Statistics, Probability, & Quantitative Finance
Applied Statistics & Financial Analysis
A project focused on applying core statistical concepts to real-world problems. The journey starts by building an A/B testing simulator from scratch to understand hypothesis testing, p-values, and confidence intervals. It then progresses to a practical financial application: analyzing and visualizing the volatility of publicly traded stocks using historical market data and advanced statistical techniques.
Technologies Used
🐍Python
🔧 Pandas
🔧 NumPy
🔧 SciPy
🔧 Matplotlib
🔧 yfinance
Project Info
CategoryStatistics, Probability, & Quantitative Finance
Technologies6
Features12
Key Features
Entry: Develop an A/B Testing simulator in Python to compare two sample groups (e.g.
website conversion rates).
Entry: Use NumPy to generate random data based on statistical distributions.
Entry: Perform a t-test using SciPy to determine statistical significance and calculate p-values.
Code Implementation
Entry: A/B Test Simulation & T-Test