4 Pillars of Data Science
4 Pillars of Data Science
1. Computer Science
-Develops algorithms and data structures.
-Works with databases and cloud computing.
-Implements machine learning and AI models.
-Optimizes code for efficiency and scalability.
Tools: Python, SQL, Git
2. Communication & Visualization
-Converts complex data into clear insights.
-Builds reports, dashboards, and presentations.
-Uses charts, graphs, and storytelling techniques.
-Bridges the gap between data and business.
Tools: Tableau, Power BI, Matplotlib
3. Mathematics & Statistics
-Applies probability and statistical methods.
-Uses linear algebra and calculus for ML.
-Conducts hypothesis testing and A/B testing.
-Ensures accurate data analysis and interpretation.
Tools: NumPy, SciPy, R
4. Domain Knowledge
-Understands industry-specific problems.
-Translates data insights into business impact.
-Aligns models with real-world applications.
-Supports strategic decision-making.
Tools: Excel, Google Analytics, Salesforce
