Most Common Interview Questions (Data Analyst/Scientist) and general BQ
Top Common BQ
- Talk about yourself (1. YOE 2.Top Skills tailored to the job/industry 3. maybe a line of what you do outside the work)
- Why you choose our company? why do you want to apply to this job? (talk about how you align their company culture/value/goal and the job)
- Why should we hire you? Why you are a good fit? (like previous question, talk about your passion for the company but this one focus more on your skillset, and the problem you can solve for the company.
- What is your greatest strength? (humble, relevant to the job, and with example)
- What is your biggest weakness? (better not related with the job; how you work on your weakness)
- How do you deal with stressful situations? Whats the most challenge problem you had, and how you overcome it?
- Whats your greatest accomplishment? (relevant to the job, and be detailed with How)
- Whats your long-term goal? Where do you see yourself in 5 years?
- Do you have any question? (Must ask the interviewer something to show you are passionate to the company; things can be like: what do you think is the best thing about working at the company)
- How you prioritize a task over others?
- What will be your action if one of your project’s deadline is shortened?
Technic that you may follow to answer these kind of question:
STAR method
- (S) Situation — What’s the context? Describe the situation or the background first.
- (T) Task — Talk about your responsibilities or the tasks you had to complete (i.e. what was the challenge for the specific task?)
- (A) Action — How did you fix the situation? Describe your process and the steps you took.
- (R) Results — Describe the results of your actions. If possible, use numbers or hard data (e.g. by what % did you increase the overall sales? What changed?).
Useful examples:
https://www.indeed.com/career-advice/interviewing/how-to-use-the-star-interview-response-technique
Common Data Science/Stat interview Questions
1. Explain what a p-value is and how it is used in hypothesis testing.
2. What is the difference between supervised learning and unsupervised learning? Give examples of each. (KNN vs K-Mean)
3. What is overfitting in machine learning, and how can you prevent it?
4. Can you explain the curse of dimensionality?
5. What are the steps involved in data preprocessing?
6. What is feature engineering, and why is it important in machine learning?
7. What is cross-validation, and why is it useful in model evaluation?
8. What are precision and recall, and how are they related to the concept of a confusion matrix?
9. What is the bias-variance trade-off, and how does it impact model performance?
10. Explain the concept of regularization in machine learning.
11. What is the purpose of a decision tree in classification problems, and how does it work?
12. What are outliers, and how can they affect your analysis? How do you handle outliers in your data ?
13. Can you explain the difference between classification and regression ?
14. What is a recommendation system, and how does it work ?
15. How do you handle missing data in a dataset ?
16. What is gradient descent, and how is it used in training machine learning models ?
17. Explain the concept of clustering. What are some popular clustering algorithms ?
18. What is the ROC curve, and what does it represent in binary classification problems ?
19. How do you handle imbalanced datasets in classification ?
20. Can you discuss the differences between L1 and L2 regularization in linear regression ?
21. When to use Xgboost vs Random Forest
I think its best to practice to answer these questions before the interviews, and you easily find these answer by Google or ChatGPT, good luck !!!
