Data Mining vs. Data Science: Key Differences

In this blog, you will learn about data science and data mining, how they are different, their applications in the industry, and the differences between them.

• Exploring Data Mining
• Steps Used for Data Mining
• Application of Data Mining
• Exploring Data Science
• Introduction to Data Science Life Cycle
• Application of Data Science
• Data Mining vs Data Science

Businesses and organizations started to realize the enormous value that is concealed in the large amounts of data that are captured on a daily basis. This led them to start introducing and employing different techniques to realize that potential and value. 

The ultimate goal is to derive actionable insights from data. However, this introduced the need for a significant number of technical terms. And although some people use data science and data mining interchangeably, there are significant differences between the two terms. Today, we will talk about some of the most prominent differences between data science and data mining.

Exploring Data Mining

The term data mining appeared in 1990 in the database community. Data mining is used by retail companies and the financial community for the purpose of analyzing data and identifying trends to increase the customer base and predict fluctuations in stock prices, interest rates, and customer demand.

Data mining is the process of identifying patterns in large datasets. It involves methods at the intersection of database systems, statistics, and machine learning. The overall goal of this interdisciplinary subfield of computer science and statistics is to extract information from large datasets or libraries of data by using sophisticated mathematical algorithms and transform them into a comprehensible structure for further use.

Data mining helps derive insights through careful extraction, reviewing, and processing of raw data to discover patterns and correlations that can be valuable for businesses. Data mining processes include different types of services such as:

• Web mining
• Text mining
• Audio mining
• Video mining
• Social network data mining
• Pictorial data mining

Data mining also referred to as Knowledge Discovery in data (KDD), is performed with the help of simple or advanced software. The following steps are involved in data mining:

• Business Understandings: It involves introducing and understanding the objective and work of the business as well as understanding the significant factors that will help achieve the target of the business.
• Data Understandings: It performs data collection and data accumulation. The data is listed based on the source data, its location, how it was achieved, and if any issues had cropped up. The data is then visualized and checked for its completeness.
• Data Preparation: It involves the selection of useful data, cleaning it, constructing attributes from it, and data integration from multiple databases.
• Modeling: It involves selecting data mining techniques, generating test designs to evaluate the selected model, building a model from the datasets, and evaluating the model with experts to know the result.
• Evaluation: It determines the degree to which the resulting model meets the business requirements by testing it based on real applications.
• Deployment: It creates a deployment plan and forms a strategy to check the usefulness of the data mining model through maintenance and monitoring.

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Steps Used for Data Mining

Data mining involves several steps to extract valuable insights and patterns from large datasets. Here are the typical steps used in the data mining process:

• Problem Definition: Clearly define the objective and scope of the data mining project. Identify the specific questions or problems you want to address using data mining techniques.
• Data Collection: Gather relevant and appropriate data from various sources. This may involve accessing databases, collecting data from APIs, web scraping, or other data acquisition methods.
• Data Cleaning: Preprocess the collected data to ensure its quality and reliability. Handle missing values, remove duplicates, correct errors, and address inconsistencies in the data. Data cleaning also involves transforming the data into a suitable format for analysis.
• Data Exploration: Perform exploratory data analysis to gain a better understanding of the data. Visualize the data, identify patterns, detect outliers, and explore relationships between variables. This step helps generate hypotheses and insights that guide subsequent analysis.
• Feature Selection: Select relevant features or variables that are most likely to contribute to the desired outcomes. Eliminate redundant or irrelevant features to simplify the analysis and improve model performance.
• Data Transformation: Apply data transformation techniques such as normalization, scaling, or encoding categorical variables to prepare the data for modeling. This step ensures that the data meets the assumptions of the chosen data mining algorithms.
• Model Building: Apply suitable data mining algorithms to develop predictive or descriptive models. This can include techniques such as decision trees, regression, clustering, association rules, or neural networks. Select the appropriate algorithms based on the nature of the problem and the characteristics of the data.
• Model Evaluation: Assess the performance and quality of the models using suitable evaluation metrics and validation techniques. This step helps determine the effectiveness and reliability of the models and identifies areas for improvement.
• Model Deployment: Implement the chosen model into a real-world setting and integrate it into the existing systems or processes. This may involve deploying the model as a software application, integrating it into a business intelligence platform, or incorporating it into decision-making processes.
• Model Monitoring and Maintenance: Continuously monitor the performance of the deployed models and update them as new data becomes available. Models may require retraining or recalibration over time to ensure their accuracy and relevance.

Applications of Data Mining

Some of the applications of data mining are:

• Market analysis
• Financial analysis
• Higher education
• Fraud detection

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Exploring Data Science

Data science, as a term, was proposed by Peter Naur in 1974 as an alternative name for computer science. However, the field resembling modern data science was described by John Tukey in 1962 and called data analysis. In 1997, C.F. Jeff Wu suggested renaming statistics as data science, and in the following year, Chikio Hayashi argued for data science as an interdisciplinary concept encompassing data design, collection, and analysis.

Data science is an interdisciplinary field or domain that involves the use of scientific methods, algorithms, processes, and systems to extract knowledge and insights from a large amount of structured and unstructured data. This, in turn, is used for building predictive, prescriptive, and prescriptive analytical models. 

Data science is related to big data, deep learning, and data mining. It is an intersection of data and computing and is all about digging, capturing (building the model), analyzing (validating the model), and utilizing the data (deploying the best model). Data science blends business with computer science and statistics. 

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Introduction to Data Science Life Cycle

The six steps involved in the data science process are:

• Framing the Problem: Before solving a problem, it is important to know what the problem is; data questions first have to be translated to actionable business questions.
• Collection of the Raw Data Required for the Problem: The required data has to be gathered to derive insights and probable solutions by scanning internal databases or purchasing databases from external sources.
• Processing the Data for Analysis: The data has to be processed before going further and analyzing it for more accurate insights.
• Exploring the Data: This crucial step involves developing ideas to help identify hidden patterns and insights. 
• Performing In-depth Analysis: In this stage, mathematical, statistical, and technological knowledge and data science tools have to be implemented to crunch the data successfully and discover and derive every insight possible as well as other crucial factors. The quantitative and qualitative data can be combined and moved into action.
• Communicating the Results of the Analysis: In this step, insights and findings are conveyed to the sales head to make them understand the importance of the findings and how they can help in business growth. 

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Applications of Data Science

Some of the applications of data science include:

• Fraud and risk detection
• Targeted advertising
• Speech recognition
• Healthcare
• Website recommendations
• Advanced image recognition
• Internet search
• Airline route planning

Data Mining vs Data Science

• The biggest difference between data science and data mining lies in their terms. While data science is a broad field that involves capturing data, analysis of data, and deriving actionable insights from it, data mining primarily involves finding useful information in a dataset and utilizing that to identify hidden patterns.
• Another big difference between data science and data mining is that the former is a multidisciplinary field consisting of statistics, data visualizations, social sciences, natural language processing (NLP), and data mining, which means that data mining is a subset of data science.
• A data scientist can be considered, to some extent, a combination of artificial intelligence (AI) researcher, machine learning engineer, deep learning engineer, and data analyst. On the other hand, a data mining professional cannot necessarily perform all these roles, which can be performed by a data scientist.
• Another notable difference lies in the type of data used. Data science mostly deals with all types of data such as structured, unstructured, and semi-structured. However, data mining mostly deals with structured data.
• When considering the nature of work, there is another difference between data science and data mining. Uncovering patterns and analyzing them is a key component of data mining. Data science involves the same, but it also involves forecasting future events by leveraging the present and historical data using various tools and technologies.
• Data science focuses on the science of data, while data mining is mainly concerned with the process of detecting anomalies and inconsistencies and predicting outcomes.
• Data Science generally commands higher salaries compared to Data Mining. Data Science involves a broader skill set and encompasses various aspects, including data mining, data analysis, machine learning, and statistical modeling. Data Scientists are often involved in more complex and strategic tasks, leading to higher earning potential. However, salary ranges can vary depending on factors such as job role, industry, experience, and location.

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Data Science vs Data Mining Comparison Table

The following table further elucidates the topics of data science vs data mining:

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Conclusion

Whether it is data science or data mining, when it comes to handling the exponentially growing data volume, both play a crucial role in helping businesses identify opportunities and make sound decisions. 

So, while the goal of both data science and data mining is, in a way, similar, i.e., to derive insights for helping businesses perform better and grow, the key differences lie in the tools and technologies that are implemented, the nature of work, and in the stages involved in performing the respective responsibilities to achieve that goal.