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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Data Mining vs Data Warehousing: 8 Critical Differences

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Analytics Vidhya Last Updated : 27 Jun, 2023
7 min read

The two pillars of data analytics include data mining and warehousing. They are essential for data collection, management, storage, and analysis. Providing insights into the trends, prediction, and appropriate strategy for the company and serving numerous other uses are distinct. Both are associated with data usage but differ from each other. Let’s explore the distinctive features of data mining vs data warehousing in different aspects, such as characteristics, functionalities, challenges, applications, and others.

Table of contents

What is Data Warehousing?

Data warehousing is the data organization and compilation method into a single database for efficient, effortless, centralized usage. It refers to copying data from different organization systems for further processing, such as data cleaning, integration and consolidation. It aids in maintaining the accuracy, consistency and quality of the data and avoids redundancy. Data warehousing also includes sorting the data into a recognizable pattern to interpret its type and format. The data characteristics are non-volatile, integrated, time-variant and subject-oriented data. The processing at the data warehouse is as follows: 

Source → Extract → Transform → Load → Target

What is Data Mining? 

Data mining is the data analysis and extraction method to fetch the information, the functional aspect of the input. It involves predictive analysis and different aspects such as statistics, artificial intelligence, machine learning, natural language processing, etc. Data mining is associated with extracting valid, hidden and useful information that might be previously unknown. The practical application includes fraud detection, building risk models, scientific discovery and trend analysis. 

Data Mining vs Data Warehousing

CriteriaData MiningData Warehousing
DefinitionProcess of discovering patterns in large datasetsProcess of collecting, storing and managing data from various sources
PurposeTo extract useful insights and knowledge from dataTo provide a comprehensive view of an organization’s data
FocusAnalyzing data to identify patterns, correlations and trendsStorage and management of data for reporting and analysis
Source of dataLarge datasets from various sourcesMultiple sources, including internal and external systems
Data processingAdvanced techniques like machine learning algorithmsAggregating, transforming and organizing data
Analysis methodsTechniques such as clustering, classification and regressionQueries, reports and online analytical processing (OLAP)
TimeframeHistorical and current dataHistorical data only

1. Objectives and Focus

The data warehouse and data mining difference concerning objectives and focus is as follows: 

Data Warehousing: Centralized Data Storage and Management

Data warehousing is a storage system that holds much data in one place. Its main goal is to make finding and analyzing the data easy and efficient. Everyone in an organization can access the data to help with their work.

Data Mining: Knowledge Discovery and Pattern Identification

The unprocessed and raw data only hold significance after being processed and that’s how data mining comes into play. It aims to discover the potential of the data for problem-solving and decision-making. It identifies the patterns and relationships and provides output as information.

Data Mining
Source: Hevodata

2. Data Sources and Integration

The difference between data mining and data warehousing in data sources and integration is explained below: 

Data Warehousing: Integration of Structured and Organized Data

Do you think data originates from a single source? No! Data gathering happens from multiple sources, such as applications, organization systems and databases. The integration process involves data extraction and transformation into a specific structured data format, and further sorting of this data is Data Warehousing. The structured and organized data are available in easily interpretable forms such as tables, rows and columns. Serving numerous benefits, data warehousing thus involves the extraction of data from different sources and conversion into the required format for better usefulness.

Data Mining: Extraction of Patterns from Diverse Data Sources

Diverse data sources include data available in unstructured, semi-structured and structured formats. The source of data mining includes sensor data, text documents, databases, social media feeds and other such sources. Based on the query, the relevant data is searched for to gain informational insights into raw and unprocessed data, derivation of relationships and discovery of hidden patterns through statistical analysis and machine learning.

3. Data Structure and Granularity

The data structure and granularity are other aspects that differentiate between data mining and data warehousing: 

Data Warehousing: Aggregated and Summarized Data for Analysis

Data warehousing provides the option to return reports of queries from data. The process is made easy through the accumulation of aggregated and summarized data. It includes using various tools like query and reporting, data visualization, business intelligence, and online analytical processing (OLAP) tools. Common examples of these tools include SQL, Tableau, Oracle Essbase, SAP business objects, Qlik view, SAP business warehouse, IBM Cognos, and others. 

Data Mining: Detailed and Granular Data Exploration

It includes analysis of each data such as transactions, records and events at granular and detailed levels to find unrecognizable patterns at aggregated levels. The applications are primarily beneficial in analyzing complex datasets, deriving logical interpretations from them, and ensuring efficient use of customer data by understanding their behavior and making further predictions. It requires the usage of programming languages like R and Python. Further data processing frameworks like Apache Spark, data science platforms like Rapid Miner, and visualization tools like KNIME find proficient use in the process. 

4. Analytical Techniques and Tools

The difference between data mining and data warehousing in analytics techniques and tools is enlisted below: 

Data Warehousing: OLAP for Reporting

OLAP is significantly involved in reporting and analysis of aggregated data. It is a complex of tools and techniques that performs specific functions. For instance, OLAP cubes are concerned with storage and data organization for analysis, and multidimensional data model functions to data organization into dimensions and measures. It is also responsible for granularity at different levels and allows the selection of specific data subsets by selecting values from different dimensions. Slice and dice operation of OLAP performs the later. 

Data Mining: ML Algorithms and Statistical Analysis

Machine learning algorithms are associated with discovering hidden patterns, relationships and data potential. The algorithms are categorized into groups depending on their functionality. The different categories involve classification, association rule mining, clustering and regression. They can predict results, make data-driven decisions and recognize the association among data from different sources. 

Concerning statistics, descriptive and inferential statistics, correlation analysis and hypothesis testing are of significance in data mining. They measure the importance, check the accuracy, validate results, and quantify the relationships. 

5. Time Dependency and Data Updates

Let us differentiate between data mining and data warehousing with respect to time dependency and data updates below: 

Data warehousing: Supports Historical and Periodic Data Snapshots

A high volume of companies rely on periodical data for their functionality. It regularly raises data storage requirements and creates a timeline with easy access to different periods. The easy access helps in analysis and comparison to identify the trends and patterns. The scheduled data refresh options allow automatic data updates from various sources and segregate the data through data partitioning techniques. It is beneficial in imparting speedy operation, retrieval and analysis.

Figure: Trend analysis through data mining
Source: Wall Street mojo

Data Mining: Focuses on Real-time and Dynamic Data Analysis

Data mining also considers time-dependent data analysis through action over real-time data streams and dynamic datasets such as financial market data, sensor data and social media feeds. It involves streaming analytics that refers to non-stop analysis of continuously flowing data. The data mining is also automated to update the specific new data rather than processing a complete data set. Further, event-based data detection and analysis also help find information from the dynamic data.

6. Usage and Applications

Based on the applicability, the difference between data mining and data warehousing is:

Data Warehousing: Strategic Decision-making and Trend Analysis

Two important factors for data warehousing are decision-making and trend analysis. It provides combined information based on time aspects that allows trend analysis. It helps in pattern identification, which provides the base to formulate a strategy and guide the company toward success.

Data Mining: Predictive Modeling, Market Segmentation, Fraud Detection

It also uses historical data to build predictive models directly applied to trend analysis. It functions by understanding customer behavior and their demands. Data mining supports target-based marketing, where its application of understanding consumer characteristics and preferences plays a crucial role. Utilizing the same features also allows fraud detection based on the history and customer’s identity.

7. Relationship and Integration

Data mining vs data warehousing in regards to relationship and integration: 

Data Warehousing Provides the Foundation for Data Mining

The data collection required to interpret information is found at the data warehouse. It provides specifically formatted data that is easy to work on and visualize. The storage after the accumulation and processing of data helps in the anywhere and anytime functionality of data mining. 

Data Mining Leverages Data from Data Warehousing Systems

Data mining is processing information from the accumulated data. A Data warehouse is a single platform containing information from multiple and distinct sources. The processed, cleansed and transformed data is easy to retrieve and further used for analysis. 

8. Challenges and Considerations

According to the data mining vs data warehousing challenges and considerations, here are some points worth viewing:

Data Quality and Consistency in Data Warehousing

Data quality and consistency is a challenging tasks in data warehousing. The data from different formats, quality, and structures require additional processes such as data duplication, normalization and resolution of inconsistencies. It also involves data cleansing and governance by establishing practices and policies for best practices.

Interpretation and Validation of Data Mining Results

The data has to be interpreted repeatedly according to different contexts. A clear understanding of the problem statement is crucial for accurate results. Cross-validation and verification are crucial while performing data mining owing to sometimes the production of overfitting and biased results. Humans are also assigned to check generated data’s practical applicability and relevance due to often witnessed discrepancies.

Final Verdict

To sum up, regardless of both dealing with data, warehousing and mining are apart from each other. While the former provides a foundation and base for the functionality of data mining, the latter is crucial to impart meaning to warehouse constituents. Data warehousing is responsible for data quality, accessibility, and consistency. Similarly, data mining is associated with leveraging the stored to help guide the company to success. Data mining vs data warehousing hence finds itself distinct yet related to each other while serving the organizations, research and market. If you want to learn both the techniques then our Blackbelt program is the best option for you. Explore the program today!

Frequently Asked Questions

Q1. What is the difference between data and data warehouse?

A. Data refers to any formatted information, while a data warehouse is a centralized data repository used for analysis and reporting.

Q2. What are the different types of data warehouse and data mining?

A. The different types of data warehouses include enterprise data warehouses, operational data stores, and data marts. Different data mining techniques include classification, clustering, regression, and association rule learning.

Q3. What is the difference between data mining and ETL?

A. ETL (extract, transform, load) is moving data from various sources into a data warehouse, while data mining is discovering patterns in large datasets.

Q4. What is the difference between database and data mining?

A. A database is a collection of structured data organized for efficient storage and retrieval, while data mining is analyzing data to extract insights or patterns.

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