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.
- Difference between a Python list and a tuple
- Why NumPy arrays outperform Python lists
- List & dictionary comprehensions
- Lambda functions and common use-cases
- Distinction between
returnandyield .locvs.ilocin pandas- Handling missing values in a DataFrame
- Merge, join, and concat in pandas (all join types)
- Using groupby for aggregations
- Concept of broadcasting in NumPy
- Counting word frequencies in text
- Reversing a string efficiently
- The roles of
__init__andselfin a class - Building and applying decorators
- Introduction to metaclasses
- Practical monkey-patching and when to use it
- Principles and code for binary search
- Removing duplicates from a sorted list in-place
- Finding the missing number in a 1‒n array
- Detecting a palindrome (case-/symbol-insensitive)
| # | 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. |