Project Instructions (Module 4: Exploratory Data Analysis with Notebooks)¶

WEDNESDAY: Complete Workflow Phases 1-3¶

Follow the instructions in ⭐ Workflow: Apply Example to complete:

1. Phase 1. Start & Run - copy the project and confirm it runs
2. Phase 2. Change Authorship - update the project to your name and GitHub account
3. Phase 3. Read & Understand - review the project structure and code

Open notebooks/eda_case.ipynb in VS Code, select the kernel (.venv), and run all cells. Confirm the notebook executes without errors and commit it with output visible.

FRIDAY/SUNDAY: Complete Workflow Phases 4-5¶

Again, follow the instructions above to complete:

1. Phase 4. Make a Technical Modification - make a change and verify it still runs
2. Phase 5. Apply the Skills to a New Problem

Phase 4 Suggestions¶

Make a small technical change to the example notebook that does not break it. Choose any one of these (or a different modification as you like):

• Add a new Markdown cell with a section heading and one observation about the data
• Change the color palette or chart style in a seaborn plot
• Add a second chart of a different type (e.g., add a box plot alongside a histogram)
• Add a cell that prints the number of missing values per column using df.isnull().sum()
• Add a cell that filters the DataFrame to a subset of rows and re-runs a chart on the subset

Re-run all cells after your change and confirm the notebook executes cleanly. Commit with output visible.

Phase 5 Suggestions¶

Phase 5 Suggestion 1. EDA on a Built-in Dataset (Directed)¶

Use seaborn's built-in datasets to perform EDA on a dataset different from the example. Being able to conduct an EDA on your own is a critical skill. You will do this again in the capstone. Seek to understand the process and be able to explore any data that interests you.

Steps:

• Choose a dataset from seaborn (see list below)
• Create a new notebook file: notebooks/eda_yourname.ipynb
• Follow the same numbered section structure as the example
• Include: shape, dtypes, missing values, descriptive statistics, and at least two charts
• Add Markdown narrative cells explaining your observations after each section

Good seaborn datasets for practice:

• tips — restaurant tipping data (244 rows, 7 columns)
• iris — flower measurements (150 rows, 5 columns)
• mpg — car fuel efficiency (398 rows, 9 columns)
• titanic — passenger survival data (891 rows, 15 columns)
• diamonds — diamond prices and attributes (53940 rows, 10 columns)

Load with: df = sns.load_dataset('dataset_name')

Then:

• All column names will change.
• Update the notebook to reflect the columns and content of your data.
• Describe the dataset: what each column represents and where the data comes from
• Identify one surprising or interesting pattern you found
• Explain what a next analytical step might be (e.g., grouping, filtering, modeling)

Phase 5 Suggestion 2. EDA on Your Own Dataset (Original)¶

Perform EDA on a dataset you bring yourself. Being able to get value out of data is one of key skills of a data analyst. Try it on any data you find interesting.

Steps:

• Find a tabular dataset (CSV) relevant to your field or interests (e.g., from https://www.kaggle.com, https://data.gov, or your own work)
• Place the file in data/raw/
• Create a new notebook: notebooks/eda_yourname.ipynb
• Load the data with pd.read_csv()
• All column names will change.
• Update the notebook to reflect the columns and content of your data.
• Follow the same numbered section structure as the example
• Include: shape, dtypes, missing values, descriptive statistics, and at least two charts
• Add Markdown narrative explaining your observations

Then:

• Cite the data source and describe what it contains
• Identify at least one data quality issue you found (missing values, outliers, wrong types)
• Describe what question you would investigate next if you had more time

Key Skill Focus¶

As you work, focus on:

• how notebooks combine narrative and code for exploratory work
• how df.info(), df.describe(), and df.isnull() give a quick dataset overview
• how distributions reveal shape, spread, and outliers
• how grouping and filtering expose patterns within subsets
• how Markdown narrative turns a notebook into a readable analysis

Your goal is to produce a notebook that tells a clear story about a dataset.

Professional Communication¶

Make sure the title and narrative reflect your work. Verify key files:

• README.md
• docs/ (source and hosted on GitHub Pages)
• notebooks/ (executed notebooks committed with output visible)

Ensure your project clearly demonstrates:

• a complete EDA with all standard sections
• at least two meaningful charts with narrative
• a readable notebook someone else could follow