Data Analysis Interview Question and Answers

1. What is the main purpose of exploratory data analysis (EDA)?

   The main purpose of EDA is to analyze and summarize data to gain insights, identify patterns, and detect anomalies.

2. What is the difference between data cleaning and data transformation in the data preparation process?

   Data cleaning involves identifying and correcting errors or inconsistencies in the data, while data transformation involves converting or reshaping the data to suit analysis requirements.

3. What is the formula to calculate the mean of a dataset?

   The mean of a dataset can be calculated by summing all the data points and dividing by the total number of data points.

4. How do you identify outliers in a dataset?

    Outliers can be identified using statistical methods such as the Interquartile Range (IQR) or z-score to detect data points that deviate significantly from the rest of the data.

5. What is the purpose of data visualization in data analysis?

    Data visualization helps in presenting complex data in a graphical format, making it easier to understand patterns, trends, and relationships within the data.

6. What is the difference between correlation and causation?

   Correlation indicates a statistical relationship between two variables, whereas causation implies that one variable directly influences the other.

7. How do you handle missing data in a dataset?

   Missing data can be handled by techniques such as imputation (replacing missing values with estimated values) or excluding incomplete records if they won't significantly impact the analysis.

8. What is the purpose of a box plot in data analysis?

    A box plot (box-and-whisker plot) provides a visual representation of the data's distribution, including median, quartiles, and potential outliers.

9. What is the difference between supervised and unsupervised learning in machine learning?

   Supervised learning involves training a model with labeled data to predict specific outcomes, while unsupervised learning analyzes unlabeled data to find patterns and groupings.

10. How do you measure the performance of a classification model?

    The performance of a classification model is assessed using metrics like accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC).