Data Mining and Data Analysis Definitions and Differences

Data Mining and Data Analysis Definitions and Differences
Share With Friends:

Data Mining and Data Analysis Definitions and Differences, all of this information have been mentioned in this article.

Data Analysis Definition/Meaning

Data analysis is the process of collecting, modeling, and analyzing data to extract insights that support decision-making. There are several methods and techniques to perform analysis depending on the industry and the aim of the analysis.

All of these different data analysis approaches are centered on two main areas: quantitative and qualitative research methodologies.

Gaining a deeper understanding of diverse data analysis methodologies, quantitative research methods, and qualitative insights can offer your data analysis efforts a more clearly defined direction, so it’s worth taking the time to soak up this information. In addition, you’ll be able to generate a thorough analytical report that will speed up your analysis.

See Also: Cloud Computing certifications, Training & Course

Types Of Data Analysis Methods

  1. Descriptive analysis – What happened.
  2. Exploratory analysis – How to explore data relationships.
  3. Diagnostic analysis – Why it happened.
  4. Predictive analysis – What will happen.
  5. Prescriptive analysis – How will it happen.

Data Mining Definition/Meaning

Data mining could be called a subset of Data Analysis. It is the exploration and analysis of huge knowledge to find important patterns and rules. 

Data mining could also be a systematic and successive method of identifying and discovering hidden patterns and data throughout a big dataset. Moreover, it is used to build machine learning models that are further used in artificial intelligence. 

Mining of Data involves effective data collection and warehousing as well as computer processing. It makes use of sophisticated mathematical algorithms for segmenting the data and evaluating the probability of future events.

Data Mining Techniques/Methods

  1. Statistics
  2. Clustering
  3. Visualization
  4. Decision Tree
  5. Association Rules
  6. Neural Networks
  7. Classification

Difference Between Data Mining and Data Analysis

Data Mining vs Data Analysis

1. Data Analysis:

Data Analysis involves extraction, cleaning, transformation, modeling and visualization of data with an objective to extract important and helpful information which can be additional helpful in deriving conclusions and making choices. 

The main purpose of data analysis is to search out some important information in raw data so the derived knowledge is often used to create vital choices. 

2. Data Mining : 

Data mining could be called as a subset of Data Analysis. It is the exploration and analysis of huge knowledge to find important patterns and rules. 

Data mining could also be a systematic and successive method of identifying and discovering hidden patterns and data throughout a big dataset. Moreover, it is used to build machine learning models that are further used in artificial intelligence.  

Data Mining vs Data Analysis

Below is a table of differences between Data Mining and Data Analysis : 

Based onData MiningData Analysis
DefinitionIt is the process of extracting important patterns from large datasets.It is the process of analyzing and organizing raw data in order to determine useful information and decisions
FunctionIt is used in discovering hidden patterns in raw data sets.In this, all operations are involved in examining data sets to fine conclusions.
Data setIn this data set are generally large and structured.Dataset can be large, medium, or small, Also structured, semi-structured, unstructured.
ModelsOften require mathematical and statistical modelsAnalytical and business intelligence models
VisualizationIt generally does not require visualizationSurely requires Data visualization.
GoalThe prime goal is to make data useable.It is used to make data-driven decisions.
Required KnowledgeIt involves the intersection of machine learning, statistics, and databases.It requires the knowledge of computer science, statistics, mathematics, subject knowledge Al/Machine Learning.
Also known asIt is also known as Knowledge discovery in databases.Data analysis can be divided into descriptive statistics,  exploratory data analysis, and confirmatory data analysis.
 
OutputIt shows the data tends and patterns.The output is verified or discarded hypothesis

Differences Between Data Mining and Data Science

Below is a table of differences between Data Science and Data Mining:

S.No.Data ScienceData Mining
1Data Science is an area.Data Mining is a technique.
2It is about the collection, processing, analyzing, and utilizing data in various operations. It is more conceptual.It is about extracting vital and valuable information from the data.
3It is a field of study just like Computer Science, Applied Statistics, or Applied Mathematics.It is a technique that is a part of the Knowledge Discovery in DataBase processes (KDD).
4The goal is to build data-dominant products for a venture.The goal is to make data more vital and usable i.e. by extracting only important information.
5It deals with all types of data i.e. structured, unstructured or semi-structured.It mainly deals with the structured forms of the data.
6It is a superset of Data Mining as data science consists of Data scrapping, cleaning, visualization, statistics, and many more techniques.It is a sub-set of Data Science as mining activities that is in a pipeline of Data science.
7It is mainly used for scientific purposes.It is mainly used for business purposes.
8It broadly focuses on the science of the data.It is more involved with the processes.

See Also: What is Data Science | How to Become a Data Scientist 2021

Image source: freepik.com

Share With Friends:

Leave a Reply

Your email address will not be published. Required fields are marked *