EDA Summary
You’ve successfully completed the Exploratory Data Analysis (EDA) layer of the CDI Learning System — working hands-on in both Python and R to load, inspect, clean, transform, and save your data.
This foundational layer has equipped you with the confidence to move forward — not just knowing how to use code, but how to think like a data scientist.
🧱 What You’ve Accomplished
- ✅ Set up a clean, organized project workspace
- ✅ Practiced essential EDA techniques with real data
- ✅ Standardized and saved a reusable dataset (data/iris.csv)
- ✅ Built fluency in both Python and R side by side
📈 What Comes After EDA?
Now that you’ve completed the Exploratory Data Analysis (EDA) layer, you’re ready to move beyond structure and cleaning — and into data storytelling.
The next stages of your journey include:
- 🎨 Data Visualization (VIZ) — turn data into clear, compelling visual insights
- 📐 Statistical Analysis (STATS) — test hypotheses and draw valid conclusions
- 🤖 Machine Learning (ML) — build predictive models using real-world data
In these upcoming layers, you’ll continue working with familiar datasets — and explore new ones tailored to each domain.