Top 10 Python Libraries

  • NumPy: Mathematial functions
  • SciPy: Scientific computing
  • Pandas: Data analysis and manipulation
  • Matplotlib: Data visualizations
  • Seaborn: Data visualizations
  • SQLModel: Interact with SQL databases
  • Scrapy: Web crawling
  • Tensorflow: Machine Learning
  • PyTorch: Machine Learning
  • Keras: Deep Learning

πŸš€ Data Science Roadmap β€” The Skills You Must Master in 2025

If you’re starting your data science journey or planning to level up, this roadmap breaks down every major skill area you need to focus on β€” from foundations to deployment.

1) Mathematics & Statistics (Foundation) β€’ Probability β€’ Linear algebra β€’ Calculus basics β€’ Statistical inference

2) Programming (Core Skills) β€’ Python β€’ R (optional) β€’ Data structures β€’ Algorithms

3) Data Analysis Tools β€’ Pandas β€’ NumPy β€’ Excel / Google Sheets β€’ Data cleaning & wrangling

4) Databases & SQL β€’ Joins, subqueries, CTEs β€’ Window functions β€’ Indexing & optimization β€’ SQL modeling

5) Data Visualization β€’ Matplotlib β€’ Seaborn β€’ Plotly β€’ Power BI / Tableau

6) Machine Learning (Core) β€’ Regression & classification β€’ Clustering β€’ Feature engineering β€’ Model evaluation

7) Deep Learning (Advanced AI) β€’ Neural networks β€’ CNNs, RNNs, LSTMs β€’ Transfer learning β€’ Model deployment

8) Specialized AI Topics β€’ NLP β€’ Computer vision β€’ Recommendation systems β€’ Time series forecasting

9) Big Data & Cloud β€’ Spark β€’ Hadoop β€’ AWS / GCP / Azure basics β€’ Data pipelines

10) Projects & Portfolio β€’ End-to-end ML pipelines β€’ SQL case studies β€’ Dashboards & reports β€’ GitHub portfolio

11) Soft Skills & Business Knowledge β€’ Communication β€’ Problem-solving β€’ Critical thinking β€’ Domain understanding

12) Career Preparation β€’ Resume & portfolio β€’ Mock interviews β€’ LeetCode + SQL practice β€’ Networking & branding

πŸ“Œ Save this roadmap for your learning plan. πŸ” Repost to help other learners. πŸ“š Follow Programming Valley for free courses, guides, and study materials.


import pandas as pd

data = pd.DataFrame({
  "brand": ["apple", "google", "apple", "google", "apple"],
  "price":[50000, 30000, 90000, 75000, 25000]
})

print(data)

Output,

    brand  price
0   apple  50000
1  google  30000
2   apple  90000
3  google  75000
4   apple  25000