Senior Analytics and Data Scientist Interview Questions and Answers

Logistic regression is used when the dependent variable is categorical, while linear regression is used for continuous variables.

• Logistic regression predicts the probability of an event occurring, while linear regression predicts the value of a dependent variable.

• Logistic regression uses a sigmoid function to map the output to a probability value between 0 and 1.

• Linear regression assumes a linear relationship between the dependent and independent variables, while logistic reg...read more

Time series is used when data points are collected over time and have a sequential order, while regression is used for predicting continuous outcomes based on independent variables.

• Time series is used when analyzing data points collected at regular intervals over time.

• Regression models are used when predicting continuous outcomes based on independent variables.

• Time series can capture trends, seasonality, and cyclic patterns in data, while regression may not be able to capture...read more

Bagging and Boosting are ensemble learning techniques used to improve model performance.

• Bagging involves training multiple models on different subsets of the data and averaging their predictions.

• Boosting involves training models sequentially, with each model focusing on the errors of the previous model.

• Bagging reduces variance and overfitting, while boosting reduces bias and underfitting.

• Examples of bagging algorithms include Random Forest and Extra Trees. Examples of boostin...read more

Handle missing values using techniques like imputation, deletion, or modeling.

• Use imputation techniques like mean, median, mode for numerical data

• For categorical data, use mode or create a new category for missing values

• Consider using advanced techniques like KNN imputation or predictive modeling

• Delete rows or columns with high percentage of missing values if appropriate

Different types of Time Series include trend, seasonality, cyclic, and irregular components.

• Trend: Long-term increase or decrease in data over time.

• Seasonality: Repeating patterns or cycles at regular intervals.

• Cyclic: Fluctuations that are not of fixed period.

• Irregular: Random variations in data that cannot be attributed to trend, seasonality, or cyclic patterns.

Time series is a sequence of data points collected at regular time intervals, used to analyze trends and patterns over time.

• Time series data is ordered chronologically

• Commonly used in forecasting future values based on past patterns

• Examples include stock prices, weather data, and sales figures

Structured data is organized and easily searchable, while unstructured data lacks a predefined format.

• Structured data is organized into rows and columns, like a database.

• Unstructured data includes text documents, images, videos, and social media posts.

• Structured data is easier to analyze and query, while unstructured data requires more advanced techniques like natural language processing.

• Examples of structured data include customer information in a CRM system, sales data in a...read more