Lilly Tech Systems
Learn Statistical Data Modeling
Master the mathematical foundations of data science. Learn probability, hypothesis testing, regression, classification, and time series analysis with Python — all for free.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is statistical modeling? Types of models, variables, population vs sample, and the model building process.
2. Probability & Distributions
Probability basics, Bayes' theorem, Normal, Binomial, Poisson distributions, and the Central Limit Theorem.
3. Hypothesis Testing
Null and alternative hypotheses, p-values, significance levels, t-tests, chi-square, ANOVA, and A/B testing.
4. Regression Models
Linear regression, assumptions, R-squared, polynomial regression, and regularization techniques.
5. Classification Models
Logistic regression, decision trees, random forests, confusion matrix, ROC curves, and cross-validation.
6. Time Series
Trend, seasonality, stationarity, ARIMA models, exponential smoothing, and forecasting techniques.
7. Best Practices
Model selection criteria, overfitting, feature engineering, validation strategies, and common pitfalls.
What You'll Learn
By the end of this course, you'll be able to:
Apply Statistical Tests
Choose and apply the right hypothesis tests to validate assumptions and make data-driven decisions.
Build Predictive Models
Create regression and classification models using Python, Scikit-learn, and Statsmodels.
Analyze Time Series
Decompose trends and seasonality, build ARIMA models, and generate forecasts.
Evaluate Models Rigorously
Use cross-validation, information criteria, and diagnostic plots to assess model quality.