Although the domains of data science and data mining are closely connected, their applications, methodologies, and scopes are different. The two are contrasted as follows:
Data Science
Definition: Data science is an interdisciplinary subject that focuses on extracting information and insights from both organized and unstructured data via the use of scientific methods, algorithms, and systems. It includes many other types of data-related tasks, such as machine learning, predictive modeling, data cleansing, analysis, and visualization.
Scope: Data science encompasses all aspects of the data processing pipeline, from gathering data to implementing models. It requires a blend of domain-specific knowledge and abilities from several fields, including statistics, computer science, and mathematics.
Source: Data Science vs Data Mining
Methods: Machine learning, statistical modeling, data visualization, and big data technologies like Hadoop and Spark are just a few of the many approaches used in data science. It involves feature engineering, predictive model creation, and exploratory data analysis (EDA).
Applications: Data science is used in many different areas, such as technology, marketing, finance, and healthcare. Applications include fraud detection, predictive maintenance, tailored marketing, and recommendation systems.
Outcome: Predictive models, insights, or frameworks for data-driven decision-making that may be incorporated into products or used in business processes are frequently the results of data science initiatives.
Definition: Finding patterns, correlations, and anomalies in big datasets is the main goal of data mining, a procedure that falls within the larger category of data science. It entails sifting through data to find links and patterns that are not immediately apparent to extract meaningful information.
Scope: Data mining is a particular stage of data analysis that usually comes after the preparation and cleansing of the data. It is more focused on the analysis stage of the data pipeline and has a smaller scope than data science.
Source: Things to know
Methods: Regression, association rule learning, clustering, classification, and anomaly detection are some of the data mining approaches. To find hidden patterns, techniques like k-means clustering, decision trees, and neural networks are frequently used.
Applications: Customer segmentation, fraud detection, market basket analysis, and network security all make use of data mining. It is particularly common in marketing and business intelligence, where it facilitates the discovery of patterns that guide judgment.
Outcome: Finding patterns, guidelines, or links in the data that may be utilized to inform corporate strategy, enhance operations, or spot chances for innovation is the result of data mining.
Key Differences
Scope and Breadth: Data Mining is a subset of Data Science that focuses exclusively on finding patterns in data, whereas Data Science covers every stage of the data lifecycle.
Techniques Used: While Data Mining is primarily focused on statistical and machine learning approaches for pattern recognition, Data Science employs a wider range of methodologies, including big data tools and machine learning.
Source: Key differences
End Goal: While data mining seeks to reveal hidden patterns and correlations in the data, data science seeks to develop models and frameworks that may be used to address practical issues.
To put it simply, data science is a larger discipline that includes many different procedures and methods to create all-encompassing, data-driven solutions. Data mining is one of the tools used in data science to extract insights from data.