Project Selection and Planning

Choose a small capstone and break it into realistic milestones.

• Level: Project practice
• Estimated time: 35–50 minutes
• You will learn: Choose a small capstone and break it into realistic milestones.
• Practice in: VS Code, Jupyter, and GitHub

Open this lesson in Google Colab

Questions

• What problem does Project Selection and Planning help us solve in a small Python program?
• What should we predict before running the example?
• What value, output, or error should we inspect after changing one line?

Objectives

• Run a complete example for capstone project selection and planning in Colab.
• Explain the example line by line using plain language.
• Change one part of the code and predict the result before running it.
• Recognize one common mistake and use the error message as evidence.

Hands-on episode: Project Selection and Planning

Project planning turns an idea into a question, a user, a minimum useful feature set, milestones, risks, and evidence of completion.

We will learn this by running code, not by memorizing a definition first. Open the Colab notebook from the button above, find this section, and run each cell in order. Keep a small note beside the notebook with three columns: prediction, actual result, and what changed.

Example 1.1

Read this plan as a promise small enough to keep.

project = {
"user": "a learner tracking practice",
"input": "daily minutes",
"output": "weekly summary",
"first_milestone": "save and total one week",
}
print(project["first_milestone"])

Run the cell once without editing it. If the result is different from your prediction, leave the prediction visible and write one sentence about the difference. That sentence is more useful than a perfect first guess.

Explain Example 1.1

• The user explains who benefits from the project.
• The input and output define the smallest useful workflow.
• The first milestone is concrete enough to test.

Now explain the example out loud or in a Markdown cell. Use short sentences: “this line creates…”, “this name stores…”, “this output appears because…”. If you cannot explain a line yet, run only the lines above it and inspect the values that exist at that moment.

Challenge 1.1

NoteChallenge

Add a tempting feature, such as AI recommendations, then decide whether it belongs in version one or a later stretch goal. Cutting scope protects completion.

Show a safe way to approach the challenge

1. Copy Example 1.1 into a new Colab cell.
2. Change exactly one value, name, condition, or line.
3. Write the expected output before running the cell.
4. Run the cell and compare the actual result with your prediction.
5. If the result surprises you, undo the change and try a smaller one.

Suggested first move: Add a tempting feature, such as AI recommendations, then decide whether it belongs in version one or a later stretch goal.

Debugging checkpoint 1.1

WarningDebugging checkpoint

Avoid project ideas that require many unknown skills before producing any result. Choose a thin slice: one input, one transformation, one output, one test, and one explanation.

Do not debug by rewriting the whole example. Read the error type or surprising output, inspect the closest value with print(...) or type(...), then change one thing. This is the same routine you will use in larger projects.

Apply it

Write a one-page proposal with problem, user, input, output, first milestone, risks, and stretch goals. Then cut at least one feature.

Finish by adding a Markdown cell that answers: What did this example teach me that I can reuse in a project?

Key points

• Learn the concept by running a complete, small example first.
• Predict before execution so your thinking becomes visible.
• Change one thing at a time so cause and effect stay clear.
• Treat errors as clues about the exact line or value Python could not handle.

Why this matters

Choose a small capstone and break it into realistic milestones.

This lesson combines related subtopics that belong together in one learning conversation. You will still pause for a quiz after each section, but you do not need to jump between separate pages while building one clear explanation.

NoteGuiding questions

By the end of this lesson, you should be able to answer:

• How do the sections in Project Selection and Planning fit together?
• Which small example demonstrates each section?
• Which debugging clue should I check first for each section?

NoteLearning objectives

You will practice how to:

• explain the shared concept for this lesson;
• use each section as one step in a larger workflow;
• complete 2 short section quizzes before moving on;
• connect examples, mistakes, and debugging routines.

Lesson map

• 1. Choosing a Project — Select a capstone project that is small, useful, and demonstrable.
• 2. Project Planning — Break a capstone into milestones, tasks, risks, and checkpoints.

1. Choosing a Project

Select a capstone project that is small, useful, and demonstrable.

TipAnalogy

Choosing a project is choosing a hiking trail: the best trail is challenging but reachable with your current gear.

What this means

A good beginner project has a clear user, input, output, and success criterion.

Example 1

Predict what will happen before you run the code.

idea = {"user": "student", "input": "study log", "output": "weekly summary"}
print(idea)

Step-by-step explanation

1. idea = {"user": "student", "input": "study log", "output": "weekly summ... — pause here and say what this line reads, creates, changes, or displays.
2. print(idea) — pause here and say what this line reads, creates, changes, or displays.

After running the example, compare the actual output with your prediction. If they differ, do not erase your prediction. The difference is the part that can teach you the most.

Challenge

NotePractice

Change one input value, predict the new output, run the code, and explain the difference in one sentence.

Show one possible solution path

1. Copy Example 1 into Colab, Jupyter, or a .py file.
2. Mark the line you plan to change.
3. Write a one-sentence prediction.
4. Run the changed code.
5. If the result surprises you, restore the original and change a smaller part.

The goal is not to find the only correct answer. The goal is to create a small experiment where you can explain cause and effect.

Common mistakes

WarningCommon mistake

A project that is too large hides learning. Choose a tiny complete version first.

When you get stuck, use this debugging routine:

1. Read the last line of the error message or inspect the unexpected output.
2. Find the smallest line of code that could be responsible.
3. Print or inspect the value and type at that point.
4. Change one thing.
5. Run again and record what changed.

Check your understanding

This quiz checks the ideas in this section before you move on.

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2. Project Planning

Break a capstone into milestones, tasks, risks, and checkpoints.

TipAnalogy

A project plan is a travel itinerary: destination, stops, timing, and backup options.

What this means

Planning turns a vague idea into small testable steps.

Example 2

Predict what will happen before you run the code.

tasks = ["load data", "clean data", "summarize", "plot", "write README"]
for task in tasks:
    print("[ ]", task)

Step-by-step explanation

1. tasks = ["load data", "clean data", "summarize", "plot", "write README"] — pause here and say what this line reads, creates, changes, or displays.
2. for task in tasks: — pause here and say what this line reads, creates, changes, or displays.
3. print("[ ]", task) — pause here and say what this line reads, creates, changes, or displays.

After running the example, compare the actual output with your prediction. If they differ, do not erase your prediction. The difference is the part that can teach you the most.

Challenge

NotePractice

Change one input value, predict the new output, run the code, and explain the difference in one sentence.

Show one possible solution path

1. Copy Example 1 into Colab, Jupyter, or a .py file.
2. Mark the line you plan to change.
3. Write a one-sentence prediction.
4. Run the changed code.
5. If the result surprises you, restore the original and change a smaller part.

The goal is not to find the only correct answer. The goal is to create a small experiment where you can explain cause and effect.

Common mistakes

WarningCommon mistake

Planning every detail before coding can delay learning. Plan the next small milestone, then iterate.

When you get stuck, use this debugging routine:

1. Read the last line of the error message or inspect the unexpected output.
2. Find the smallest line of code that could be responsible.
3. Print or inspect the value and type at that point.
4. Change one thing.
5. Run again and record what changed.

Check your understanding

This quiz checks the ideas in this section before you move on.

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Notebook and Colab practice

Open a blank notebook at https://colab.new. Use one section at a time: copy the Example 1, predict the result, run it, answer the section quiz, and then move to the next section. This is better than copying the entire page at once.

Instructor note

Teaching notes

• Treat each section as a short teaching episode.
• Pause for the section quiz before introducing the next section.
• Ask learners to compare sections: what stayed the same, and what changed?
• If time is short, teach the first two sections live and assign the rest as practice.

Key points

TipKey points

• Choosing a Project: A good beginner project has a clear user, input, output, and success criterion.
• Project Planning: Planning turns a vague idea into small testable steps.
• Use the section quizzes as gates: review before moving on if a quiz feels uncertain.

References

• Python packaging user guide: https://packaging.python.org/en/latest/
• The Turing Way reproducible research: https://book.the-turing-way.org/reproducible-research/reproducible-research
• Python Tutorial: https://docs.python.org/3/tutorial/
• Quarto OJS documentation: https://quarto.org/docs/interactive/ojs/
• ipywidgets documentation: https://ipywidgets.readthedocs.io/en/stable/