Traditional and Modern Data Science
==2024-12-23
Difference is the key.
Theory
| Aspect | Traditional Data Science | Modern Data Science |
|---|---|---|
| Data Handling | Structured data from relational databases | Structured and unstructured data from diverse sources |
| Techniques & Tools | Basic statistics, regression, SQL, Excel | Machine learning, deep learning, Python, R, TensorFlow |
| Computational Power | Standard hardware, simpler algorithms | Cloud computing, GPUs, distributed systems |
| Applications | Finance, market research, business reporting | Healthcare, AI, NLP, computer vision, e-commerce |
| Modeling Techniques | Linear regression, time series analysis, ANOVA | Neural networks, deep learning, ensemble methods |
| Automation & AI | Limited automation, manual data processing | AI-driven automation for data preprocessing and analysis |
| Examples | Sales forecasting using linear regression | Predicting churn using classification algorithms |
| Future Directions | Foundational but limited in complex data scenarios | Evolving with AI, big data, and future technologies |