Linear Models Course
This course equips statistics professionals with essential skills to build, diagnose, and deploy robust linear models. From data preparation through to explaining regressions, handling bias and uncertainty, and converting model outputs into actionable business insights, you'll master the full lifecycle of linear modeling for real-world applications.
4 to 360h flexible workload
certificate valid in your country
What will I learn?
Gain practical skills to construct reliable predictive models using linear regression techniques. Delve into data exploration, feature engineering, and fitting simple, multiple, and regularized models with Python or R. Master model validation, assumption checks, outlier and missing data management, plus effective communication of results, uncertainty, and business implications for informed decisions.
Elevify advantages
Develop skills
- Develop robust linear models using OLS, regularization, and feature engineering techniques.
- Diagnose model problems including residuals, multicollinearity, outliers, and robustness checks.
- Evaluate model performance with metrics like R-squared, RMSE, AIC/BIC, cross-validation, and confidence intervals.
- Communicate findings by interpreting coefficients, quantifying uncertainty, and linking to business outcomes.
- Adopt a deployment-focused approach covering monitoring, data drift detection, retraining, and ethical considerations.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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