Linear and Logistic regression are the most basic form of regression which are commonly used. The essential difference between these two is that Logistic regression is used when the dependent variable is binary in nature. In contrast, Linear regression is used when the dependent variable is continuous and nature of the regression line is linear. Regression is a technique used to predict the value of a response (dependent) variables, from one or more predictor (independent) variables, where … [Read more...]

Classification and Regression are two major prediction problems which are usually dealt in Data mining. Predictive modelling is the technique of developing a model or function using the historic data to predict the new data. The significant difference between Classification and Regression is that classification maps the input data object to some discrete labels. On the other hand, regression maps the input data object to the continuous real values. Content: Classification Vs Regression … [Read more...]

The Schema and Instance are the essential terms related to databases. The major difference between schema and instance lies within their definition where Schema is the formal description of the structure of database whereas Instance is the set of information currently stored in a database at a specific time. Instance changes very frequently while the schema acquires changes in a seldom manner. Content: Schema Vs Instance Comparison Chart Definition Key Differences … [Read more...]

A DBMS is a group of interrelated data and a collection of programs to access that data. RDBMS is the variant of DBMS devised to remove the inefficiencies of DBMS. The common difference between DBMS and RDBMS is that DBMS just provide an environment where people could conveniently store and retrieve information with in the presence of redundant data. On the other hand, RDBMS uses normalization to eliminate the data redundancy. DBMS follows a navigational model while RDBMS uses the relational … [Read more...]

Classification and Clustering are the two types of learning methods which characterize objects into groups by one or more features. These processes appear to be similar, but there is a difference between them in context of data mining. The prior difference between classification and clustering is that classification is used in supervised learning technique where predefined labels are assigned to instances by properties, on the contrary, clustering is used in unsupervised learning where similar … [Read more...]

Normalization and denormalization are the methods used in databases. The terms are differentiable where Normalization is a technique of minimizing the insertion, deletion and update anomalies through eliminating the redundant data. On the other hand, Denormalization is the inverse process of normalization where the redundancy is added to the data to improve the performance of the specific application and data integrity. Normalization prevents the disk space wastage by minimizing or … [Read more...]

WHERE and HAVING clause are mainly used in the statement of SQL queries, these allow us to restrict the combination in the result relation through using a specific predicate. The major difference between WHERE and HAVING is that WHERE clause specifies the conditions for selecting the tuples (rows) from the relations, including join conditions if needed. On the other hand, HAVING clause specifies a condition on the groups being selected rather than on individual tuples. SQL stands for … [Read more...]

Data warehouse and Data mart are used as a data repository and serve the same purpose. These can be differentiated through the quantity of data or information they stores. The vital difference between a data warehouse and a data mart is that a data warehouse is a database that stores information-oriented to satisfy decision-making requests whereas data mart is complete logical subsets of an entire data warehouse. In simple words, a data mart is a data warehouse limited in scope and whose data … [Read more...]

Star and snowflake schemas are the most popular multidimensional data models used for a data warehouse. The crucial difference between Star schema and snowflake schema is that star schema does not use normalization whereas snowflake schema uses normalization to eliminate redundancy of data. Fact and dimension tables are essential requisites for creating schema. You can also refer our previously published article on the difference between fact and dimension table to understand it … [Read more...]

Data is raw, unanalyzed, unorganised, unrelated, uninterrupted material which is used to derive information, after analyzation. On the other hand, Information is perceivable, interpreted as a message in a particular manner, which provides meaning to data. Data doesn't interpret anything as it is a meaningless entity, while information is meaningful and relevant as well. Data and Information are different common terms which we frequently use, although there is a general interchangeability … [Read more...]