Up to this point, we have focused on understanding data.
We inspected a table, cleaned it, transformed it, visualized it, summarized it, and wrote careful insights.
This is where many learning paths stop.
But in CDI, this point is also a handoff.
The Data Science Foundations System prepares analysis-ready tables and evidence outputs. The Applied Data Science System builds on those outputs when prediction, modeling, evaluation, or decision support is needed.
Data analysis helps us answer questions such as:
Modeling extends the workflow further.
It helps us ask questions such as:
Modeling is not separate from analysis.
It depends on analysis.
Clean, well-structured, well-understood data is what makes modeling possible.
The Foundations System produces a compact set of reusable outputs:
data/iris.csv
data/iris_clean.csv
data/iris_wrangled.csv
results/inspection/
results/cleaning/
results/wrangling/
results/figures/
results/summary/These outputs document the journey from raw teaching table to analysis-ready evidence package.
The handoff point looks like this:
Data Science Foundations System
↓
tidy, cleaned, wrangled table
↓
figures and summary evidence
↓
Applied Data Science System
↓
modeling, evaluation, interpretation, decision-makingThe important idea is that modeling should not begin from an unexplored table.
It should begin from a table whose structure, quality, and interpretation have already been examined.
flowchart LR
A[Tidy Table] --> B[Inspection]
B --> C[Cleaning]
C --> D[Wrangling]
D --> E[Visualization]
E --> F[Summary & Insights]
F --> G[Modeling Readiness]
G --> H[Applied Data Science System]flowchart LR
A[Tidy Table] --> B[Inspection]
B --> C[Cleaning]
C --> D[Wrangling]
D --> E[Visualization]
E --> F[Summary & Insights]
F --> G[Modeling Readiness]
G --> H[Applied Data Science System]
This workflow shows why Foundations is reusable across CDI pathways.
Any pathway that produces a tidy table can enter this workflow.
Examples:
Proteomics result table
Microbiome diversity table
Clinical cohort table
AI evaluation tableEach can move through the same foundational process before advanced modeling begins.
Modeling introduces new responsibilities.
A model is not just a calculation. It is a system component that may influence interpretation, prioritization, or decisions.
Modeling adds questions about:
These topics belong mainly in the Applied Data Science System.
The Foundations System introduces the transition but does not try to cover all modeling details.
Before moving into modeling, ask whether the data is ready.
Modeling readiness checklist
[ ] Is the input table tidy?
[ ] Are required columns present?
[ ] Are missing values understood or handled?
[ ] Are duplicates checked?
[ ] Are feature types clear?
[ ] Is the target variable clearly defined?
[ ] Are grouped patterns already explored?
[ ] Are summary outputs available?
[ ] Are figures available for interpretation?
[ ] Are limitations documented?If several items are missing, return to inspection, cleaning, wrangling, or summarization before modeling.
The following example shows what the next stage may look like. It is intentionally brief.
The goal is not to teach full machine learning here. The goal is to show how the wrangled table could become model-ready input.
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
df = pd.read_csv("data/iris_wrangled.csv")
features = [
"sepal_length",
"sepal_width",
"petal_length",
"petal_width",
"petal_area"
]
X = df[features]
y = df["species"]
X_train, X_test, y_train, y_test = train_test_split(
X,
y,
test_size=0.25,
random_state=42,
stratify=y
)
model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(classification_report(y_test, predictions))This preview introduces several new concepts:
Each of these deserves careful treatment in the Applied Data Science System.
A model becomes more useful when it is part of a larger system.
flowchart LR
A[Analysis-Ready Table] --> B[Model]
B --> C[Evaluation]
C --> D[Interpretation]
D --> E[Decision Support]flowchart LR
A[Analysis-Ready Table] --> B[Model]
B --> C[Evaluation]
C --> D[Interpretation]
D --> E[Decision Support]
In more advanced settings, the workflow may extend further:
flowchart LR
A[Build] --> B[Test]
B --> C[Deploy]
C --> D[Monitor]
D --> E[Improve]
E --> Aflowchart LR
A[Build] --> B[Test]
B --> C[Deploy]
C --> D[Monitor]
D --> E[Improve]
E --> A
This is why the next CDI layer is not just about algorithms.
It is about applied analytical systems.
The Applied Data Science System can build on Foundations with topics such as:
This creates a clean CDI separation:
Data Science Foundations System
= inspect, clean, wrangle, visualize, summarize, interpret tidy tables
Applied Data Science System
= model, evaluate, explain, improve, and support decisionsBoth are part of the broader Data Science Pathway.
The Foundations System is reusable because many CDI pathways eventually produce tidy tables.
Omics Pathway
↓
differential results, abundance tables, diversity metrics
Clinical & Medical Data Pathway
↓
cohort tables, lab tables, outcome tables
AI, Thinking & Decision Pathway
↓
evaluation tables, prediction logs, decision recordsOnce these tables are tidy and analysis-ready, they can use the same foundations:
inspect
clean
wrangle
visualize
summarize
interpretWhen modeling is needed, the workflow continues into Applied Data Science.
Review the outputs produced so far:
data/iris_wrangled.csv
results/figures/
results/summary/analysis-insights.mdThen answer:
A reasonable classification target is:
speciesPotential input features include:
sepal_length
sepal_width
petal_length
petal_width
petal_areaThe strongest exploratory support comes from petal-related plots and grouped summaries, which show clearer descriptive separation across species.
A careful caution is:
The Iris dataset is small, clean, and educational. Model performance on this dataset should not be treated as evidence that the same workflow will generalize to larger, noisier, real-world datasets without validation.Data analysis is not the final step.
It is the foundation for building reliable analytical systems.
A model built without inspection, cleaning, visualization, and summary interpretation is fragile.
A model built after careful data science foundations is easier to understand, evaluate, and trust.
In this lesson, you:
The Data Science Foundations System now has a complete reusable workflow for tidy-table analysis. The next step is to close the foundations track and then continue into the Applied Data Science System when modeling, evaluation, and decision-making are needed.