Completing the Foundations System
You have completed the Data Science Foundations System of the CDI Data Science Pathway.
At this stage, you have developed the core habits that support reliable table-based analysis:
- loading and inspecting datasets
- cleaning and validating data
- transforming and structuring data
- visualizing patterns
- summarizing evidence using statistics
- writing careful interpretations grounded in evidence
These are not isolated skills.
They form a connected analytical workflow that can be reused across CDI pathways.
What you have built
Across the Foundations System, you moved through a complete tidy-table workflow:
environment setup
↓
table inspection
↓
data cleaning
↓
data wrangling
↓
visualization
↓
summary statistics
↓
interpretation
↓
modeling readinessThe goal was not only to learn individual commands.
The goal was to build a reproducible pattern for moving from a tidy table to defensible analytical evidence.
System outputs
By this point, the project has produced a small but complete analysis package.
data/
├── iris.csv
├── iris_clean.csv
└── iris_wrangled.csv
results/
├── inspection/
├── cleaning/
├── wrangling/
├── figures/
└── summary/These outputs show the full progression:
input table
↓
cleaned table
↓
wrangled table
↓
figures
↓
summary tables
↓
insights reportThis is the same pattern that can be reused when other CDI systems produce tidy, analysis-ready tables.
Scripts created in the Foundations System
The system also includes reusable Python scripts:
scripts/python/
├── inspect_table.py
├── clean_example_data.py
├── wrangle_example_data.py
├── plot_example_data.py
└── summarize_table.pyEach script has a clear role:
inspect_table.py
→ inspect structure, columns, types, and missing values
clean_example_data.py
→ clean and validate a table
wrangle_example_data.py
→ create derived features and analysis-ready outputs
plot_example_data.py
→ save reusable exploratory figures
summarize_table.py
→ create summary tables and an insights reportThis makes the guide more than a set of lessons.
It becomes a small reproducible analysis system.
What this foundation means
The Foundations System teaches a reusable analytical pattern:
inspect
clean
wrangle
visualize
summarize
interpretThis pattern is useful beyond the Iris example.
It can support:
Omics result tables
Clinical cohort tables
Medical lab tables
AI evaluation tables
Decision records
Survey data
Business datasetsThe domain changes, but the foundational analysis logic remains similar.
CDI parent-layer role
The Data Science Foundations System serves as the shared parent layer for CDI pathways once pathway-specific systems produce tidy, analysis-ready tables.
Omics Pathway
↓
tidy result tables
↓
Data Science Foundations System
Clinical & Medical Data Pathway
↓
clean cohort tables
↓
Data Science Foundations System
AI, Thinking & Decision Pathway
↓
evaluation and decision tables
↓
Data Science Foundations SystemThis is why the Foundations System should remain focused.
It does not replace domain-specific systems.
It gives them a common analytical language after the data has been structured.
What comes next
The next stage moves beyond description and interpretation.
You begin to ask:
- Can we predict an outcome?
- How well does a model perform?
- Which features influence predictions?
- What limitations should be stated?
- Can results support a decision?
- What happens when a model is used on new data?
This is the transition from foundational analysis to applied analytical systems.
In CDI terms:
Data Science Foundations System
↓
Applied Data Science SystemFrom analysis to applied systems
In the Foundations System, you focused on:
tidy data
inspection
cleaning
wrangling
visualization
summary
interpretationIn the Applied Data Science System, the focus expands to:
feature engineering
model building
model evaluation
cross-validation
model interpretation
claims and limitations
decision-making
responsible useThe transition is not a jump.
It is a continuation.
Good modeling depends on strong foundations.
Key mindset shift
In the Foundations System, you asked:
- What does the data contain?
- Is the table clean and usable?
- How do groups differ?
- What patterns are visible?
- What summaries support interpretation?
In the next stage, you will ask:
- What outcome are we trying to predict or explain?
- Which features should be used?
- How should performance be evaluated?
- Does the model generalize?
- What conclusions are justified?
- What risks or limitations remain?
This shift is essential.
The CDI reasoning loop
As you move forward, your work follows an iterative process:
define the question
↓
prepare and validate data
↓
explore patterns
↓
summarize evidence
↓
apply models or formal methods
↓
interpret results
↓
revisit earlier steps as neededAnalysis is not strictly linear.
It improves through iteration.
A later modeling result may reveal that you need to revisit cleaning, features, grouping variables, or assumptions.
Where this leads
From here, you can continue into:
Applied Data Science System
→ modeling, evaluation, interpretation, decision-making
Clinical & Medical Data Systems
→ cohort analysis, outcomes, reporting
Omics Systems
→ pathway-specific result tables and interpretation
AI, Thinking & Decision Systems
→ evaluation, reasoning, decision supportEach stage builds on the foundations already developed.
Beyond this system
Transition summary
You are now prepared to:
- move from descriptive analysis to structured modeling
- connect computation to reasoning
- reuse tidy-table workflows across CDI pathways
- begin working with systems that extend beyond a single dataset
The Foundations System gives you the analytical base.
The Applied Data Science System builds on it.
Final thought
Data analysis is not the final step.
It is the foundation for building systems that can be inspected, trusted, reused, and improved.