C2-M2 - Mathematical Thinking

Definition of Mathematical Thinking
- A logical approach to problem-solving.
- Involves breaking down problems step by step to identify data patterns.
- Useful for choosing appropriate analytical tools based on problem specifics.
Types of Data
- Small Data:
  - Defined as specific metrics over short time periods.
  - Example: Daily water intake.
  - Best for day-to-day decision making.
  - Tools: Spreadsheets for organizing and analyzing data.
- Big Data:
  - Involves larger and less specific datasets, covering extended periods.
  - Requires breakdown for effective analysis.
  - Useful for large-scale problems and significant business decisions.
  - Tools: SQL for handling large datasets.

Problem Description:
- Hospitals may experience over or underutilization of beds.
- Goal: Optimize bed usage while minimizing waste of resources.
Using Mathematical Thinking:
- Step-by-Step Breakdown:
  - Identify key metrics (e.g., number of beds open and used over time).
  - Calculate Bed Occupancy Rate:
    - Formula: Bed Occupancy Rate = (Total Inpatient Days) / (Total Available Beds).
- Identifying Patterns:
  - Analyze relationships between key variables to find actionable insights.
Choosing the Right Tool:
- Due to the extensive patient data over time, SQL is the logical choice for analysis.
Finding a Solution:
- Discovering consistently unused beds leads to action:
  - Decision to reduce the number of beds saves space and resources.
  - Resources can be reallocated for necessary supplies like protective equipment.

Importance of data empowerment in decision-making.
Difference between quantitative (numerical) and qualitative (descriptive) analysis.
Utilizing reports and dashboards for efficient data visualization.
Defining and understanding metrics in data analysis.
Applying mathematical approaches to enhance problem-solving.