Final Assessment Rubric
Use this rubric before considering the project complete.
| Area | Beginner | Developing | Strong |
|---|
| Chart selection | Uses familiar charts | Usually appropriate | Consistently question-driven |
| Matplotlib | Basic plots | Customizes plots | Strong control and polish |
| Seaborn | Basic usage | Uses statistical plots | Uses hue/faceting/distributions effectively |
| Data understanding | Describes columns | Connects variables to questions | Understands analytical context |
| Distribution analysis | Makes histograms | Interprets shape | Connects distribution to decisions |
| Relationship analysis | Makes scatter plots | Discusses correlation | Investigates confounding and segmentation |
| Outliers | Identifies extremes | Investigates them | Distinguishes anomalies from legitimate rare events |
| Time series | Creates line chart | Aggregates appropriately | Explains trend/seasonality/noise |
| Communication | Gives chart titles | Explains findings | Builds a coherent analytical story |
| Statistical caution | Avoids obvious errors | Understands correlation | Consistently distinguishes evidence from causation |
| Reproducibility | Code runs | Organized notebook | Clear, reusable, documented workflow |
Final Checklist
Before submitting your notebook, verify:
Code
- [ ] All imports are included.
- [ ] Code runs from top to bottom.
- [ ] Random operations use a fixed seed where appropriate.
- [ ] Variables have meaningful names.
- [ ] Repeated logic is avoided where possible.
- [ ] Charts render correctly.
Data
- [ ] You understand what each row represents.
- [ ] You understand the important variables.
- [ ] Missing data has been investigated.
- [ ] Duplicates have been investigated.
- [ ] Outliers have been investigated.
- [ ] Categories have been checked.
- [ ] Dates have been parsed correctly when applicable.
Visualization
- [ ] Every chart answers a question.
- [ ] Every chart has a descriptive title.
- [ ] Axes are labeled.
- [ ] Units are explicit.
- [ ] Categories are readable.
- [ ] Scales are appropriate.
- [ ] Visual clutter is minimized.
- [ ] Colors have a purpose.
- [ ] You avoid unnecessary decoration.
Interpretation
- [ ] You distinguish observation from interpretation.
- [ ] You do not claim causation from correlation alone.
- [ ] You discuss important limitations.
- [ ] You identify potential confounders.
- [ ] You identify useful follow-up questions.
Communication
- [ ] Someone unfamiliar with your code can understand the story.
- [ ] Markdown explains why each analysis exists.
- [ ] Findings are written in plain language.
- [ ] The conclusion answers the original question.
- [ ] The notebook ends with clear next steps.
Final Challenge: Think Like an Analyst
Before moving to the next phase, complete this exercise without writing code.
You receive a dataset containing:
text
customer_id
age
city
income
product
quantity
price
purchase_date
A stakeholder asks:
"Tell me something interesting about our customers."
Do not immediately make a chart.
Write down:
Step 1 - Questions
What would you want to know?
Step 2 - Assumptions
What do you need to understand about the dataset?
Step 3 - Quality checks
What could be wrong?
Step 4 - Variables
Which variables might be useful?
Step 5 - Visualizations
Which charts would help answer your questions?
Step 6 - Interpretation
What would count as a meaningful finding?
Step 7 - Next steps
What would you investigate after discovering an interesting pattern?
This exercise is the real objective of Phase 1.
What You Should Take Away
Matplotlib and Seaborn are not the skill.
The skill is visual reasoning.
You should now be moving from:
"I know how to make a scatter plot."
to:
"I know when a scatter plot is useful."
Then from:
"I found a correlation."
to:
"I found an association, and I understand what it does and does not establish."
And finally from:
"Here are five charts."
to:
"Here is a question, here is the evidence, here is what the evidence suggests, here are the limitations, and here is what I would investigate next."
That is the foundation of strong Exploratory Data Analysis.
Next Phase
Once you can independently investigate a dataset with NumPy, Pandas, Matplotlib, and Seaborn, you are ready to move from:
toward:
text
Using data to answer questions systematically.
The next phase should build on this foundation rather than skipping it.
Do not rush past EDA.
The ability to understand data before modeling it is one of the most important skills in the entire data-science and AI workflow.