INFO
Data Science is an interdisciplinary field that extracts insights and knowledge from structured and unstructured data using statistics, programming, and domain expertise.
It combines analytical techniques, computational tools, and scientific reasoning to support decision-making, prediction, and discovery.
Purpose
- Understand and model real-world phenomena using data
- Enable evidence-based decision-making across domains
- Build reproducible workflows for exploration, inference, and prediction
- Bridge the gap between raw data and actionable insights
Benefits
- Reveals hidden patterns, correlations, and trends
- Supports automation, personalization, and optimization
- Enhances accuracy and scalability in analytics pipelines
- Drives innovation in fields like healthcare, finance, marketing, and policy
Applications
-
Descriptive Analysis
- Summarizes and presents data using measures like mean, median, and standard deviation
-
Data Visualization
- Converts raw data into graphical formats like charts, graphs, and maps
-
Statistical Inference
- Draws conclusions from data using probabilistic frameworks and hypothesis testing
-
Sampling
- Selects representative subsets from larger populations to enable scalable analysis
-
Regression Analysis
- Models relationships between variables to predict outcomes and quantify influence
Challenges
- Requires careful handling of bias, missing data, and overfitting
- Demands statistical rigor and computational efficiency
- Raises ethical concerns around privacy, fairness, and transparency
- Needs cross-functional collaboration between analysts, engineers, and domain experts
Video Resource
Data Science Paradigm
- What is Data Science
- Data Visualization
- Descriptive Analysis
- Model Evaluation
- Regression Analysis
- Sampling
- Statistical Inference