Compunnel Data Analyst Interview Questions, Process, and Tips

Measures of Dispersion are used to describe the spread of data around the central tendency.

• Measures of Dispersion include Range, Variance, Standard Deviation, and Interquartile Range.

• Range is the difference between the maximum and minimum values in a dataset.

• Variance measures how far each value is from the mean.

• Standard Deviation is the square root of the variance.

• Interquartile Range is the difference between the 75th

Linear regression is used for continuous data while logistic regression is used for categorical data.

• Linear regression predicts a continuous outcome while logistic regression predicts a probability of an event occurring.

• Linear regression uses a straight line to fit the data while logistic regression uses an S-shaped curve.

• Linear regression is used for predicting values like house prices while logistic regression is use...read more

Feature selection is the process of selecting relevant features from a dataset, while feature engineering involves creating new features.

• Feature selection helps to reduce the dimensionality of the dataset and improve model performance.

• Feature engineering involves transforming or combining existing features to create new ones that may be more informative.

• Examples of feature engineering include creating interaction terms...read more

Gradient Descent is an optimization algorithm used to minimize the cost function of a machine learning model.

• Gradient Descent is used in machine learning to find the optimal parameters of a model by minimizing the cost function

• It works by iteratively adjusting the parameters in the direction of steepest descent of the cost function

• There are two types of Gradient Descent: Batch Gradient Descent and Stochastic Gradient D...read more

There are three types of machine learning: supervised, unsupervised, and reinforcement learning.

• Supervised learning involves training a model on labeled data to make predictions on new data. Example: predicting house prices based on features like location, size, etc.

• Unsupervised learning involves finding patterns in unlabeled data. Example: clustering customers based on their purchasing behavior.

• Reinforcement learning ...read more