10 Data Analyst Interview Questions to Land a Job in 2024
What is hypothesis testing in data analysis?
Hypothesis testing in data analysis is a statistical method used to determine if there is enough evidence in a sample data to draw conclusions about a population6. It involves formulating two competing hypotheses, the null hypothesis (H0) and the alternative hypothesis (Ha), and then collecting data to assess the evidence6. The process helps analysts make decisions about population parameters based on sample data, allowing them to infer conclusions and make data-driven decisions.
How does one handle missing data in datasets?
To handle missing data in datasets, one can use various methods such as removal, imputation, or using advanced algorithms. Removal involves dropping rows or columns with missing values, while imputation fills missing values with mean, median, or mode. Advanced algorithms like K-Nearest Neighbors (KNN) or Multiple Imputation by Chained Equations (MICE) can also be used to estimate and fill in missing values. The choice of method depends on the problem statement and the variable's distribution.
What techniques are used to detect and manage outliers?
Outlier detection techniques include statistical methods like Z-score and Interquartile Range (IQR), as well as visualization tools such as scatter plots and box plots. Outliers can be managed through methods like removal, transformation, or applying robust statistical methods. Handling outliers requires careful consideration, as they may represent valuable insights or errors in data collection.