Difference Between Data Mining and Data Analysis Last Updated : 11 Jan, 2024 Comments Improve Suggest changes Like Article Like Report 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 make 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. Below is a table of differences between Data Mining and Data Analysis : Based onData MiningData AnalysisDefinitionIt is the process of extracting important pattern from large datasets.It is the process of analysing and organizing raw data in order to determine useful informations and decisionsFunctionIt 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 modelsVisualizationIt generally does not require visualizationSurely requires Data visualization.GoalPrime goal is to make data usable.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 Comment More infoAdvertise with us Next Article Difference Between Data Mining and Data Analysis Y yash41997 Follow Improve Article Tags : Computer Subject DBMS Difference Between Similar Reads Difference Between Data Analysis and Data Interpretation Data analysis and Data Interpretation come pretty close; the only difference is in their roles in the data-driven process. In the process, it is all about the systematic inspection, cleaning, transformation, and modelling of the data to discover useful information, patterns, or trends—it mainly diss 6 min read Difference Between Big Data and Data Mining Big Data: It is huge, large or voluminous data, information or the relevant statistics acquired by the large organizations and ventures. Many software and data storage created and prepared as it is difficult to compute the big data manually. It is used to discover patterns and trends and make decisi 3 min read Difference between Big Data and Data Analytics 1. Big Data: Big data refers to the large volume of data and also the data is increasing with a rapid speed with respect to time. It includes structured and unstructured and semi-structured data which is so large and complex and it cant not be managed by any traditional data management tool. Special 4 min read Difference Between Data Visualization and Data Analytics Data Visualization: Data visualization is the graphical representation of information and data in a pictorial or graphical format(Example: charts, graphs, and maps). Data visualization tools provide an accessible way to see and understand trends, patterns in data and outliers. Data visualization too 3 min read Difference between a Data Analyst and a Data Scientist Nowadays as we know the roles of Data analyst and Data scientist are often used in extracting insights from the data. Both professionals work with data to get various insights, but their responsibilities, skill sets, and the depth of their involvement in the data analytics process differ significant 5 min read Like