Updated June 14, 2023
Difference between Data Mining and Statistics
Data analysis is all about analyzing past and present data to predict issues in the future. Organizations are using Data Mining and Statistics to make this data-driven decision which is a core part of Data Science. Data Mining and Statistics are often confused as the same, but it is the wrong notion let us check out if are they really similar or different.
It is the process of extracting previously unknown, comprehensible, and actionable information from large data warehouses and using it to make a crucial business decision. So in data modeling, data from customers are mined to get business insight. The origin of data modeling is statistics, machine learning, and artificial intelligence. In today’s world, all organizations are collecting data from social media, Sensor data, website logs, etc. Almost everything emits data as the use of IoT is increasing, and data mining is the process of extracting useful information from this raw data to predict unknown patterns.
Process of Data Mining:
Data mining process is broken down into 5 stages:
- Data Exploration/ Gathering: Identify data from different data sources and load it to decentralized data warehouses.
- Store and Manage Data: Store the data in distributed storage (HDFS), in-house servers, or a cloud (Amazon S3, Azure).
- Modeling: The business team, Developers will access the data and apply sampling and transformation in data and remove corrupt, irrelevant, inaccurate, and incomplete data.
- Deploying Models: Based on the results from modeled data, sort the data based on users’ expectations or results.
- Visualize Data: Presents the data in graphs or tables or charts, or decision tree format so that end users can understand.
Data Mining Applications:
Data mining is used in many domains following are some highly used domains −
- Market Analysis and Management
- Corporate Analysis & Risk Management
- Fraud Detection
Statistics is the analysis and presentation of numeric facts of data, and it is the core of all the data mining and machine learning algorithms. It provides analytical techniques and tools to apply to large-volume data sets. Statistics include planning, designing, collecting data, analyzing, drawing meaningful interpretations, and reporting the research findings, and due to this statistics is not only limited to a mathematician, business analyst are also using it. To get the desired output or quantify data, statistics uses probability, designing surveys and experiments.
Head to Head Comparison between Data Mining and Statistics
Below are the 11 head-to-head differences between data mining vs statistics.
Key Differences between Data Mining and Statistics
- Data mining is the beginning of data science, and it covers the entire process of data analysis, whereas statistics is the base and core partition of the data mining algorithm.
- Data Mining is an exploratory analysis process in which we first explore and gather the data and build a model on the data to detect the pattern and make theories on them to predict the future outcome or resolve issues. At the same time, statistics is the confirmative process in which first theories are made, and then validation is applied to that theory to test the datasets.
- As day by day, data size is increasing; data format is also changing. Mostly received data is unstructured data which may contain numeric or non-numeric data, and both types of data are used for data mining, but statistics only numeric type of data is used for the probabilistically and mathematical calculation and prediction.
- Data mining is an inductive process that uses an algorithm like a decision tree or clustering algorithm to derive data partition and generate hypotheses from data. In contrast, statistics is a deductive process i.e. it does not involve any predictions.
- Data mining is not much concerned with the collection or gathering of data as it is exploratory data analysis. also, data mining is mostly a software and computational process for discovering patterns on large datasets. In contrast, statistics is more about collecting data to get confirmation of the predicted data. We need to gather data and analyze it to answer questions. Collected data can be Quantitative, Qualitative, Primary, or secondary data.
- Data cleaning in data mining is the first step, as it helps to understand and correct the data quality to get an accurate final analysis. In data cleaning, a user can clean inaccurate or incomplete data. Without proper data quality, your final analysis will suffer in accuracy, or you could potentially arrive at the wrong conclusion.
- Data mining is a process of digging deep into the previously available unknown but actionable information from large databases for using it to make some crucial decisions. It is a confluence of various processes, including statistics, machine learning, database management, artificial intelligence (AI), and data pattern recognition, etc. In contrast, Statistics is an important component of data mining that offers effective analytics techniques and tools for dealing with a large amount of data for benefiting businesses. It is a science of data learning that covers everything from collecting to using data effectively.
- It uses predictive analytics to run scenarios that help decide future actions. On the other hand, statistics give breathing into lifeless data.
- Some of the popular evolving trends in Data mining are application exploration, visual data mining, biological data mining, web mining, software mining, distributed data mining, real data mining, and lots more. And Statistics help to identify new patterns in the available unstructured data.
Data Mining vs Statistics Comparision Table
The differences between Data Mining vs Statistics are explained in the points presented below:
|Explore and gather data first, builds a model to detect patterns, and make theories.||It provides theories to test using statistics.|
|Data used is Numeric or Non numeric.||Data used is Numeric.|
|Inductive Process (Generation of new theory from data)||Deductive Process (Does not involve making any predictions)|
|Data collection is less important.||Data collection is more important.|
|Data Cleaning is done in data mining.||Clean data is used to apply statistical methods.|
|Needs less user interaction to validate the model; hence, easy to automate.||Needs user interaction to validate the model; hence, difficult to automate.|
|Suitable for large data sets||Suitable for smaller data sets|
|It’s an algorithm that learns from data without using any programming rule.||Formalization of relationship in data in the form of a mathematical equation|
|Use heuristic thinking (rules used to form judgments and make decisions)||Does not have scope for heuristic thinking.|
|Classification, Clustering, Neural network, Association, Estimation, Sequence-based analysis, Visualization||Descriptive Statistical, Inferential Statistical|
|Financial Data Analysis, Retail Industry, Telecommunication Industry, Biological Data Analysis, Certain Scientific Applications, etc.||Demography, Actuarial Science, Operation Research, Biostatistics, Quality Control, etc.|
To conclude, in any organization due to the emergence of big data with big volume and different velocities, data plays an important role and predict outcomes data mining and statistics are an integral part. Data mining will always use statistical thinking to draw output; hence, both Data Mining and Statistics will grow inevitably in the near future. And it is using statistics on large data users/organizations need to use data mining thinking and approaches.
This has been a guide to Data Mining vs Statistics, their Meaning, Head to Head Comparison, Key Differences, Comparison Table, and Conclusion. You may also look at the following articles to learn more –