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MM - Explain predictive, Descriptive, and Prescriptive data analysis. And also mention their difference.

Creating mind maps with concise keywords and short sentences is an excellent way to recall the key concepts of predictive, descriptive, and prescriptive data analysis. Here are the essential keywords and short sentences for each type of analysis to help you create your mind maps:

Descriptive Data Analysis

  • Objective: Summarize past events
  • Focus: What happened?
  • Techniques:
    • Data Aggregation: Summarize large datasets
    • Data Visualization: Charts, graphs, dashboards
    • Descriptive Statistics: Mean, median, mode, variance
  • Output: Reports, summaries, visualizations
  • Examples: Sales reports, customer demographics

Predictive Data Analysis

  • Objective: Forecast future events
  • Focus: What will happen?
  • Techniques:
    • Regression Analysis:
      • Linear Regression: Predict continuous outcomes
      • Logistic Regression: Binary classification
    • Time Series Analysis: Forecast future values
    • Machine Learning Algorithms: Decision trees, random forests, neural networks
  • Output: Predictive models, forecasts
  • Examples: Sales forecasts, customer churn prediction

Prescriptive Data Analysis

  • Objective: Recommend actions
  • Focus: What should we do?
  • Techniques:
    • Optimization Models: Best solution from options
    • Simulation: Monte Carlo simulation
    • Decision Analysis: Making decisions under uncertainty
  • Output: Actionable recommendations, optimal strategies
  • Examples: Inventory optimization, marketing strategies

Differences Summary

  • Descriptive:
    • Objective: Summarize past
    • Focus: What happened?
    • Techniques: Aggregation, visualization, statistics
    • Output: Reports, summaries
  • Predictive:
    • Objective: Forecast future
    • Focus: What will happen?
    • Techniques: Regression, time series, ML algorithms
    • Output: Predictive models, forecasts
  • Prescriptive:
    • Objective: Recommend actions
    • Focus: What should we do?
    • Techniques: Optimization, simulation, decision analysis
    • Output: Recommendations, strategies

Visual Examples

  1. Descriptive Analysis Mind Map:

    • Center Node: Descriptive Analysis
      • Objective: Summarize past events
      • Focus: What happened?
      • Techniques: Data Aggregation, Data Visualization, Descriptive Statistics
      • Output: Reports, Summaries, Visualizations
      • Examples: Sales reports, Customer demographics

  2. Predictive Analysis Mind Map:

    • Center Node: Predictive Analysis
      • Objective: Forecast future events
      • Focus: What will happen?
      • Techniques: Regression Analysis, Time Series Analysis, Machine Learning Algorithms
      • Output: Predictive models, Forecasts
      • Examples: Sales forecasts, Customer churn prediction

  3. Prescriptive Analysis Mind Map:

    • Center Node: Prescriptive Analysis
      • Objective: Recommend actions
      • Focus: What should we do?
      • Techniques: Optimization Models, Simulation, Decision Analysis
      • Output: Recommendations, Optimal strategies
      • Examples: Inventory optimization, Marketing strategies

Using these keywords and short sentences, you can create detailed and visually organized mind maps that will aid in recalling the concepts of each type of data analysis quickly.