DATA analyst Project 1-invisible-load-working-mothers

 

PHASE 1: Foundation (DONE / ALMOST DONE)

✅ 1. GitHub Setup

  • Repository created

  • Folder structure created

  • README.md added (paste the content we prepared)

📌 Goal: Project looks real and professional from day one.

PHASE 2: Define the Problem (VERY IMPORTANT)

2. Write Survey Questions (survey/survey_questions.md)

You will define:

  • Who the survey is for

  • What you are measuring

  • How each question maps to analysis

Sections to include:

  • Demographics (age range, number of kids, job type)

  • Paid work hours

  • Unpaid work hours

  • Mental load (planning, remembering, coordinating)

  • Support system

  • Well-being (stress, burnout, sleep)

  • Career & dreams

📌 This controls your entire analysis.

PHASE 3: Data Collection

3. Create Survey

  • Tool: Google Forms or Typeform

  • Make responses anonymous

  • Use scales (1–5) where possible

4. Collect Data

  • Share quietly (DMs, small groups)

  • No pressure on numbers

  • Target: 50–100 responses

5. Save Raw Data

  • Download responses as CSV

  • Upload to:

data/raw/survey_responses_raw.csv

📌 Never edit this file.

PHASE 4: Data Cleaning

6. Clean the Data

Tools:

  • Excel / Google Sheets / Python

Tasks:

  • Remove duplicates

  • Handle missing values

  • Standardize hours (per week)

7. Create New Columns

Examples:

  • total_work_hours

  • unpaid_labor_hours

  • mental_load_score

  • support_index

  • burnout_level

8. Save Clean Data

data/cleaned/survey_responses_cleaned.csv

PHASE 5: Exploratory Data Analysis (EDA)

9. EDA Notebook (notebooks/eda.ipynb)

Include:

  • Summary statistics

  • Distributions

  • Correlations

  • Early charts

Answer questions like:

  • Paid vs unpaid work?

  • Does support reduce burnout?

  • Where does time disappear?

📌 Comment your thinking — recruiters read this.

PHASE 6: SQL Analysis

10. SQL Queries (sql/analysis.sql)

Examples:

  • Avg unpaid hours by support level

  • Burnout vs total workload

  • High performers vs rest time

Even simple SQL is enough.

PHASE 7: Insights & Storytelling

11. Write Insights (insights/findings.md)

Structure:

  • Key insight

  • Supporting data

  • Human meaning

Example:

Women without support work an average of X unpaid hours per week, equivalent to a second job.

PHASE 8: Visualization

12. Dashboard (dashboard/tableau/)

Dashboard sections:

  • A day in her life

  • Invisible work breakdown

  • Burnout risk

  • Support impact

Save:

  • Tableau file

  • Or screenshots

PHASE 9: Final Polish

13. Export Charts

Save images to:

visuals/charts/

14. Update README

Add:

  • Summary of findings

  • Link to dashboard

  • Screenshots

PHASE 10: Sharing

15. LinkedIn Post (End of Month)

  • Introduce project

  • Share why it matters

  • Link GitHub

Now you’re not “starting” —
you’re showing real work.

🧠 Golden Rules (Remember These)

  • Raw data is never edited

  • Document every decision

  • Simple analysis > complex but unclear

  • Story matters as much as charts

NEXT ACTION (Do this next)

👉 Write survey questions

When you’re ready, just say:
“Survey questions”
and I’ll help you write them line by line, no overwhelm.

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