Top Data Science Interview Questions and Answers
🔹 General Data Science Questions
1. What is Data Science?
Answer:
Data Science is a multidisciplinary field that uses statistical methods, algorithms, and machine learning techniques to extract knowledge and insights from structured and unstructured data.
2. What is the difference between Data Science, Data Analytics, and Machine Learning?
Answer:
Data Science: End-to-end process of extracting insights from data.
Data Analytics: Focuses on analyzing data sets to summarize their characteristics.
Machine Learning: Subfield of Data Science involving training models to make predictions or decisions.
📊 Statistics & Probability
3. What is the Central Limit Theorem (CLT)?
Answer:
The CLT states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the original distribution of the data.
4. What is p-value?
Answer:
A p-value indicates the probability of observing the given results when the null hypothesis is true. A small p-value (< 0.05) typically leads to rejecting the null hypothesis.
5. Explain Type I and Type II errors.
Answer:
Type I Error (False Positive): Rejecting a true null hypothesis.
Type II Error (False Negative): Failing to reject a false null hypothesis.
🤖 Machine Learning
6. What is the difference between supervised and unsupervised learning?
Answer:
Supervised Learning: Labeled data; used for classification and regression.
Unsupervised Learning: Unlabeled data; used for clustering and dimensionality reduction.
7. How do you handle overfitting in a model?
Answer:
Cross-validation
Regularization (L1, L2)
Pruning (for trees)
Reducing model complexity
Early stopping
8. What are precision and recall?
Answer:
Precision: TP / (TP + FP) → How many predicted positives are actual positives.
Recall: TP / (TP + FN) → How many actual positives were correctly predicted.
9. What is the difference between bagging and boosting?
Answer:
Bagging: Reduces variance by training models in parallel (e.g., Random Forest).
Boosting: Reduces bias by training models sequentially, each learning from the previous (e.g., XGBoost, AdaBoost).
10. What is regularization?
Answer:
Regularization adds a penalty term to the loss function to prevent overfitting by discouraging overly complex models (L1 = Lasso, L2 = Ridge).
🧪 Data Analysis & SQL
11. What steps would you follow in Exploratory Data Analysis (EDA)?
Answer:
Understand the dataset
Handle missing values and outliers
Summary statistics
Univariate and bivariate analysis
Data visualization (histograms, boxplots, heatmaps)
12. Write a SQL query to find the second highest salary from a table.
Answer:
SELECT MAX(salary) AS SecondHighest
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);
📈 Model Evaluation
13. What is cross-validation?
Answer:
Cross-validation is a technique to assess how a model generalizes to an independent dataset. The most common is k-fold cross-validation, which splits data into k subsets and rotates training/validation.
14. What is ROC-AUC?
Answer:
ROC-AUC measures the ability of a classifier to distinguish between classes. AUC represents the area under the ROC curve. A value closer to 1 indicates better performance.
🧠 Deep Learning (Basics)
15. What is the difference between CNN and RNN?
Answer:
CNN (Convolutional Neural Network): Used for spatial data like images.
RNN (Recurrent Neural Network): Designed for sequential data like time series or text.
📂 Case Study / Business Sense
16. How would you approach a customer churn prediction problem?
Answer:
Understand the business goal
Collect and preprocess customer data
Feature engineering (e.g., last purchase, activity level)
Model building (classification)
Evaluation using precision, recall, F1-score
💻 Programming / Python
17. How would you handle missing values in a dataset using Python?
Answer:
# Drop missing values
df.dropna()
# Fill with mean
df.fillna(df.mean())
# Fill with forward fill
df.fillna(method='ffill')
18. What are lambda functions in Python?
Answer:
Anonymous functions used for short, one-line expressions.
square = lambda x: x**2
print(square(5)) # Output: 25
🔍 Behavioral
19. Tell me about a challenging data science project you worked on.
Answer Tip:
Use the STAR method – describe the Situation, Task, Action, and Result. Focus on problem-solving, technical decisions, and impact.
❓ Bonus: Trick/Conceptual Question
20. Why is accuracy not a good metric for imbalanced datasets?
Answer:
Because the model can get high accuracy by predicting the majority class always. In such cases, use metrics like precision, recall, F1-score, or ROC-AUC.
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