Python Interview Questions for Data Analysts & Scientists: Statistical Testing to Model Evaluation!

Here are 5 fresh Python interview questions for data analysts and scientists, with detailed answers and examples:

1️⃣ How do you perform hypothesis testing using Python?

Hypothesis testing involves comparing a null hypothesis against an alternative hypothesis using statistical tests.

Libraries like SciPy provide functions for t-tests, chi-square tests, etc.

Example using a t-test:

from scipy import stats
import numpy as np

# Sample data: two independent samples

# Perform t-test
print("t-statistic:", t_stat, "p-value:", p_val)

This helps decide if the differences between groups are statistically significant.

2️⃣ How can you integrate SQL with Python for data extraction?

Use libraries like SQLAlchemy or pandas' read_sql() to query databases directly from Python.

Example using pandas and SQLite:

import pandas as pd
import sqlite3

# Connect to SQLite database
query = "SELECT * FROM sales_data"

This integration streamlines data extraction and analysis in one environment.

3️⃣ What is Principal Component Analysis (PCA) and how is it used for dimensionality reduction?

PCA reduces the dimensionality of data by transforming original features into a new set of uncorrelated variables (principal components).

It helps in visualizing data, speeding up algorithms, and reducing noise.

Example using scikit-learn:

import numpy as np

# Sample dataset

pca = PCA(n_components=1)
print("Reduced Data:\n", X_reduced)

4️⃣ How do you handle imbalanced datasets in machine learning?

Techniques include resampling (oversampling minority class or undersampling majority class), synthetic data generation (SMOTE), and using appropriate metrics.

Example using SMOTE:

from collections import Counter
import numpy as np

# Example data

print("Original distribution:", Counter(y))

smote = SMOTE(random_state=42)
print("Resampled distribution:", Counter(y_res))

5️⃣ How do you evaluate a machine learning model using cross-validation in Python?

Cross-validation splits data into multiple folds to train and test the model iteratively, ensuring robust evaluation.

Use scikit-learn’s cross_val_score for this purpose.

Example:

iris = load_iris()

model = LogisticRegression(max_iter=200)
scores = cross_val_score(model, X, y, cv=5)
print("Cross-validation scores:", scores)

This method provides insights into model performance across different subsets of data.

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