Sample interview questions: How do you analyze and interpret complex virology data sets?
Sample answer:
Analysis of Complex Virology Data Sets
Data Preprocessing and Cleaning:
- Filter and quality control data to remove outliers and noise.
- Impute missing values using statistical methods or machine learning algorithms.
- Normalize data to eliminate biases or scale differences.
Exploratory Data Analysis (EDA):
- Use descriptive statistics and visualizations (e.g., histograms, scatterplots) to identify trends, patterns, and outliers.
- Perform correlation analysis to assess relationships between variables.
- Conduct dimensionality reduction techniques, such as principal component analysis (PCA) or t-distributed stochastic neighbor embedding (t-SNE), to simplify data structure.
Statistical Modeling:
- Develop statistical models (e.g., regression, classification, machine learning) to identify relationships between variables and make predictions.
- Use cross-validation or other techniques to assess model accuracy and generalizability.
- Apply model optimization techniques to improve model performance.
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