Parametric and non-parametric tests in Data science
Parametric and non-parametric tests are two broad categories of statistical tests used to analyze d…
Parametric and non-parametric tests are two broad categories of statistical tests used to analyze d…
Data science is a multiple sub-skills you should to have it starts from Data collection to inform…
In data science, the concepts of sample and population are fundamental for statistical analysis, …
In data science, skewness and kurtosis are two important statistical measures used to describe th…
A z-score is a statistical measure that quantifies the number of standard deviations a data point …
In data science, a distribution refers to the way in which values of a random variable or dataset …
Bootstrapping is a powerful statistical technique used to estimate the distribution of a statistic …
ANOVA, which stands for Analysis of Variance, is a statistical technique used to compare means acro…
The chi-square test is a statistical test used to determine whether there is a significant associat…
Multicollinearity in data science refers to a situation in which two or more predictor variables in…
Covariance and correlation are both measures used to describe the relationship between two variable…
Preparing for a data science interview can be daunting given the breadth of topics covered. Here’s …