Although these two disciplines have a lot in common, there are some significant differences between data mining and data science. What are both these disciplines all about? When do you need data science, and when do you need data mining? In this article, we are going to show you a comprehensive comparison: Data mining vs. data science.
For the sake of this article, think about two professions: a miner and a scientist. A miner’s main role is to dig in the ground to find something valuable–coal, diamonds, gold, etc. A scientist’s role is different, although they can work on material dug by the miners, for instance, diamonds. Even though it’s a massive simplification, it’s a good illustration of the difference between data mining and data analytics.
And now, let’s cut right to the chase, drop our example for a few moments and let’s think about the differences between data science and data mining. We will start with data science, primarily because it’s a much more comprehensive and complex field.
Data science and data mining comparison: Data science
Data science is a field that’s frequently discussed on our blog, and for a reason. In fact, when it comes to various AI-fueled technologies and solutions, data science is always the cornerstone that allows them to work properly.
Data science is all about processing and analyzing data in your company. It’s a broad term that comprises a whole set of tools that help you obtain useful information that further can be used to make more informed decisions or to fuel various ML/AI algorithms.
We could generally say that data scientists analyze, process, and model data they possess. Then, their role is to interpret the results to create actionable plans and insights for the organization they work in. Data science is a multi-role field of expertise that entails an understanding of such disciplines as computer science, math, statistics, software development, and, to some extent, general business knowledge as well. When it comes to data science, there’s no art for art’s sake here. This tool should be utilized when there’s a measurable business benefit at the end of this process.
DIFFERENCE BETWEEN DATA MINING AND DATA ANALYTICS: WHAT IS DATA SCIENCE USED FOR?
From the business perspective, data science has five fundamental applications/goals.
Identifying trends and patterns in large datasets
This is where data science and big data overlap. As you know from our other blog posts, big data simply cannot be processed or analyzed without the tools provided by data science. With data science, companies can efficiently analyze large datasets they possess and use this advantage to identify trends and patterns within. What for? In order to make better decisions and set the direction of development for the company.
Although anomalies in data happen in literally every sector and industry, especially the broadly understood financial sector makes the most of identifying anomalies. For instance, if a bank detects some unusual activity on their customer’s account, they can take necessary steps to prevent, for example, money laundering.
Predicting the likelihood of future events
For obvious reasons, companies want to know what the future holds. Predictive analytics is a solution that’s a blend of data science and machine learning. ML algorithms analyze historical data and other variables (market conditions, economical situation, etc.) in order to try and predict the future. For instance, predictive analytics could be used to assess future sales levels or real estate prices in a given city. Such knowledge allows you to take some steps in advance.
Uncovering correlations in data
Our recent article about common mistakes in data science warned you about mixing correlation and causation. You shouldn’t do that, but detecting both correlations and causations can be immensely helpful in understanding how your company works and why something happens. On many occasions, companies struggle to determine the source of a given situation or problem. Thanks to the ability to uncover trends and correlations in data, data science can help you with similar doubts.
Categorizing data points
Categorizing or, in other words, data classification is based on determining to which category a given new object or observation should belong. This feature is commonly used, for instance, in SPAM detection, but also sometimes in search engines.
Data science vs. data mining: Work optimization
Naturally, the aforementioned applications are not the only aspects of data science. In general, we could say that the ultimate goal of data science is to improve and optimize work. Let us consider two significant applications making that happen: IT systems management and the Internet of Things.
IT SYSTEMS AND INFRASTRUCTURE MANAGEMENT
If you run some kind of industrial facility–read on because this application is extremely useful when it comes to monitoring various sensors and measurements in such facilities. IT systems and infrastructure management have to happen 24/7, all year round. Data science, along with IoT, facilitates monitoring these systems and devices, and everything happens remotely, so the need for on-site inspections is decreased
INTERNET OF THINGS (IoT)
IoT is based on sensors and detectors that measure various parameters 24/7 in places where your products/devices are stored, used, transported, and shipped. Frequently this technology is combined with data science and machine learning algorithms that make it much more effective and useful. Thanks to IoT, your company knows what happens with your products, infrastructure, machines, and devices at any given moment.
We are right in the middle of our data mining vs. data science comparison. Now, you what data science is all about and what this field entails. Now, let’s concentrate on data mining. Although it’s a sphere that’s tightly related to data science, there are some differences, mostly due to the fact that data mining is a much narrower area. But, since data mining is a much rarer visitor on our blog, some history first.
What is data mining? Short history
We ought to begin by saying that the history of data mining started in the 18th century when in 1763, Thomas Bayes described the probability of an event, based on prior knowledge of conditions that might be related to the event. His work is currently known as Bayes’ Theorem. However, the real development of technology that was necessary to make data mining happen was in the 20th century, when the computer age started. Here, the most important milestones and inventions were:
- Neural Networks: 1943
- Evolutionary computation: 1965
- Databases: 1970
- Genetic Algorithms: 1975
The term data mining appeared around 1990. Interestingly, other terms that were used at that time to describe what is currently known as data mining were: Data archaeology, information harvesting, information discovery, knowledge extraction.
Actually, these other terms can be useful to understand what data mining is all about. Let’s cut right to the chase:
Difference between data analytics and data mining: What is data mining all about?
Take a look at the definition coined by SAS: Data mining is the process of finding anomalies, patterns, and correlations within large data sets to predict outcomes. It does sound quite similar to data science, doesn’t it? So what’s the difference?
Think of data mining as a subset of data science. Data science is a much more comprehensive term that describes the entire discipline, whereas data mining is rather a technique. The whole point of data mining is to make data your company possesses more usable. Data science, on the other hand, is all about building more holistic, data-centric products. Data mining deals almost exclusively with structured data, while data science analyzes all forms of data.
Additionally, data mining is a part of the larger Knowledge Discovery in Databases (KDD) process, which typically consists of five stages:
And the data mining part comprises other six elements:
- Business understanding
- Data understanding
- Data preparation
So yes, in many ways, data science and data mining are similar. Now, let’s take a look at the typical data mining process.
DIFFERENCE BETWEEN DATA ANALYTICS AND DATA MINING: THE DATA MINING PROCESS
As it frequently happens in AI-related fields, we have to start with the definition of our problem. The first part of the data mining process is data gathering and preparation, which comprises three elements:
- Data access
- Data sampling
- Data transformation
The crucial element of data gathering is to take a closer look at the data you are going to process in order to determine how well it addresses the business problem you’re dealing with. In fact, this stage of the data mining process is frequently critical to the entire project’s success.
The next step is model building and evaluation, where you select and apply various modeling techniques and calibrate the parameters to optimal values. Here, we have:
- Model creation
- Evaluation and interpreting
And finally, we end up with knowledge deployment, and this stage consists of:
- Model’s application
- Custom reports
- External applications
Additionally, it’s worth noting that in the deployment phase, insight and actionable information can be derived from data. As you can see, the data mining approach is much more technical and focused on processing data.
Now, let’s get back to our miner/scientist example. The miner’s role is to dig for diamonds, extract them, and sometimes prepare them for further treatment. The scientist’s role is quite different, although they also work with the same diamond. They analyze it, study it, and try to make the best use of the miners’ diamond excavated. And that’s essentially the difference between data mining and data science. Which one of these two disciplines is necessary? The answer is simple –both of them. If you’d like to find out more about data mining and data science–drop us a line! We will gladly help you make the most of the data in your company.