VIZ Summary
You’ve successfully completed the Data Visualization (VIZ) layer of the CDI Learning System — working hands-on in both Python and R to explore a wide range of visual techniques that transform raw data into meaningful insight.
This layer focused on building your data storytelling skills — helping you present information clearly, detect patterns, and support analysis through compelling visuals.
🎨 What You’ve Accomplished
- ✅ Created basic plots: histograms, bar charts, boxplots, and scatter plots
- ✅ Enhanced your plots with color, grouping, faceting, and trend lines
- ✅ Visualized multivariate relationships: pair plots, heatmaps, and parallel coordinates
- ✅ Explored dimensionality reduction techniques (PCA, t-SNE, UMAP)
- ✅ Used part-to-whole and structural plots like pie charts, donut charts, treemaps, and Venn diagrams
- ✅ Practiced on both small (iris) and large (diamonds) datasets
- ✅ Built fluency across
matplotlib,seaborn,ggplot2,GGally,plotly, and more
📐 What Comes After Visualization?
Now that you’ve developed your visual intuition, the next step is to quantify relationships — using statistics to draw valid conclusions from your data.
In the next stages of your journey, you’ll dive into:
- 📐 Statistical Analysis (STATS) — measure, test, and explain key patterns
- 🤖 Machine Learning (ML) — learn from data and make predictions with real-world models
Each layer builds on what you’ve already learned — using the same datasets and dual-language structure to deepen your understanding.