Python Data-Science Interview Jupyter Notebook

This repository supports the Medium article
“Top 20 Python Data-Science Interview Questions 2025 + 5 Essential Concepts Every Data Scientist Should Know.”

It delivers fully executed Jupyer notebook with step-by-step answers for every question and concept listed below.

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Top 20 Interview Questions Covered

1. Difference between a Python list and a tuple
2. Why NumPy arrays outperform Python lists
3. List & dictionary comprehensions
4. Lambda functions and common use-cases
5. Distinction between return and yield
6. .loc vs .iloc in pandas
7. Handling missing values in a DataFrame
8. Merge, join, and concat in pandas (all join types)
9. Using groupby for aggregations
10. Concept of broadcasting in NumPy
11. Counting word frequencies in text
12. Reversing a string efficiently
13. The roles of __init__ and self in a class
14. Building and applying decorators
15. Introduction to metaclasses
16. Practical monkey-patching and when to use it
17. Principles and code for binary search
18. Removing duplicates from a sorted list in-place
19. Finding the missing number in a 1‒n array
20. Detecting a palindrome (case-/symbol-insensitive)

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Five Essential Concepts Explained Computationally

#	Concept	Why It Matters
1	Central Limit Theorem	Justifies normal-based inference even for non-normal data.
2	p-Value	Quantifies evidence against the null hypothesis.
3	Type I (α), Type II (β) Errors & Power	Specify reliability of statsitical tests.
4	Confusion Matrix	Delivers actionable precision, recall, and F1 metrics.
5	Cross-Validation	Provides robust model evaluation.

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