Forecast salary growth and cost of living
Due to both inflation and increased productivity from experience, you can expect your salary to grow at different rates depending on your job. Now, since you are working in a growing and in-demand career field as a Data Scientist, you can assume a steady growth in your annual salary every year based on performance.
You can assume an annual salary growth rate of 5%, which means if you start at $85,000 per year, you can expect to earn over $176,000 per year after 15 years. After taxes, assuming your tax rate hasn't changed, that works out to roughly $125,000 per year, which is not unreasonable for a Data Scientist. In fact, you might even make it to that level in a few years! But just to be safe, you should be conservative with your projections.
For this application, assume all inflation and salary growth happens in smaller increments on a monthly basis instead of just one large increase at the end of each year.
monthly_takehome_salary from the previous exercise is available.
This exercise is part of the course
Introduction to Financial Concepts in Python
Exercise instructions
- Derive the equivalent monthly salary growth (see the hint for the formula!)
- Derive your actual salary forecast over time using the
cumulative_salary_growth_forecastwe defined for you.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
import numpy as np
# Create monthly forecasts up to 15 years from now
forecast_months = 12*15
# Set your annual salary growth rate
annual_salary_growth = 0.05
# Calculate your equivalent monthly salary growth rate
monthly_salary_growth = ____
# Forecast the cumulative growth of your salary
cumulative_salary_growth_forecast = np.cumprod(np.repeat(1 + monthly_salary_growth, forecast_months))
# Calculate the actual salary forecast
salary_forecast = ____
# Plot the forecasted salary
plt.plot(salary_forecast, color='blue')
plt.show()