of data to get the exact kind of data required for this project. <\/p>\n\n\n\n

The data that you obtain from this step isn\u2019t always ready for modeling or analysis. So the analyst would then proceed to clean the data<\/a> and structure it so that the analysis process returns accurate results. <\/p>\n\n\n\n

How Does Machine Learning Work?<\/h3>\n\n\n\n

Let\u2019s return to our example of making a medical diagnosis by analyzing scans provided by a patient. A medical professional could label the images based on the appropriate diagnosis, each serving as a training example for a computer. As the medical professional labels each image, the model would slowly learn how they\u2019re being classified, with the ultimate goal of being able to make diagnoses on its own. <\/p>\n\n\n\n

The process of building models for machine learning applications is an iterative one. The process starts with an input source of data and an analysis of how well the model absorbs information from training sets. You could then go back to training machine learning models based on the model\u2019s ability to make accurate predictions. <\/p>\n\n\n\n

Data Mining vs. Machine Learning: Applications<\/h2>\n\n\n\n
\"Data<\/figure>\n\n\n\n

Both data mining and machine learning have been around long enough that they\u2019re widely used in all kinds of software applications. Let\u2019s take a look at the specific use cases that they\u2019re commonly used for. <\/p>\n\n\n\n

What Are the Different Applications of Data Mining?<\/h3>\n\n\n\n

Let\u2019s take a look at how different sectors used data mining. <\/p>\n\n\n\n

Retail<\/h4>\n\n\n\n

Retailers regularly use data mining to sift through the huge volumes of customer and product data available to them. Supermarkets, for example, can use the technique to determine which SKUs perform the best or to identify what kinds of discounts are most favored by customers during the holiday season. <\/p>\n\n\n\n

Agriculture<\/h4>\n\n\n\n

With IoT and sensors becoming widely used in farming, data mining has had a bigger say in agricultural decision-making. It is used to gather and analyze data pertaining to crop yield, soil quality, weather conditions, and so on. <\/p>\n\n\n\n

Insurance<\/h4>\n\n\n\n

The insurance industry has always used number crunching to make a lot of decisions. Data mining is widely used in this space to guide decision-making pertaining to matching customers with insurance products, detecting fraud, and identifying risky customers. <\/p>\n\n\n\n

Marketing<\/h4>\n\n\n\n

Marketers have access to troves of data each day from various sources. Professionals in this industry regularly use data mining to make large volumes of data more manageable. The insights gained from that process are used to design more efficient marketing campaigns and reduce the cost of acquiring new customers. <\/p>\n\n\n\n

What Are the Different Applications of Machine Learning?<\/h3>\n\n\n\n

Here are a few sectors in which machine learning is prominent. <\/p>\n\n\n\n

Image Recognition<\/h4>\n\n\n\n

Google\u2019s reverse image search and Facebook\u2019s ability to identify your friends in pictures are both examples of how machine learning can be used to identify elements within images and cross-reference them with other images. These are now also used in generative cases where new images of a certain kind need to be reproduced. <\/p>\n\n\n\n

Natural Language Processing<\/h4>\n\n\n\n

Personal assistants like Siri and Alexa are possible only because of developments in the machine learning field. The speech recognition and signal processing carried out by our devices is underpinned by complex machine learning systems. <\/p>\n\n\n\n

Self-Driving Cars<\/h4>\n\n\n\n

A self-driving car is essentially an agglomeration of machine learning systems that use image recognition, natural language processing<\/a>, and various other techniques to make autonomous driving possible. The neural information processing systems that guide these vehicles are constructed using various different machine learning algorithms.<\/p>\n\n\n\n

Related Read: Top 10 Machine Learning Applications<\/a><\/em><\/p>\n\n\n\n

Data Mining vs. Machine Learning: Tools<\/h2>\n\n\n\n

Both data mining and machine learning software are, of course, built using programming languages to guide their functioning. That said, there are also various tools that are used to streamline workflows and build templates. Let\u2019s take a look at what they are. <\/p>\n\n\n\n

Popular Data Mining Tools<\/h3>\n\n\n\n
\"data<\/figure>\n\n\n\n

Oracle Data Miner<\/a><\/h4>\n\n\n\n

This is an extension that is offered within Oracle\u2019s SQL development product. It provides an easy interface using which you can access features that make it possible to unearth hidden patterns, build relationships between disparate data items, and produce background knowledge to understand a dataset. <\/p>\n\n\n\n

IBM SPSS Modeler<\/a><\/h4>\n\n\n\n

The SPSS Modeler is IBM\u2019s attempt at making data mining accessible even to users who may not have a full technical understanding of the process. It has a drag-and-drop editor which you can use to stack different features and carry out mining and analysis on datasets. The visual approach is complemented by the ability to program features using Python and R. <\/p>\n\n\n\n

SAS Enterprise Miner<\/a><\/h4>\n\n\n\n

The SAS Enterprise Miner has the ability to mine vast repositories of data and does so in a surprisingly efficient fashion. It also makes it possible to use multiple models on the same dataset and compare their results side-by-side so you can decide which one you want to use going forward in the process. <\/p>\n\n\n\n

Popular Machine Learning Tools<\/h3>\n\n\n\n
\"data<\/figure>\n\n\n\n

IBM Watson<\/a><\/h4>\n\n\n\n

IBM Watson took the world by storm when it first came out thanks to its incredible computational capabilities. Watson Studio makes it simple for developers to access Watson\u2019s capabilities to build machine learning applications. Popular machine learning frameworks like PyTorch, SciKit, and TensorFlow are all accessible via Watson. <\/p>\n\n\n\n

Amazon SageMaker<\/a><\/h4>\n\n\n\n

Amazon\u2019s machine-learning product is known as SageMaker. It makes it extremely simple to deploy and maintain machine learning models dealing with large-scale applications. A big advantage of using the product is that it offers an IDE for users with technical skills and a no-code environment for those who aren\u2019t from a technical background. <\/p>\n\n\n\n

Apache Mahout<\/a><\/h4>\n\n\n\n

Apache Mahout is a machine learning tool that\u2019s widely used especially by mathematicians and statisticians. This is the case because the product offers features like a distributed linear algebra framework, making it simple to carry out complex operations at scale. Apache Spark is recommended as the backend for Mahout. They\u2019re easy to combine since they both belong to the Apache stable.<\/p>\n\n\n\n

Here is a list of some of the most commonly used open-sourced machine learning tools<\/a>. <\/p>\n\n\n

Get To Know Other\tData Science Students<\/h4>
\"Jonah<\/a>

Jonah Winninghoff<\/p>

Statistician at Rochester Institute Of Technology<\/p><\/div>

<\/div>

Read Story<\/a><\/p><\/div><\/div>

\"Meghan<\/a>

Meghan Thomason<\/p>

Data Scientist at Spin<\/p><\/div>

Read Story<\/a><\/p><\/div><\/div>

\"Bryan<\/a>

Bryan Dickinson<\/p>

Senior Marketing Analyst at REI<\/p><\/div>

Read Story<\/a><\/p><\/div><\/div><\/div><\/div>\n\n\n\n

Data Mining vs. Machine Learning: Careers<\/h2>\n\n\n\n

Data mining and machine learning are both skills that are quite in demand in the software industry. Let\u2019s find out what kind of roles you can land with them. <\/p>\n\n\n\n

What Jobs Are Available in Data Mining?<\/h3>\n\n\n\n

Data professionals who want to focus exclusively on the mining aspect of the field can find work as data mining specialists. These are software professionals who are experts at mining data from various sources and making them easily available for the analysis process. <\/p>\n\n\n\n

You can also work in other capacities in the data field if you\u2019re somebody who\u2019s good at data mining. That includes roles such as data analyst<\/a>, data scientist<\/a>, and data architect<\/a>. Although mining isn\u2019t the only thing you\u2019ll be doing at those jobs, being good at mining can help you execute your other tasks more efficiently. <\/p>\n\n\n\n

What Jobs Are Available in Machine Learning?<\/h3>\n\n\n\n

The most common job in this field is that of a machine<\/a> <\/a>learning engineer<\/a>. This is a role where you focus on developing machine learning algorithms and building models for specific use cases. <\/p>\n\n\n\n

The other jobs that you can get in machine learning depend on your interests. Engineers who are interested in robotics, for example, can work as robotics engineers. Machine learning developers also often go into areas like natural language processing, cybersecurity, and AI. <\/p>\n\n\n\n

Data Mining vs. Machine Learning: Future Outlook<\/h2>\n\n\n\n

Let\u2019s find out what the prospects are like for a career in machine learning or data mining.<\/p>\n\n\n\n

What Does the Future of Data Mining Look Like?<\/h3>\n\n\n\n

Data mining is a core skill in the field of data science<\/a>. It\u2019s regularly used by data analysts, data scientists, and data engineers<\/a>. <\/p>\n\n\n\n

The future of all those jobs is bright. According to the US Bureau of Labor Statistics<\/a>, employment for data scientists is going to rise by an incredible 36% between 2021 and 2031. That\u2019s a lot higher than the average for all jobs. <\/p>\n\n\n\n

What Does the Future of Machine Learning Look Like?<\/h3>\n\n\n\n

The field of machine learning has become so popular that companies are scrambling for talent. Because of this, machine learning is also among the more lucrative fields in the software industry. The current average annual salary for machine learning engineers<\/a> is $153,151. That\u2019s a lot higher than the average, and those numbers will only go up in the future as the demand for machine learning experts is expected to continue increasing. <\/p>\n\n\n\n

What Are Some Other Differences Between Data Mining and Machine Learning?<\/h2>\n\n\n\n
\"What<\/figure>\n\n\n\n

Let\u2019s sum up what sets data mining and machine learning apart along a few different parameters. <\/p>\n\n\n\n

Goal<\/h3>\n\n\n\n

The goal of machine learning methods is to enable computing systems to learn from training datasets and identify hidden patterns in them. Essentially, the aim is to make pattern analysis and machine intelligence a reality. <\/p>\n\n\n\n

Data mining is the process of acquiring and preparing data for the data analysis process. Tools such as a mining algorithm and mining engine are selected based on the objectives of the overall project. <\/p>\n\n\n\n

Manual vs. Automatic Process<\/h3>\n\n\n\n

Data mining is a more manual process than machine learning. This is because it\u2019s not easy to automate the parts that involve finding data sources, cleaning data, and structuring it before analysis. <\/p>\n\n\n\n

Machine learning is entirely geared toward automation. The whole goal of the discipline is to automate tasks so that computers are able to learn more and more from training datasets and carry out decision analysis on their own. <\/p>\n\n\n\n

Existing Dataset vs. Trained Dataset<\/h3>\n\n\n\n

Data mining is carried out on an existing dataset. There is no notion of training a dataset in data mining. Machine learning, on the other hand, involves training machine learning algorithms on datasets<\/a>. This is how they\u2019re able to identify patterns and unearth insights from new datasets.<\/p>\n\n\n\n

What Are the Similarities Between Data Mining and Machine Learning?<\/h2>\n\n\n\n

Although the two fields are quite unlike each other in many ways, there are some similarities. The thing that unites these two fields is their focus on data. Both data mining and machine learning fall under the purview of data science. You need to understand the foundational principles of data science if you want to work with either of them.<\/p>\n\n\n\n

Another thing they have in common is the volume of data that is involved in each of them. Data mining and machine learning both usually involve working with large volumes of data. So you need to have systems that can handle these large volumes and process them efficiently.<\/p>\n\n\n\n

Data Mining vs. Machine Learning FAQs<\/h2>\n\n\n\n

We\u2019ve got the answers to your most frequently asked questions:<\/p>\n\n\n

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Is Data Mining a Part of Machine Learning?<\/h3>\n
\n\n

No, data mining is not considered a part of machine learning. Data mining is the independent process of preparing and analyzing data to solve a business problem. Machine learning, on the other hand, is the field that concerns itself with teaching computers to learn from trained datasets.<\/p>\n\n<\/div>\n<\/div>\n

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Does Data Mining Require Coding?<\/h3>\n
\n\n

Yes, data mining does often require coding. Python<\/a> and R<\/a> are commonly used languages for the process.<\/p>\n\n<\/div>\n<\/div>\n

\n

Which Career Is Right for Me: Data Mining or Machine Learning?<\/h3>\n
\n\n

Machine learning is the right career for you if you have a strong understanding of the computational aspects of the field as well as mathematical concepts like statistics and linear algebra. Data mining is a good choice if you want to focus more narrowly on preparing and processing data for analysis.<\/p>\n\n<\/div>\n<\/div>\n

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Which Career Earns More: Data Mining or Machine Learning?<\/h3>\n
\n\n

Machine learning engineers make $153,151 per year on average<\/a>. Meanwhile, the average salary in the data mining field annually is $96,535<\/a>.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n

Since you\u2019re here\u2026
<\/strong>Curious about a career in data science? Experiment with our free data science learning path<\/a>, or join our Data Science Bootcamp<\/a>, where you\u2019ll get your tuition back if you don’t land a job after graduating. We\u2019re confident because our courses work \u2013 check out our student success stories<\/a> to get inspired.<\/p>\n","protected":false},"excerpt":{"rendered":"

If you\u2019re just starting to learn about the world of big data, it\u2019s easy to get lost in all the jargon. One of the most common misconceptions is the difference between the terms \u201cdata mining\u201d and \u201cmachine learning.\u201d Both are often used interchangeably. But in fact, they are two different (albeit related) fields.  If you […]<\/p>\n","protected":false},"author":100,"featured_media":42988,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_eb_attr":"","_eb_data_table":"","footnotes":""},"categories":[67],"tags":[],"marketing_tags":[1466],"class_list":{"0":"post-22888","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science"},"acf":[],"_links":{"self":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/22888"}],"collection":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/users\/100"}],"replies":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/comments?post=22888"}],"version-history":[{"count":4,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/22888\/revisions"}],"predecessor-version":[{"id":49184,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/22888\/revisions\/49184"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/media\/42988"}],"wp:attachment":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/media?parent=22888"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/categories?post=22888"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/tags?post=22888"},{"taxonomy":"marketing_tags","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/marketing_tags?post=22888"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}