1. Data Analysis Definition: The practice of examining data to answer questions, identify trends, and extract insights.

2. Data Analysis vs Data Analytics: ◦ Data Analysis: Focuses on historical data to identify patterns and insights. ◦ Data Analytics: Includes predictive and prescriptive techniques to inform decisions.

3. Types of Data Analytics: ◦ Descriptive Analytics: Summarizes historical data. ◦ Diagnostic Analytics: Explains why something happened. ◦ Predictive Analytics: Forecasts future outcomes. ◦ Prescriptive Analytics: Recommends actions based on data.

4. Data Analysis Applications: Medical, business, governmental, and digital marketing applications.

5. SMART Business Goals: Specific, Measurable, Achievable, Relevant, and Time-bound goals.

6. OSEMN Framework: A data science process involving Obtain, Scrub, Explore, Model, and iNterpret.