10+ RITS & Associates Interview Questions and Answers

Rank assigns unique ranks to each row based on the order specified, while Dense Rank assigns consecutive ranks without gaps.

• Rank may have gaps in ranks if there are ties, while Dense Rank does not have gaps.

• Rank function is used to assign a unique rank to each row based on the specified order, while Dense Rank function assigns consecutive ranks.

• Example: If three rows have the same value and are ranked 1, 1, and 2 using Rank, they will be ranked 1, 1, and 2 using Dense Rank.

Stemming reduces words to their root form, while lemmatization reduces words to their dictionary form.

• Stemming chops off prefixes or suffixes to get the root form (e.g. 'running' becomes 'run')

• Lemmatization uses vocabulary analysis to reduce words to their base form (e.g. 'better' becomes 'good')

• Lemmatization is more accurate but slower than stemming

• Stemming is faster but may not always result in a valid word

R-Squared measures the proportion of variance explained by the model, while Adjusted R-Squared adjusts for the number of predictors in the model.

• R-Squared increases as more predictors are added to the model, even if they are not relevant.

• Adjusted R-Squared penalizes for adding irrelevant predictors, making it a more reliable measure of model fit.

• R-Squared can never decrease when adding predictors, while Adjusted R-Squared may decrease if the added predictors do not improve th...read more

Series is a one-dimensional labeled array while Dataframe is a two-dimensional labeled data structure.

• Series can hold data of any type while Dataframe is a collection of Series.

• Dataframe is like a table with rows and columns, while Series is like a single column of that table.

• Dataframe is more versatile and powerful compared to Series.

• Example: Series - a column of employee names. Dataframe - a table with columns for employee names, ages, and salaries.

Analyzing datasets and building a Machine Learning model for Associate Data Scientist role.

• 1. Explore and understand the datasets to identify patterns and relationships.

• 2. Preprocess the data by handling missing values, encoding categorical variables, and scaling numerical features.

• 3. Split the data into training and testing sets for model evaluation.

• 4. Choose a suitable Machine Learning algorithm based on the nature of the problem (classification, regression, clustering, etc...read more

Central Limit Theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases.

• The Central Limit Theorem is a fundamental concept in statistics that states that the sampling distribution of the sample mean will be approximately normally distributed, regardless of the shape of the population distribution, as the sample size increases.

• It is important because it allows us to make inferences about a population mean bas...read more

SQL query to join two tables

• Use JOIN keyword to combine rows from two or more tables based on a related column between them

• Specify the columns to be selected from each table

• Use ON keyword to specify the join condition

Assumptions of linear regression are important for the model to be valid and reliable.

• Linear relationship between independent and dependent variables

• Independence of residuals (errors)

• Homoscedasticity (constant variance of residuals)

• Normality of residuals

• No multicollinearity among independent variables

Random Forest is an ensemble learning algorithm that creates multiple decision trees and combines their predictions.

• Random Forest is a collection of decision trees that are trained on random subsets of the data.

• Each tree in the Random Forest independently predicts the outcome, and the final prediction is made by averaging the predictions of all trees.

• Random Forest is used for classification and regression tasks, and it helps reduce overfitting compared to a single decision tr...read more

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables.

• Linear regression is used to predict the value of a dependent variable based on the value of one or more independent variables.

• It assumes a linear relationship between the independent and dependent variables.

• The goal of linear regression is to find the best-fitting line that minimizes the sum of squared differences between the observed va...read more