Cluster analysis is a statistical method for processing data. It works by organizing items into groups, or clusters, on the basis of how closely associated they are.

The objective of cluster analysis is to find similar groups of subjects, where “similarity” between each pair of subjects means some global measure over the whole set of characteristics.

Clustering is measured using intraplate and inter-cluster distance.

• Intra-cluster distance is the distance between the data points inside the cluster. If there is a strong clustering effect present, this should be small (more homogenous).
• Inter-cluster distance is the distance between data points in different clusters. Where strong clustering exists, these should be large (more heterogeneous).

They are different types of clustering methods that we use, including:

• Partitioning methods
• Hierarchical clustering
• Fuzzy clustering
• Density-based clustering
• Model-based clustering

Factorial analysis is a technique that is used to reduce a large number of variables into fewer numbers of factors.  This technique extracts maximum common variance from all variables and puts them into a common score.

There are different types of methods used to extract the factor from the data set:

• Principal component analysis: This is the most common method used by researchers.  PCA starts extracting the maximum variance and puts them into the first factor.  After that, it removes that variance explained by the first factors and then starts extracting maximum variance for the second factor.  This process goes to the last factor.
• Common factor analysis: The second most preferred method by researchers, it extracts the common variance and puts them into factors.  This method does not include the unique variance of all variables.  This method is used in SEM.
• Image factoring: This method is based on a correlation matrix.  OLS Regression method is used to predict the factor in image factoring.
• Maximum likelihood method: This method also works on correlation metrics but it uses the maximum likelihood method to factor.
• Other methods of factor analysis: Alfa factoring outweighs least squares.  Weight square is another regression-based method that is used for factoring.

A decision tree is a flowchart-like structure in which each internal node represents a “test” on an attribute (e.g., whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes).

The paths from root to leaf represent classification rules:

• A decision tree consists of three types of nodes
• Decision nodes – typically represented by squares
• Chance nodes – typically represented by circles
• End nodes – typically represented by triangles

There are two main types of decision trees that we have:

1. Categorical variable decision tree

A categorical variable decision tree includes categorical target variables that are divided into categories. For example, the categories can be yes or no. The categories mean that every stage of the decision process falls into one of the categories, and there are no in-betweens.

2. Continuous variable decision tree

A continuous variable decision tree is a decision tree with a continuous target variable. For example, the income of an individual whose income is unknown can be predicted based on available information such as their occupation, age, and other continuous variables.

Interview techniques are the practices you follow during an interview to convince hiring managers that you’re the best candidate for the role.

Types of interview techniques:

• Video interview techniques
• Phone interview
• One-one-one in-person interview
• Group interview
• STAR technique
• Panel interview