FAQ on Data Analytics

In KNN imputation, the missing attribute values are imputed by using the attributes value that are most similar to the attribute whose values are missing. By using a distance function, the similarity of two attributes is determined. KNN imputation, or k-nearest neighbors imputation, is a technique used to fill in missing values in a dataset … Read more

The missing patterns that are generally observed are Missing completely at random Missing at random Missing that depends on the missing value itself Missing that depends on unobserved input variable In data analytics interviews, when asked about missing patterns commonly observed in datasets, it’s essential to demonstrate a solid understanding of the challenges related to … Read more

Hadoop and MapReduce is the programming framework developed by Apache for processing large data set for an application in a distributed computing environment. The correct answer to this question is Apache Hadoop. Hadoop is an open-source framework developed by the Apache Software Foundation for distributed storage and processing of large datasets across clusters of computers … Read more

Some of the common problems faced by data analyst are Common misspelling Duplicate entries Missing values Illegal values Varying value representations Identifying overlapping data Certainly! Here are some common problems faced by data analysts: Data Quality Issues: Incomplete, inaccurate, or inconsistent data can pose significant challenges to analysis and decision-making. Data Cleaning and Preprocessing: Before … Read more

The difference between data mining and data profiling is that Data profiling: It targets on the instance analysis of individual attributes. It gives information on various attributes like value range, discrete value and their frequency, occurrence of null values, data type, length, etc. Data mining: It focuses on cluster analysis, detection of unusual records, dependencies, … Read more