Statistical Thinking in Business
What is Statistical Thinking and Why It Matters
Data-Driven
Decision Making
Decision Making
Pattern
Recognition
Recognition
Uncertainty
Management
Management
Business Value of Statistical Thinking:
↑
Increased Revenue
↓
Reduced Costs
↗
Better Predictions
Potential Issues from Lack of Critical Thinking
Ethical Implications:
Bias in Decision Making
Privacy Concerns
Algorithmic Fairness
Selecting and Applying Statistical Techniques
Creating simple solutions to complex business problems:
The Power of Simplicity
Complex Model
High Maintenance
Difficult to Explain
93% Accuracy
VS
Simple Model
Low Maintenance
Easy to Explain
92% Accuracy
How Feature Engineering Adds Value
Raw Data
Date: 2024-02-25
Amount: $145.30
Customer ID: C28392
Feature Engineering
Day of Week: Monday
Is Holiday: No
Customer Segment: Premium
Days Since Last Purchase: 14
Business Value
+37% Prediction Accuracy
+22% Customer Insights
Common Feature Engineering Techniques:
Time-based Features
Extract day, month, season from timestamps
Categorical Encoding
Convert categories to numerical values
Aggregations
Group and summarize related data points
Domain-Specific Features
Create variables with business meaning
Applying Unsupervised Learning to Business Problems
Premium
Customers
Customers
Regular
Customers
Customers
Budget
Customers
Customers
Customer Segmentation
- Identify natural groupings of customers
- Personalize marketing strategies
- Optimize product recommendations
Anomaly Detection
- Identify unusual patterns in data
- Detect fraud and security breaches
- Find equipment malfunctions
Market Basket Analysis
- Discover product associations
- Optimize store layouts
- Create effective bundling offers
Dimension Reduction
- Simplify complex data
- Visualize high-dimensional datasets
- Improve model performance