Project 1: Personal Data Dashboard

Build a dashboard that summarizes your own daily data.

Project Overview

Attribute Value
Difficulty Level 1: Beginner
Time Estimate Weekend
Main Language Python
Alternative Languages R, JavaScript
Knowledge Area Descriptive statistics
Tools Spreadsheet or CSV
Main Book “Naked Statistics” by Charles Wheelan

What you’ll build: A simple report that shows averages, variability, and trends from your own data.

Why it teaches stats: Personal data makes statistics meaningful and interpretable.

Core challenges you’ll face:

  • Defining measurable variables
  • Cleaning data consistently
  • Interpreting summary stats

Real World Outcome

You will produce a dashboard with charts and summary metrics.

Example Output:

Average sleep: 7.2 hours
Std dev sleep: 0.9
Most productive day: Tuesday

Verification steps:

  • Check for missing entries
  • Validate summary values against raw data

The Core Question You’re Answering

“What does my own data say about my habits and patterns?”

This project makes statistics personal.

Concepts You Must Understand First

Stop and research these before coding:

  1. Mean and median
    • When do they differ and why?
    • Book Reference: “Naked Statistics”, Ch. 2
  2. Variance and spread
    • What does variability say about consistency?
    • Book Reference: “Naked Statistics”, Ch. 3
  3. Time series summaries
    • How do you compute weekly trends?
    • Book Reference: “Practical Statistics for Data Scientists” by Bruce & Gedeck, Ch. 1

Questions to Guide Your Design

  1. Metric selection
    • Which variables matter most (sleep, steps, study)?
    • How will you handle missing days?
  2. Visualization
    • Will you use line charts or bar charts?
    • How will you highlight trends?

Thinking Exercise

Consistency

If your average sleep is 7 hours but standard deviation is 2 hours, what does that tell you?

Questions while working:

  • Why can the same mean hide variability?
  • What does consistency mean for habits?

The Interview Questions They’ll Ask

Prepare to answer these:

  1. “Why are averages sometimes misleading?”
  2. “What does standard deviation measure?”
  3. “How do you detect trends over time?”
  4. “How do you handle missing data?”
  5. “Why is descriptive stats the first step?”

Hints in Layers

Hint 1: Starting Point Start with a single variable like sleep hours.

Hint 2: Next Level Add weekly summaries and trend lines.

Hint 3: Technical Details Use rolling averages to smooth noise.

Hint 4: Tools/Debugging Cross-check computed stats with spreadsheet functions.

Books That Will Help

Topic Book Chapter
Mean/median “Naked Statistics” Ch. 2
Variability “Naked Statistics” Ch. 3
Trends “Practical Statistics for Data Scientists” Ch. 1

Implementation Hints

  • Keep data in a simple CSV.
  • Document your definitions for each metric.
  • Use consistent units.

Learning Milestones

  1. First milestone: You can compute basic summaries.
  2. Second milestone: You can visualize trends.
  3. Final milestone: You can interpret your own data confidently.