I wrapped up a data science minor last spring. I went in curious. I came out tired, happy, and a bit nerdy. This is my honest take on the requirements, with real things I did and messed up along the way.
The Short List: What I Had To Take
My school’s checklist had six parts. Yours may look close to this:
- Intro coding (Python or R)
- Calculus and linear algebra (the math with limits, vectors, and matrices)
- Probability and statistics
- Data wrangling and databases (SQL)
- Machine learning or modeling
- Ethics or a capstone project (or both)
That’s the skeleton. The meat is in the work. For instance, UC Berkeley’s Data Science minor requirements mirror this lineup almost course-for-course, as outlined on their official page.
What Those Classes Actually Looked Like
Let me explain how it played out week to week. It wasn’t magic. It was practice.
1) Intro Coding: Python 101 that got very real
We used Jupyter Notebooks and Google Colab. I liked Colab because my laptop ran hot like a toaster.
- Real example: I cleaned a messy “NYC taxi trips” file. It had broken dates, weird zeros, and a driver ID column that meant nothing. I used pandas to drop junk rows, fixed date formats, and made a quick chart with seaborn. It felt like washing dishes, but for data.
- Tiny win: I wrote a loop to flag trips under 1 minute. Turns out, many were errors. That saved my later stats work.
2) Calculus + Linear Algebra: The math I swore I’d never need… until I did
At first, I didn’t see the point. Then I hit machine learning.
- Real example: In linear algebra, I learned matrix multiplication. Later, in ML, it explained how a neural net passes signals. I finally saw why “dimensions” matter.
- Calc example: We looked at change over time. It helped when I modeled bike-share demand swings by hour. Peak rush hour wasn’t random. The curve told a story.
3) Probability and Statistics: Where the questions get sharp
This class taught me to ask, “Is this signal real, or is it noise?”
- Real example: I used NBA player data to test if three-point shooters had better plus-minus on average. I used a t-test. The early result said “yes,” but then I checked sample size and outliers. After fixes, the effect was smaller. Not gone, but smaller. Lesson: clean first, brag later.
- Another one: I took the Titanic dataset. I built a simple logistic regression. Women and children had higher survival odds. We talked odds ratios in plain English. That felt good.
4) Data Wrangling + Databases: SQL made me slow down, in a good way
We used PostgreSQL and a bit of SQLite.
- Real example: A music streaming dataset had user plays, songs, and artists. I wrote a JOIN to find the top 10 artists by unique listeners in June. Then I filtered by country. The biggest time sink was fixing the date column format. Again with the dates!
- Side quest: I made a tiny data dictionary. Not fancy. Just a doc with column names and notes. It saved me hours later.
5) Machine Learning: The buzzword class that made me humble
We used scikit-learn in Python. Models are cool. But data prep matters more.
- Real example: I built a model to predict late food orders from a campus kitchen. Features: time of day, rain, size of order, distance. Baseline accuracy looked great at first. But the data was imbalanced. Most orders weren’t late. So “always on time” looked… good. I used F1 score and a confusion matrix to fix my view. Much better picture.
- Another: I tried random forest, then logistic regression. Random forest won by a hair. But the simple model was easier to explain. My team picked simple. Our prof nodded.
6) Ethics + Capstone: Where the “should we” lived
We talked bias, consent, and fairness. Not fluffy. Real.
- Real example: We studied a facial recognition case where darker skin tones got worse results. We mapped how harm can spread when you train on skewed data.
- Capstone example: My team used Airbnb listings and crime data to explore price and neighborhood patterns. We kept addresses fuzzy to protect privacy. The final deck showed clear limits. We said what the data could not tell us. That felt honest.
Hidden Rules I Didn’t See Coming
- You need a C or better in the math and stats classes. Some schools want a C+.
- No pass/fail on core classes. Painful, but fair.
- You can’t double count too many courses with your major. I lost one overlap. That pushed me into summer.
- A short placement test decided if I could skip pre-calc. I couldn’t. I took it. Worth it.
- Group projects eat time. Plan for the “Can we meet at 8 p.m.?” texts.
- Thinking of aiming for a flagship program? I applied to UC Berkeley’s track, and here’s how the acceptance rate felt from the inside. Other universities—like the University of Minnesota, which posts a concise overview of its graduate data science minor—lay out similar expectations in their curriculum guide.
Tools We Actually Used (and why)
- Python (pandas, NumPy, scikit-learn), R for a few labs
- Jupyter and Google Colab (free GPU sometimes, bless it)
- SQL on PostgreSQL; a bit of SQLite for quick tests
- Tableau for dashboards; Matplotlib and seaborn for plots
- Git and GitHub for version control (merge conflicts are a rite of passage)
- Datasets: NYC taxi, Titanic, UCI Iris, NOAA weather, CDC, NBA stats, Airbnb listings
I even pulled a weekend scrape of VHF radio spot reports from vhfdx.net to sharpen my time-series cleaning chops on truly scrambled logs.
Small tip: use environment files. My team had one person on Windows, one on Mac, and me on a Chromebook hack. Same package versions saved fights.
A Week That Stuck With Me
- Monday: Stats lecture on confidence intervals. Quick quiz.
- Tuesday: SQL lab. I wrestled with a LEFT JOIN and lost. Then won.
- Wednesday: ML office hours. Fixed my bad cross-validation split.
- Thursday: Team meeting. We set our capstone scope smaller. We made a punch list: map, model, write-up.
- Friday: I built charts. I deleted half. The story got cleaner.
Was it hard? Sometimes. Was it fair? Yes.
What I Loved
- Hands-on labs. Less talk, more build.
- Clear rubrics. I knew how my work was judged.
- Instructors who cared about plain words. No fog.
- Projects with real, public data. It felt useful.
What Bugged Me
- Prereqs made term planning tight. One wrong move, and you add a semester.
- Group work grading felt uneven sometimes. We fixed it with peer reviews, but still.
- Office hours filled fast near deadlines. Book early. I learned the hard way.
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Who Should Pick This Minor
- If you like puzzles and patterns, yes.
- If you want to tell stories with numbers, double yes.
- If you hate math, you can still do it, but be ready to practice. A little each day beats a long Sunday panic.
If you're on the fence about going all-in, here's my honest look at whether data science is a good major as well as a minor.
You know what? I’d do it again. The requirements looked stiff on paper. In real life, they built muscle. Not flash. Muscle.
My Quick Advice If You’re Starting
- Don’t stack calculus and machine learning in the same term. Spread the load.
- Learn basic Git early. You’ll thank yourself.
- Write short notes after each lab. What