Analytics is most interrelated in Human resources, recruitment and hiring. HR analytics includes many terms like data mining, predictive analytics and so on.
HR analytics: HR analytics is nothing but application of essential data mining and business analytics techniques to talent data.
Predictive analytics: Predictive analytics includes different techniques like statistics modeling, data mining, artificial intelligence and machine learning to scrutinize existing data and make predictions about the coming event.
- Data mining: Data mining is searching for the data and converting them into concrete information.
- Machine learning: Machine learning is a representation refers to the Artificial Intelligence (AI) which enables to get a useful tool to explore candidates work history and their profile.
- Descriptive analytics: Descriptive analytics mines historical performance data to look for the reasons behind the past success or failure.
- Cost modeling: Most modeling helps in recruitment and on-boarding costs, estimated time required for an employee to attain maximum productivity, compensation, employee turnover, and overall productivity costs.
- Decision tree: A decision tree supports in making predictions.
- R: R is the most attractive tool for data scientists for statistical visualization and computation.
- Structured data vs. unstructured data: Usually two types of data in the HR analytics domain structured and unstructured data.
- Multivariate analysis: Multivariate analysis is a statistical analysis procedure which involves analyzing of multiple independent (or predictor) variables with multiple dependent (outcome or criterion) variables when you want to predict how age and engagement levels influence someones compensation and using matrix algebra (most multivariate analyses has a correlation).
- Pruning: Pruning is the concept is associated with the decision tree.
- Quantitative scissors: Quantitative scissors is a phrase used by data scientists to describe a moment when an employee begins to be profitable.