Data has had a transformative effect both in the industry and in our daily lives and continues to. Data is one of the most crucial requirements in today’s world because it helps policymakers and business leaders make informed decisions based on facts, trends, and statistical numbers. Due to this growing scope and the need for data, Data Science emerged as a multidisciplinary field with applications in many industrial, academic, and applied research sectors. With it grew the importance of various Data Science Courses aimed at training and imparting knowledge to budding Data Scientists.
As Data Science continues to impact the world, there is the inevitable confusion over the terms ‘Data Science’ and ‘Data Mining’ that often end up being used interchangeably. However, there are stark differences between the two.
So, let’s take a closer look at Data Science and Data Mining, and how they differ.
In simple words, Data Science is an interdisciplinary field dealing with scientific approaches, algorithms, procedures, and frameworks that are used to extract insights and knowledge from vast amounts of data. It encompasses computer science, machine learning, mathematics, statistical research, data processing, domain expertise, and several other techniques to make sense of the immense volume of raw data generated every day.
With Data Science come the Data Scientists, experts in the domain, mathematics, computer science, and communication. Their roles and responsibilities range from industry research and data extraction to creating algorithms for automation tools, data analysis, and data modeling.
Now that we’ve looked at what Data Science means, here’s a brief introduction to the concept of Data Mining.
Data Mining describes the method of automatically rummaging through or “digging” large datasets to discover trends and patterns that are otherwise impossible to discern from a simple analysis. It uses advanced mathematical algorithms to fragment the data and assess the probability of future events. Data Mining is one of the steps of a more critical process known as KDD or Knowledge Discovery in Databases. The unique feature of this process of “digging for valuable information” is that it can answer questions that cannot be solved through straightforward query and reporting techniques. Some of the key attributes of Data Mining include:
- Emphasis on large databases and datasets
- Automatic pattern discovery
- Prediction of possible outcomes
- Inferring useful information
With a crystal clear definition of Data Science and Data Mining at our disposal, the most obvious question that arises is, “Where at all is their difference? They pretty much sound the same!”
Well, even though the two concepts seem to have an apparent similarity, a closer study will reveal the subtle disparities between the two.
So, let’s take a look at some of the differences:
- Data Science is a wide-ranging field, including big data analytics, predictive modeling, data mining, mathematics, data visualization, statistics and mathematics, computer science, behavioral science, and more.
But Data Mining is a subset of Data Science describing the specific techniques for extracting useful information from data. Being a part of Data Science, Data Mining also entails statistics, machine learning, data visualization, data transformation, pattern recognition, and data cleaning.
- The primary focus of Data Science is on scientific study with a multidisciplinary approach.
In contrast, Data Mining is more focused on the business process.
- Data Science is a more ancient concept and has been in use since the 1960s.
But the concept of Data Mining became popular much later in the 1990s.
- A Data Scientist has to fill the shoes of many. A professional in Data Science is usually the combination of a data engineer, a machine learning engineer, a deep learning engineer, an AI researcher, and a data analyst.
On the other hand, a Data Mining professional need not have to fulfill all these roles.
- Another main difference lies in the type of data used by Data Science and Data Mining professionals. Data Science can deal with all kinds of data, that is, structured, unstructured, and semi-structured information.
Data Mining, however, mainly works with structured data.
- Data Science includes the following steps – Data gathering, data manipulation, data analysis, data visualization, making predictions and recapitulation, to improve performance.
On the other hand, Data Mining involves steps such as data integration, data selection, data cleaning, data transformation, data mining, pattern evaluation, and applying the knowledge to make decisions.
- The primary aim of Data Science is to help in the creation of data-centric application products and make primarily data-driven business decisions.
In contrast, Data Mining is more about gathering data from multiple sources and making it more actionable.
- Another notable difference lies in the nature of the work involved in either field. The tools and techniques of Data Science not only help make sense of data but also help in forecasting future events by using past and present data.
By contrast, the critical components of Data Mining include looking for patterns and probing them.
- The field of Data Science also goes by the name of data-driven science.
Data Mining, however, has several alternate names such as information harvesting, data archeology, knowledge extraction, and information discovery.
- Last but not least, Data Science and Data Mining differ in their applications and use cases. Data Science involves hard-core scientific research that sets the foundation for a program, project or portfolio-centric study.
On the other hand, the patterns and trends identified from Data Mining are utilized by organizations to prepare marketing, financial and operational strategies to fuel business growth.
Data Science and Data Mining are closely associated, and one has no meaning without the other. They have far-reaching applications in many fields like social science, healthcare, agriculture, risk management, marketing optimization, marketing analytics, fraud detection, public policy and academia. The demand for various Data Science courses has also shot up in recent years. So, if you’ve been thinking of signing up for one of such courses, there is no better time than now!
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