4 Tools and Technologies

CDI emphasizes layered toolkits organized by analytical purpose and programming language. Domains may extend these with specialized tools.

4.1 🧪 EDA – Exploratory Data Analysis

Python: pandas, numpy, sweetviz, ydata-profiling
R: dplyr, tidyr, skimr, DataExplorer, readr, janitor

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4.2 📊 VIZ – Data Visualization

Python: matplotlib, seaborn, plotly, altair
R: ggplot2, GGally, ggcorrplot, plotly

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4.3 📈 STATS – Statistical Analysis

Python: scipy.stats, statsmodels, pingouin
R: stats, car, MASS, emmeans, lme4, broom, brms

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4.4 🤖 ML – Machine Learning

Python: scikit-learn, xgboost, lightgbm, joblib, mlflow
R: tidymodels, xgboost, ranger, caret, mlr3, tune

🧰 Additional examples:
• Microbiome: QIIME2, phyloseq
• RNA-Seq: DESeq2, edgeR
• GWAS: PLINK, GEMMA
• Time Series: Prophet, tsibble
• Deployment: FastAPI, Streamlit

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