Python for Data Science, Algorithms & Interviews

Software
Python
Data Science
Algorithms
Move from Python syntax to NumPy, pandas, algorithms, complexity, coding practice and technical interview preparation.
Author

MLStation

Published

October 1, 2026

Knowing Python syntax is different from being effective with Python.

For Data Science, the next layer is:

Python
 ├── NumPy       → numerical arrays
 ├── pandas      → tabular data
 ├── visualization
 ├── scikit-learn → ML workflows
 └── algorithms  → computational thinking
TipKey ideas

array shape axis vectorization DataFrame index groupby merge complexity stack queue hash map tree graph recursion

Data Science Python

Start with arrays before treating pandas as magic.

NumPy Quickstart:
https://numpy.org/doc/stable/user/quickstart.html

pandas Getting Started:
https://pandas.pydata.org/docs/getting_started/

10 Minutes to pandas:
https://pandas.pydata.org/docs/user_guide/10min.html

scikit-learn Getting Started:
https://scikit-learn.org/stable/getting_started.html

A useful translation:

Python list       → general collection
NumPy ndarray     → numerical computation
pandas DataFrame  → labeled tabular analysis
Spark DataFrame   → distributed tabular computation

Algorithms & data structures

Learn the concepts before memorizing interview templates:

Big-O
arrays / strings
hash maps / sets
stacks / queues
linked lists
binary search
sorting
trees
heaps
graphs
recursion
dynamic programming

Resources

  • Python Data Structures tutorial: https://docs.python.org/3/tutorial/datastructures.html
  • VisuAlgo: https://visualgo.net/en — visual intuition for algorithms/data structures.
  • LeetCode: https://leetcode.com/ — extensive coding-problem practice.
  • HackerRank: https://www.hackerrank.com/ — structured programming/problem-solving practice.

Interview preparation

Use problems as reasoning practice, not a memorization contest.

1. Restate problem
2. Work a tiny example
3. Describe brute force
4. Identify data structure
5. Improve complexity
6. Code
7. Test edge cases
8. State time + space complexity

For DS interviews, also practice writing ordinary Python transformations without relying on pandas for everything.

WarningCommon trap

Solving 300 problems badly is less useful than solving 60 problems while understanding why the algorithm works, its invariants, complexity and alternatives.

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