Ipsos Interview Questions, Process, and Tips

30 general aptitude question and one email writing

Python numpy and pandas

Q1. Project from previous company
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Developed a survey programming tool to streamline data collection process.
• Created user-friendly interface for survey creation

• Implemented logic for skip patterns and branching

• Integrated with data analysis software for seamless data transfer
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Q2. All question were on my previous company projects
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Q1. State linear regression assumptions.
Ans. 
Linear regression assumptions include linearity, independence, homoscedasticity, and normality.
• Linearity: The relationship between the independent and dependent variables is linear.

• Independence: The residuals are independent of each other.

• Homoscedasticity: The variance of the residuals is constant across all levels of the independent variables.

• Normality: The residuals are normally distributed.

• Example: If we are predict...read more
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Q2. What is multicollinearity? How to tackle this condition?
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Multicollinearity is a phenomenon in which two or more predictor variables in a regression model are highly correlated.
• Multicollinearity can lead to unstable estimates of the coefficients and make it difficult to determine the effect of each predictor variable on the outcome.

• One way to tackle multicollinearity is to identify the highly correlated variables and consider removing one of them from the model.

• Another approa...read more
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Q3. What are the components of time series analysis?
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Time series analysis components include trend, seasonality, cyclicality, and irregularity.
• Trend: Long-term movement or direction of the data.

• Seasonality: Regular patterns that occur at specific intervals.

• Cyclicality: Repeating patterns that are not necessarily at fixed intervals.

• Irregularity: Random fluctuations or noise in the data.

• Examples: Trend in stock prices, seasonality in retail sales, cyclicality in economic c
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Q4. What are the preventive measures if regression assumptions are not met?
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Preventive measures for regression assumptions not met
• Check for multicollinearity among independent variables

• Transform variables if they are not normally distributed

• Consider using non-parametric regression methods

• Use robust regression techniques to handle outliers

• Collect more data to improve model performance
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Q1. How to tackle with missing values ?
Ans. 
Handling missing values is crucial in data analysis. Various techniques like imputation, deletion, or prediction can be used.
• Use imputation techniques like mean, median, mode to fill in missing values.

• Consider using predictive modeling to estimate missing values based on other variables.

• Delete rows or columns with a high percentage of missing values if they cannot be accurately imputed.

• Use advanced techniques like K-ne...read more
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Q2. What is the difference between tuple and list ?
Ans. 
Tuple is immutable and fixed in size, while list is mutable and can change in size.
• Tuple is created using parentheses, while list is created using square brackets.

• Tuple elements can be of different data types, while list elements are usually of the same data type.

• Tuple is faster than list for iteration and accessing elements.

• Example: tuple = (1, 'a', True), list = [1, 2, 3]
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Q3. How to detect outlier ?
Ans. 
Outliers can be detected using statistical methods like Z-score, IQR, or visualization techniques like box plots.
• Calculate Z-score for each data point and identify points with Z-score greater than a certain threshold (usually 3 or -3).

• Use Interquartile Range (IQR) to identify outliers by determining data points that fall below Q1 - 1.5 * IQR or above Q3 + 1.5 * IQR.

• Visualize the data using box plots and identify points...read more
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Q1. Basics about market research and how to analyze data even when it's limited
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Q1. About past work experience
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Q1. Why do you think you are a fit ?
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Written test covering economic questions

Q1. Please introduce yourself in mandarin
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