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Data Scientist

Certified R Data Scientist

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Course overview

Curriculum

01The ML workflow and first models5

Framing a Problem as Machine Learning The Bias-Variance Tradeoff Train, Validation, Test, and Data Leakage Your First End-to-End Model in R Quiz

02Regression, done properly9

OLS Regression from Scratch Regression Assumptions and Residuals Influence and Leverage Multicollinearity in Regression Heteroskedasticity and Autocorrelation Inference and Prediction in Regression Logistic Regression Done Properly GLMs Beyond Logistic Quiz

03Classification fundamentals7

kNN and the Curse of Dimensionality Naive Bayes for Tabular and Text Discriminant Analysis, LDA and QDA Decision Trees for Classification Decision Boundaries and Model Geometry Reading a Classifier Quiz

04Trees and gradient boosting10

Gradient Boosting from Scratch Decision Trees from scratch LightGBM and CatBoost in R From one tree to a forest The Hyperparameters That Matter Train, tune and read a forest in R Early Stopping and Learning Curves Monotonic Constraints for Business Rules Quantile Regression Forests and Prediction Intervals Quiz

05The tidymodels workflow8

Preprocess with recipes Define models with parsnip Bundle steps with workflows Resample with rsample Measure with yardstick Tune with the tune package Compare many models with workflowsets Quiz

06Feature engineering and selection8

Encoding Categorical Variables Target Encoding Without Leakage Scaling and Transformations Interaction and Spline Features Features from Dates, Text and Geo Imputing Missing Values in Features Feature Selection and Spotting Leakage Quiz

07Model evaluation, resampling and tuning8

Cross-Validation Strategies Grouped, Blocked, and Time-Aware CV Nested Cross-Validation Hyperparameter Tuning Strategies Scoring Rules and Regression Metrics Comparing Models Statistically From Metrics to Money Quiz

08Imbalanced, cost-sensitive and calibrated classification7

Beyond Binary: Multiclass Classification Class Imbalance and Resampling Thresholds Under Asymmetric Costs ROC, PR, Lift and Gains Curves Calibrating Predicted Probabilities Why AUC Is Not Enough Quiz

09Unsupervised - clustering and dimensionality reduction9

PCA in R Factor Analysis k-Means and Choosing k Hierarchical and Density Clustering Gaussian Mixture Models Cluster Validation and Stability t-SNE and UMAP Association Rules and Market Basket Quiz

10Experiment and causal basics6

Correlation, Causation and Potential Outcomes Causal Diagrams with DAGs A/B Testing and Experiment Design Reading an Experiment When You Cannot Randomize Quiz

11Interpretability and responsible AI (essentials)7

Global vs Local Explanations Permutation and Drop-Column Importance SHAP Values Partial Dependence, ICE, and ALE Fairness Basics Model Cards and Documenting a Model Quiz

12Shipping your first model (production essentials)7

Reproducible pipelines with targets Versioning models with vetiver and pins Serving a model with plumber Batch vs real-time inference Monitoring and drift An ML system design checklist Quiz

13Regression and GLMs - the expert cut14

Robust Regression with M-Estimators MM-Estimation and the Breakdown Point Quantile Regression Ridge Regression and Shrinkage Lasso and Elastic Net GAMs, Splines and Smooths Choosing Smoothness in a GAM Poisson and Negative Binomial Regression Zero-Inflated and Hurdle Models Gamma and Tweedie Regression Beta and Ordinal Regression Mixed Models and Random Intercepts Random Slopes and GLMMs Quiz

14Advanced supervised learning9

Support Vector Machines and the Maximum Margin Kernel SVMs and the Kernel Trick Regularized Discriminant Analysis Gaussian Processes for Regression Stacking and the Super Learner Bayesian Optimization for Hyperparameters Approximate Nearest Neighbors at Scale A Tuned, Stacked Model End to End Quiz

15Survival and time-to-event8

Survival Data and Censoring Kaplan-Meier and the Log-Rank Test Cox Proportional Hazards Checking Proportional Hazards Parametric and AFT Models Competing Risks and Cumulative Incidence Survival ML and Evaluation Quiz

16Bayesian and hierarchical modeling10

The Bayesian Update Build an LLM agent from scratch Conjugacy and Choosing Priors MCMC and the Metropolis Sampler HMC, NUTS and MCMC Diagnostics Hierarchical Models and Partial Pooling Posterior Predictive Checks Bayesian Model Comparison with LOO and WAIC Bayesian Regression and GLMs End to End Quiz

17Experimentation and online learning8

Designing Experiments for Power Variance Reduction with CUPED Experiment Pitfalls, Peeking and SRM Cluster and Switchback Experiments Multi-Armed Bandits, Explore vs Exploit Thompson Sampling and Bayesian Bandits Contextual Bandits and Off-Policy Evaluation Quiz

18Causal inference for decisions12

Matching and the Propensity Score Inverse-Probability Weighting and Doubly-Robust Estimation Difference-in-Differences and Parallel Trends Staggered DiD and the Negative-Weights Problem Regression Discontinuity Instrumental Variables and 2SLS Synthetic Control Uplift and Heterogeneous Effects Double Machine Learning Sensitivity Analysis and Placebo Tests Mediation Analysis Quiz

19Robustness, drift and distribution shift8

Kinds of Distribution Shift Detecting Distribution Shift Adapting to Drift: Reweighting and Retraining Out-of-Distribution and Novelty Detection Group Robustness and DRO Adversarial Robustness A Monitoring and Robustness Playbook Quiz

20Anomaly detection and advanced unsupervised8

What Is an Anomaly?Isolation Forest and Extended Isolation Forest Local Outlier Factor and One-Class SVM Autoencoders for Anomaly Detection Time-Series Anomaly Detection Kernel PCA, Sparse PCA and NMF Self-Supervised and Contrastive Learning Quiz

21Uncertainty - conformal, calibration and probabilistic prediction8

Prediction Intervals You Can Trust Split Conformal Prediction Conformal Prediction for Classification Quantile and Distributional Regression Calibration, Reliability and Recalibration The Bootstrap and the Jackknife+Reporting Uncertainty Honestly Quiz

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Framing a Problem as Machine Learning

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