Financial & HR Analytics
389 words
2 min read
Visual companion
Python
Type and operator map
Python Week 1: the first filter for runtime behavior
View
Revision summary
What this note is really saying
Short form
# Financial & HR Analytics ## 🎯 Learning Objectives - Apply financial ratio analysis and forecasting models - Analyze employee attrition using predictive models - Design HR dashboards for workforce planning - Understand the analytics maturity model ## 📖 Core Content ### 5.1 Financial Analytics Framework Analysis T...
Financial & HR Analytics
🎯 Learning Objectives
- Apply financial ratio analysis and forecasting models
- Analyze employee attrition using predictive models
- Design HR dashboards for workforce planning
- Understand the analytics maturity model
📖 Core Content
5.1 Financial Analytics Framework
| Analysis Type | Purpose | Key Metrics |
|---|---|---|
| Liquidity | Short-term solvency | Current ratio, Quick ratio |
| Profitability | Earnings efficiency | Gross margin, Net margin, ROE |
| Leverage | Debt management | Debt-to-Equity, Interest coverage |
| Efficiency | Asset utilization | Asset turnover, Inventory turnover |
| Valuation | Company worth | P/E ratio, EV/EBITDA |
5.2 Financial Forecasting
Time series forecasting for financial data:
- Moving averages (smooth noise)
- Exponential smoothing (weighted recent)
- ARIMA (autoregressive patterns)
- Prophet (Facebook's additive model)
5.3 HR Analytics: Employee Attrition
Key drivers of attrition (commonly found):
- Years at company (bimodal: early years and around 3-5 years)
- Promotion lag
- Compensation vs market
- Manager satisfaction score
- Work-life balance ratings Attrition prediction model:
- Features: tenure, compensation ratio, promotion history, engagement score, commute distance
- Model: Logistic Regression (interpretable) or Random Forest
- Action: Early warning system for at-risk employees
5.4 People Analytics Maturity
| Level | Description | Example |
|---|---|---|
| 1: Reporting | What happened? | Headcount report |
| 2: Analysis | Why did it happen? | Attrition by manager |
| 3: Prediction | What will happen? | Who is at risk of leaving? |
| 4: Prescription | What should we do? | Targeted retention interventions |
| 5: Optimization | Automated decisions | Real-time compensation adjustments |
📝 Practice Questions
Q1: What financial ratios would you analyze for a company considering a loan?Liquidity ratios (can they pay short-term?): Current ratio > 1.5. Leverage ratios (how much debt?): Debt-to-Equity < 2. Coverage ratios (can they service debt?): Interest coverage > 3. Profitability (are they profitable?): Positive net margin. Each industry has different benchmarks — don't compare a bank to a manufacturer. Q2: What are the strongest predictors of employee attrition?Empirically: (1) low engagement score, (2) manager quality (worst predictor of attrition), (3) years since last promotion, (4) compensation below market rate, (5) high commute time. Surprisingly, tenure has a U-shaped relationship: high attrition in first 6 months (bad hiring) and around 3 years (career stagnation). Q3: Why use logistic regression for attrition prediction instead of random forest?Interpretability — HR decisions affect people's careers. With logistic regression, you can explain: "The model shows that employee engagement score and time since promotion are the strongest factors." Random Forest would give a black-box score. HR analytics demands explainable models for ethical and legal reasons. Join Discord PreviousData StorytellingNextMarketing Analytics