Top Ten Python Libraries
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
π Data Science Roadmap β The Skills You Must Master in 2025
If youβre 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
π Save this roadmap for your learning plan. π Repost to help other learners. π 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