DATA SCIENCE

• Mathematics & Statistics: The foundation for understanding data distributions, hypothesis testing, and machine learning.
• Programming: Languages like Python, R, and SQL are used to manipulate, analyze, and model data.
• Machine Learning: Algorithms are trained on historical data to predict future trends and classify information.
• Domain Expertise: Industry-specific knowledge (e.g., healthcare, finance, or retail) is required to ask the right questions and interpret results effectively. 

The Data Science Process

1. Data Collection: Gathering raw data from databases, APIs, or sensors.
2. Data Cleaning/Wrangling: Preparing raw data by handling missing values and inconsistencies.
3. Exploratory Data Analysis (EDA): Visualizing and analyzing the data to discover hidden patterns and relationships.
4. Model Building & Evaluation: Applying machine learning algorithms to make predictions.
5. Data Visualization & Communication: Using dashboards to present findings to stakeholders clearly. 

Common Use Cases

• Recommendation Engines: Streaming services (e.g., Netflix) and e-commerce (e.g., Amazon) personalizing content.
• Fraud Detection: Banks analyzing transactions in real-time to flag anomalies.
• Healthcare: Predicting patient outcomes and medical imaging analysis.