1What My 4-Month-Old Taught Me About Data Patterns

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It’s been 9 months since a became a mother. However, the distance motherhood takes you from your career and life in general is beyond comprehension.

After a successful normal delivery, which took almost a complete quarter to heal physically, I started noticing some patterns since she was 4 months old.

I realised became a data analyst again — not because I chose it, but because my baby forced me to.

Before my daughter arrived, I saw patterns in dashboards. Now I could see them in sleep cycles, feeding times, restlessness during nursing, and the exact minute she needs a diaper change. The tools changed. The mindset didn’t.

Here’s what my 4-month-old taught me about data patterns — and how it reshaped my approach to data science.

The First Pattern: Sleep Data Isn’t Linear

When my baby was newborn, I tracked every hour of sleep. I wanted to find the “perfect” schedule: 12 hours at night, 3 hours during the day, naps at 9 AM, 1 PM, and 5 PM.

Spoiler: That schedule doesn’t exist.

What I learned instead is that sleep data is cyclical, not linear. My daughter’s sleep patterns shifted every 3–4 weeks:

• Week 1–2: 9 hours night sleep, 2 hours day sleep
• Week 3–4: 7 hours night sleep, 3 hours day sleep (due to growth spurt)
• Week 5–8: 10 hours night sleep, 2.5 hours day sleep
• Week 9–12: Back to 8 hours night, but naps became inconsistent
• Week 13–16: stabilised at 9.5 hours night, 2.5 hours day

I was frustrated for the first two months because I was treating this like a time-series forecast when it was actually seasonal data with outliers.

In data science, I’d call this:

• Signal: The underlying sleep pattern (9–10 hours night, 2–3 hours day)
• Noise: Growth spurts, teething, colds, vacation disruptions
• Seasonality: The 3–4 week cyclical shifts

Something I would like to clarify again before we proceed. this was her sleep cycle which only lasted as long as she was held. My sleep was thrown our of the window for 5 months straight.

When I stopped trying to predict exact hours and started identifying the signal, I stopped stressing. Same principle applies to dashboards.

Pattern #2: Feeding Data Has Hidden Variables

Our daughter is breastfed. Early on, I noticed she was restless during feeds — pulling away, squirming, sometimes screaming. I thought: “Is she not getting enough milk? Is the flow too fast? Too slow?”

I started tracking:

• Time of feed
• Duration
• Her restlessness level (1–5 scale)
• What I ate 2 hours before
• Her position during feeding

The pattern emerged after 3 weeks: restlessness spiked when I ate spicy food 2–3 hours before feeding. Not immediately. Not randomly. With a 2–3 hour lag.

This is exactly like lag variables in data modeling. In Power BI, I’d set up a calculated column like:

sql

Restlessness_Lag_2Hr = 
CALCULATE(
    AVERAGE(FeedingData[Restlessness]),
    FILTER(
        FeedingData,
        FeedingData[FeedTime] - 2Hours = CurrentFeedTime
    )
)

I didn’t write that code. I lived it. My baby taught me that correlation doesn’t always mean immediate causation. Sometimes the effect shows up later.

Pattern #3: Correlation ≠ Causation (The Colic Drop Lesson)

When my daughter was 2 months old, she had what looked like colic. Restless, crying, pulling her legs up. I bought colic aid drops. Nothing happened.

I searched for answers. One article said colic drops (simethicone) often don’t work for babies over 1 year because symptoms that look like colic are usually gas, constipation, infection, or food issues — not true infantile colic.

I tracked:

• When she cried
• What I ate the day before
• Her poop frequency
• Whether she had a nose block or chest congestion

The pattern: Crying spiked when she had chest congestion + dry cough, not when she had “colic” at all. She had a mild respiratory issue, not digestive pain.

I learned my first real lesson in false positives:

• What looked like colic → was actually congestion
• What looked like “drop failure” → was actually wrong diagnosis
• What looked like random crying → was actually a pattern tied to illness

In data work, I’ve seen this too. A dashboard shows sales dropping. Everyone assumes it’s a marketing problem. But the real cause? A system outage that no one logged. The data showed the pattern, but the context was missing.

Pattern #4: Outliers Matter More Than You Think

At 3 months, my daughter slept 12 hours straight. One night. I filmed it. I sent it to my mom. I celebrated like she’d won an Olympic gold medal.

But then the next night? 6 hours. The night after? 8 hours.

I panicked. Was the 12-hour night a fluke? Was I reading the data wrong?

No. It was an outlier — and outliers teach you more than averages.

In data science, outliers can be:

• Errors (data entry mistakes)
• Edge cases (rare but valid scenarios)
• Signals of something new (a trend you haven’t detected yet)

My 12-hour sleep night was an edge case. Maybe she was extra tired. Maybe she had a growth spurt. Maybe the room was cooler. I don’t know. But I learned: don’t delete outliers. Investigate them.

Same with dashboards. If sales spike 40% one day, don’t just smooth it out. Ask: “What happened?”

Pattern #5: You Need Multiple Data Sources

I tried tracking sleep in one app. Feeding in another. Diaper changes in a notebook. Mood in my head.

It was chaos. I couldn’t see patterns because my data was fragmented across 4 sources.

Then I switched to one app that tracked everything: sleep, feeding, diapers, meds, mood. Suddenly, patterns appeared:

• Low sleep → high restlessness during feeding
• Night waking → correlated with spicy food I ate 3 hours before
• Congestion → worse sleep → more crying

This is data integration. In Power BI, I’d call it building a star schema with a central feeding table linked to sleep, diaper, and illness tables.

In real life, I call it: “Stop using 4 apps. Use one.”

The Big Takeaway: Data Is Human

Before my baby, I thought data was about accuracy. Precision. Perfect visualizations. Clean dashboards.

Now I know: data is about people.

• Sleep data isn’t numbers. It’s a tired mom trying to understand her baby.
• Feeding data isn’t timestamps. It’s a breastfeeding mom worried about her baby’s nutrition .
• Crying data isn’t volume. It’s a baby communicating discomfort.

The same applies to work dashboards. Sales data isn’t revenue. It’s:

• A customer deciding whether to buy
• A salesperson nervously waiting for a deal to close
• A product manager wondering if their feature works

Patterns matter. But context matters more.

What I’m Doing Now

I resumed work 3 months ago, after maternity leave. I bring my baby-taught data mindset everyday nwith me:

1. I look for lag variables, not just immediate correlations
2. I investigate outliers, not smooth them out
3. I integrate data sources, not fragment them
4. I ask “why”, not just “what”

Today, my daughter is 9months old. She can’t speak. She can’t write SQL. But she taught me more about data patterns than any certification ever did.

And that’s the real lesson: Data science isn’t about tools. It’s about seeing patterns in the chaos and understanding the human story behind them.

Thanks for reading. If you’re a parent in tech, I’d love to hear what your kids taught you about your career or anything in general. Drop a comment — let’s swap stories.

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