Tutorials

Step-by-step guides to master data science with DS. Each tutorial includes conceptual explanations, mathematical foundations, and practical code examples.

Available tutorials

Ordination Analysis

Learn when and how to use PCA, LDA, and RDA for multivariate analysis. Understand the mathematical foundations and interpret biplots.

Topics covered:

• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Redundancy Analysis (RDA)
• Choosing the right method
• Interpreting biplots

Statistical analysis

From descriptive statistics to mixed-effects models. Learn to test hypotheses and model relationships.

Topics covered:

• Descriptive statistics (mean, SD, quantiles)
• Hypothesis testing (t-test, ANOVA)
• Generalized Linear Models (GLM)
• Logistic regression
• Mixed-effects models

Clustering

Discover natural groupings in your data with hierarchical and k-means clustering.

Topics covered:

• Hierarchical Clustering (HCA)
• K-means clustering
• Dendrogram interpretation
• Choosing the number of clusters
• Validation and evaluation

Machine Learning

Build predictive models with proper cross-validation and hyperparameter tuning.

Topics covered:

• Classification vs regression
• Train/test splitting
• Feature scaling and encoding
• Cross-validation
• Grid search
• Model evaluation

Learning path

Beginner: Start here if you’re new to data science

1. Getting Started
2. Statistical Analysis
3. Clustering

Intermediate: Build on statistical foundations

1. Ordination Analysis
2. Machine Learning

Advanced: Dive into specific topics in the API Reference

1. API Reference