Analysis Evolutions and ..

With Claude
this image that shows the evolution of data analysis and its characteristics at each stage:

Analysis Evolution:

1. 1-D (One Dimensional): Current Status analysis
2. Time Series: Analysis of changes over time
3. n-D Statistics: Multi-dimensional correlation analysis
4. ML/DL (Machine Learning/Deep Learning): Huge-dimensional analysis including exceptions

Bottom Indicators’ Changes:

1. Data/Computing/Complexity:

• Marked as “Up and Up” and increases “Dramatically” towards the right

2. Accuracy:

• Left: “100% with no other external conditions”
• Right: “not 100%, up to 99.99% from all data”

3. Comprehensibility:

• Left: “Understandable/Explainable”
• Right: “Unexplainable”

4. Actionability:

• Left: “Easy to Action”
• Right: “Difficult to Action require EXP” (requires expertise)

This diagram illustrates the trade-offs in the evolution of data analysis. As analysis methods progress from simple one-dimensional analysis to complex ML/DL, while the sophistication and complexity of analysis increase, there’s a decrease in comprehensibility and ease of implementation. It shows how more advanced analysis techniques, while powerful, require greater expertise and may be less transparent in their decision-making processes.

The progression also demonstrates how modern analysis methods can handle increasingly complex data but at the cost of reduced explainability and the need for specialized knowledge to implement them effectively.