I’m Kayla, and I studied Data Science. I’m treating the major like a product I used for four years. I tested it in class, on real projects, at an internship, and in my first job. Here’s my honest review. For readers who want the longer, unfiltered story, I put together an even more detailed breakdown of the major’s pros and cons.
Quick Verdict: Worth It… if you like messy puzzles
Short answer? Yes. It was worth it for me. But only because I like puzzles, numbers, and people. If you want something clean and neat all the time, you might feel annoyed. Data gets messy. Plans change. People ask new questions. You have to roll with it.
Let me explain what it felt like, day to day.
What it felt like to study it
My first week, we wrote simple Python code in Jupyter Notebook. I remember my first real task: clean a giant CSV from a small bakery. The owner tracked sales by hand, then copied it over. Dates were wrong. Prices had commas. Half the rows had missing values. I used pandas to fix it. I felt smart… and also tired. Both can be true.
The best class project I did was a gym churn model. We used scikit-learn. We built a simple logistic regression to guess who might quit next month. Then we made a short plan for the gym: send a friendly check-in at week 6. Keep it human, not pushy. Seeing that model help one manager keep three members? That hit me.
Not all projects went smooth. One group project blew up. We tried to use a fancy neural net. It looked cool. It did worse than a plain decision tree. We learned the hard way: simple wins if it’s clear and stable.
My real internship: city health data
I interned at a city health department the summer after my junior year. I pulled flu case data from a PostgreSQL database, cleaned it in Python, and made a weekly Tableau dashboard. My first version was ugly and slow. My mentor said, “Join on keys. Keep only what you need.” I trimmed it down. It ran fast. A clinic used it to stock test kits before a spike. That felt helpful. Like, useful in a real way.
Tools I used there:
- SQL (lots)
- Python (pandas, matplotlib)
- Tableau for charts
- A tiny bit of R for a time series check
You know what? I thought the hard part would be math. But it was the people part. I had to explain a chart to a doctor who hated charts. I learned to say, “Three bullet points. One clear ask.”
First job: junior data analyst in retail
Right after graduation, I joined a retail company as a junior data analyst. Pay was fair for a first role. High 60s where I live. Forbes keeps an updated breakdown of data science salaries across experience levels and regions. I worked on weekly sales reports, A/B test reads, and a little SKU forecasting. I used SQL every day. Python most days. Sometimes Power BI.
One small win: I built a simple stock-out alert using a cron job and a Python script that checked inventory daily. When levels dropped below a threshold, it pinged Slack. It saved the team time. Nothing fancy. Just useful. Folks in bigger metros see slightly different trends—I summed up what I found while interviewing in SoCal in this reflection on data science jobs in Los Angeles.
What I liked (the good stuff)
- Real impact: Your work can help someone act today. Not next year. Today.
- Clear skills: SQL, Python, statistics, and a bit of data viz. You see yourself get better.
- Mix of brain modes: Logic for the code. Story for the people. I liked that balance.
- Friendly tools: Jupyter, pandas, scikit-learn, Tableau, Power BI. These are common and well supported.
What bugged me (the not-so-fun stuff)
- Messy data. Always. Typos. Duplicates. Weird time zones. You wrestle with it a lot.
- Math isn’t the wall. Communication is. If you can’t explain it, it won’t ship.
- Job titles are noisy. “Data Analyst,” “Data Scientist,” “ML Engineer,” “Analytics Engineer.” They blur.
- You need a portfolio. Classes help, but GitHub and one or two real projects help more.
- Group work can be rough. One person ghosts; you carry the bag.
If you want to see another real-world example of untidy, high-volume data—this time from radio propagation logs—take a quick look at vhfdx.net.
I’ve also put together a hands-on comparison of Business Intelligence versus Data Science roles if you’re still deciding which lane fits you.
Real classes that mattered most for me
- Intro Stats: confidence intervals, p-values, A/B logic.
- Data Wrangling with Python: pandas groupby, merges, cleaning text.
- Databases: SQL joins, indexes, query plans. Boring title, huge payoff.
- Machine Learning: cross-validation, feature engineering, avoiding overfit.
- Communication for Data: short write-ups, clear slides, stake-holder Q&A.
Funny thing—Databases was the sleeper hit. It helped me get work done fast.
Who should pick this major?
- You like puzzles and patterns.
- You’re okay being wrong on Monday and fixing it by Friday.
- You enjoy learning tools on your own. Docs, Stack Overflow, little experiments.
Who might hate it?
- If you want clean answers every time.
- If you don’t like coding at all.
- If talking to non-tech folks drains you a lot.
One side note: if you’re a queer woman in tech looking for solidarity while you grind through problem sets and job searches, there are welcoming online hangouts where you can swap study tips, vent about group projects, or find a late-night debugging buddy—check out InstantChat’s lesbian chat rooms where you can drop in anonymously and connect with other women navigating STEM degrees; the community is friendly, quick to share resources, and great for a morale boost.
On a totally different “keep-your-sanity” note, a few classmates who studied near Butte County complained that dating apps in smaller NorCal towns felt like ghost towns. If you’re in that boat and want to scope out which platforms actually have real local activity, skim this quick breakdown for Oroville—Skip the Games Oroville guide—it compares the major hook-up apps, lists which ones still have active users, and shares a few safety pointers so you can decide whether any of them are worth downloading.
What I wish I knew before I started
- Learn SQL early. It’s your daily bread.
- Keep a tidy GitHub. One solid project beats five half-baked ones.
- Make one project that solves a small real need. A local shop, a school club, a team at church. Real data beats fake data.
- Kaggle is fine—just write a short readme that explains your choices in plain words.
- Cloud basics help. I touched AWS S3 and Google BigQuery in school. That helped me land interviews.
Money and time: is it worth the cost?
Data Science can be pricey at some schools. The University of Virginia publishes clear career outcome data for its data science alumni that can help you see the kinds of roles graduates actually land. If cost is tight, I’d still say there are strong paths:
- Major in Statistics or Computer Science, then add a minor or a few data classes.
- Do a cheap Python course online. Practice with public data (city open data portals are great).
- Build a tiny portfolio and look for a data internship, even part-time.
If you’re eyeing a highly competitive program, here’s how the UC Berkeley Data Science acceptance rate felt when I applied.
A full major helped me because it gave structure, friends to study with, and a push to finish hard stuff. But it’s not the only path. Another viable route is a focused fellowship; I wrote up my honest experience of going through the Insight Data Science program for anyone considering that option.
One more real example: a tiny bakery model that almost failed
Back to that bakery. I made a neat forecast with Facebook Prophet. It looked sharp, but the owner just needed “how many croissants for Saturday.” So we made a simple moving average with a holiday bump. It won. Sometimes “good enough and clear” beats “fancy and fragile.” That lesson stuck with me.
Would I choose it again?
Yes, I would. I like the mix. I like turning messy tables into simple choices. I like charts that make someone nod. I’m not married to the title, though. If my job shifts toward product analytics or analytics engineering, that’s fine. The core skills