# Difference Between Data Mining VS Data Science

Data mining and data science have become ubiquitous terms used interchangeably in analytics and business contexts. However, they refer to related but distinct processes, mindsets, and capabilities for extracting value from data.

This guide provides an in-depth comparison of data mining and data science across various parameters including methods, models, applications, processes, and skillsets. It also covers how the two domains converge in the world of Big Data analytics.

## What is Data Mining?

Data mining is the process of looking at big sets of data to find useful patterns and information. The key components of data mining are given below:

- Data Selection – Selecting the dataset(s) to analyze. This may involve combining data from multiple sources.
- Data Cleaning – Detecting and removing errors, inconsistencies, missing values, and duplicate data.
- Data Transformation – Converting data into appropriate formats for mining. This may involve normalization, discretization, attribute construction, aggregation etc.
- Choosing the Data Mining Task – Deciding the kind of patterns to look for e.g. classification, regression, clustering, association rule mining etc. based on the objective.
- Choosing the Data Mining Algorithm – Selecting appropriate algorithms like decision trees, neural networks, regression, k-means clustering etc. based on the task.
- Data Mining – Running the data through the data mining models to identify meaningful patterns and relationships.
- Interpretation/Evaluation – Interpreting mined patterns and assessing the interestingness and validity of the results.
- Iteration – Using discoveries as feedback to iterate through the process and refine the mining process.

Some key characteristics of data mining include:

- Applying statistical and machine learning techniques to find interesting trends and patterns in data.
- Leveraging algorithms like classification, clustering, regression, decision trees on structured and unstructured data.
- Focus on predictive modeling â€“ identifying factors and variables that can predict a target outcome.
- Utilizing techniques like anomaly detection, association rules mining to uncover hidden patterns.
- Processing large historical datasets from databases and data warehouses.
- Goal is to discover new, non-intuitive insights from legacy data.
- Often an exploratory, ad-hoc analysis process driven by data researchers.

Data mining emerged as a field in the 1990s focused on analyzing structured enterprise data. It provides the techniques and algorithms required to perform predictive analysis on big data.

## What is Data Science?

Data science is the interdisciplinary field of extracting insights from various data types using scientific methods and processes to drive decision making. It combines skills in math, statistics, programming, and domain expertise.

These are the key aspects of data science.

- Leveraging statistics, machine learning, advanced analytics to solve real-world problems.
- Working with structured, unstructured, spatial, temporal, textual and graphical data.
- Focus on data analytics lifecycle – data collection, cleaning, analysis, modelling, and deployment.
- Applying techniques like classification, clustering, topic modelling, sentiment analysis, image recognition etc. based on the problem and data.
- Open-ended exploration as well as rigorous hypothesis testing approaches.
- Aligning to business goals to drive innovation using data.
- Collaborative approach leveraging capabilities across data engineering, analytics, visualization, product, and domain expertise.

Data science provides the comprehensive framework to harness data and analytics to create business value.

## Key Differences Between Data Mining and Data Science

Basis | Data Mining | Data Science |
---|---|---|

Goal | Discover interesting patterns and relationships in data | Solve real-world business problems with data |

Data Sources | Structured data in databases and warehouses | Any – structured, unstructured, open source, real-time streams |

Data Scope | Historical data | Historical, real-time, future projections |

Techniques | Classification, clustering, regression, association, anomaly detection | All data mining techniques + NLP, ML, graph analysis, etc. |

Process | Ad-hoc, exploratory, black box modeling | Structured using CRISP-DM, OSEMN or Team Data Science Process |

Toolkits | R, Python, Weka, KNIME, SQL | R, Python, specialized libraries – Keras, TensorFlow etc. |

Analytics Focus | Predictive modeling – forecasting and probabilities | Predictive + prescriptive modeling – recommendations |

Key Outputs | Lists of patterns, factors, clusters, decision trees | Actionable models, analytics applications, intelligent systems |

Problem Framing | Narrow technical focus | Aligning to business objectives |

Organizational Role | IT-driven analytics | Cross-functional collaborative domain |

While data mining provides the foundation, data science incorporates a much wider array of data types, techniques, and business contexts.

## Areas of Convergence

Data mining and data science converge on the following aspects:

**Statistical Foundation**

Both are grounded in statistics – distributions, hypothesis testing, regression modeling, significance testing etc. Statistical thinking guides the analysis.

**Machine Learning Models**

Supervised and unsupervised ML models like regression, random forests, kmeans, etc. are leveraged extensively by both disciplines.

**Programming**

R and Python provide the common analytical toolkit to implement data mining and data science techniques.

**Algorithms**

Algorithms for classification, clustering, anomaly detection, association rule mining etc. enable uncovering patterns.

**Big Data Platforms**

Hadoop, Spark, distributed stream processing underpin data science pipelines and data mining at scale.

**Cloud Infrastructure**

Platforms like AWS, GCP and Azure provide on-demand access to storage, computing for analysis.

**Focus on Insights**

The core emphasis of generating new insights from data through sophisticated techniques is shared.

## Typical Process Flows

The workflows for typical data mining and data science projects also showcase their converged and divergent nature:

**Data Mining Process Flow**

- Identify interest area or factors to analyze e.g. retail sales, drug effects
- Collect relevant structured data sets and integrate data as needed
- Explore data visually and statistically to understand distributions and cleansing needs
- Transform data into target variables and features for input into models
- Select data mining algorithms and techniques like decision trees, SVMs, cluster analysis etc. based on goals
- Train models with different configurations and parameter tuning
- Evaluate and compare models using metrics like accuracy, precision, recall, F1
- Analyze and interpret the key patterns, relationships and insights discovered
- Create reports, visualizations and presentations to communicate findings

**Data Science Process Flow**

- Frame business challenge and identify relevant data sources and variables
- Ingest data from disparate sources like sensors, web, enterprise systems
- Explore, cleanse and preprocess data – handling missing values, outliers etc.
- Perform statistical analysis like correlation, sentiment analysis, signal processing to understand data relationships
- Engineer features from structured and unstructured data for modelling
- Train machine learning models using algorithms like SVM, XGBoost, neural networks etc.
- Rigorously evaluate models for overfitting and underfitting
- Interpret model results and extract meaningful insights
- Deploy models and analytics apps to products and business processes
- Continuously monitor models and retrain with new data

While focused only on mining insights, data science covers the end-to-end cycle from data to deployment.

## Career Transitions

The overlaps enable movement across the two domains:

**Data Mining to Data Science**

For this transition, developing software engineering skills, knowledge of statistical and ML techniques, and business acumen are key.

**Data Science to Data Mining**

Data scientists moving to data mining roles need to strengthen core data mining algorithms knowledge, techniques, and R/Python libraries like Keras, PyTorch, scikit-learn etc.

**Hybrid Roles**

Cross-functional roles like business intelligence developers, data analytics consultants, insights analysts combine both skillsets.

## Emergence of Data Science

Data science has evolved as a multidisciplinary field encompassing data mining due to various factors:

**Exponential Data Growth**

The explosion of Big Data across structured, unstructured, spatial, temporal and network formats requires expanded analytical capabilities.

**Disparate Data Sources**

Data science incorporates newer data types like clickstream, social media, mobile, IoT and combines them with traditional enterprise data.

**Expanding Analytics Scope**

Predictive modeling now expands into recommendation systems, text analytics, image recognition, customer lifetime value etc.

**Increasing Complexity of Analysis**

Techniques have evolved from statistical models to sophisticated machine learning and deep learning algorithms.

**Cloud Computing Infrastructure**

Scalable cloud infrastructure has enabled applying data science approaches economically.

**Business Alignment**

Tight alignment to business objectives and KPIs differentiates modern data science.

**Organizational Integration**

Data science brings together cross-functional expertise spanning business, analytics, engineering and product.

**Focus on Deployment**

The goal of operationalizing models into apps, products and business processes distinguishes data science.

## Future Outlook

As data analytics matures, data mining and data science will converge further:

- Wider adoption of full-lifecycle data science frameworks that incorporate data mining techniques
- Automation will make sophisticated modelling accessible to business users beyond data scientists
- Expanding real-time and streaming data capabilities will blend historic and current data
- Convergence of capabilities on integrated cloud analytics platforms
- Growth of analytics app development platforms for industrialized deployment
- Evolution of analytics engineering capabilities combining software and ML engineering

Data mining will become deeply assimilated as a core component of business-centric data science capabilities in most organizations.

## Key Takeaways

- Data mining focuses on extracting insights from structured historical data using predictive modeling.
- Data science expands modeling to new data types and sources in an end-to-end framework from acquisition to deployment.
- While data science incorporates data mining techniques, its evolution has been shaped by factors like Big Data, organizational integration, and business alignment.
- Tools, statistical knowledge, modeling algorithms, and emphasis on deriving value from data are common across data mining and data science.
- Increasing automation, cloud platforms, and new data streams will drive convergence of the two fields in the analytics landscape.

**More to read**

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