I’m Kayla, and I actually took the data science path at Santa Monica College last year while working part-time at a cafe. I wanted real skills without going broke. Did it work? Mostly yes. Let me explain what it felt like, class by class and week by week.
Curious about the official roadmap? You can skim SMC’s official Data Science certificate overview to see every requirement laid out semester by semester.
The classes felt hands-on, not just talk
The pace started gentle. We used Jupyter notebooks right away, which helped. I coded in Python using pandas and NumPy. We made small charts with Matplotlib and Seaborn, then moved on to scikit-learn for models. It wasn’t magic. It was practice. Lots of it.
If you’re curious how a formal minor compares, I detailed the course load and surprises in this deep-dive on finishing a data science minor.
- In stats, we did hypothesis tests and confidence intervals on real data. Not fake toy stuff.
- In linear algebra, we played with vectors and matrices and used them to see how models learn.
- In the intro to databases class, I wrote SQL queries until they finally made sense. SELECT felt like a new language at first. Then it clicked.
Some nights I sat in the STEM Center with a giant iced tea, pushing through loops and joins. I liked that the labs matched the lectures. When we covered regression in class, the lab had me build one. No fluff.
Real projects I built (that I can still talk about)
These were my favorite part. I kept them in my GitHub and showed them in interviews.
- Santa Monica Breeze Bike Share: I pulled old trip data and tried to predict busy hours by station. I used linear regression and a random forest. The random forest won by a bit. I added weather and day-of-week as features.
- LA 311 Calls: I mapped complaint hot spots near the beach vs inland. I cleaned messy dates and missing fields. I made a simple dashboard with Plotly. It wasn’t pretty at first, but it got better.
- Farmers Market Sales: I joined weekly vendor data with holiday flags. Sales dipped on rain days. Shocker, I know. But seeing it in a plot felt cool.
- A tiny Kaggle challenge: Our club picked a “Playground” set. We split tasks like adults—badly at first. Then we made a plan. I handled feature engineering. We got a score in the middle of the pack, but we learned a ton.
If you ever need an open dataset on radio signal propagation to play with, the logs over at vhfdx.net are surprisingly rich and free to download.
I used Google Colab most of the time because my laptop is… humble. Colab timed out on me more than once. That part annoyed me. Still, it worked.
Teachers who show up (and tell you when you’re wrong)
Office hours saved me. One professor sat with me for 20 minutes and walked through my messy feature set. He didn’t sugarcoat it. “You added noise,” he said. He was right. I took half of it out and the model got better. Another instructor was big on clear code and docstrings. At first I rolled my eyes. Later, I saw why it matters when teammates read your work. If you want a sense of who teaches what, the Computer Science & Information Systems department page lists current faculty and upcoming course rotations.
The STEM Center had tutors who explained stats in plain words. No ego. They also had whiteboards everywhere, which helped me think.
Clubs, pizza, and people who like weird charts
The Data Science Club met on Thursdays. We had pizza more often than we should’ve. We shared plots, asked silly questions, and fixed each other’s bugs. A few of us went to a weekend hackathon at UCLA. We didn’t win. We did meet folks who now message us when jobs pop up. Worth it.
If you’re shy, you’ll still be okay. But it helps to sit by someone and say hi. Study buddies make a huge difference.
Santa Monica’s student body is incredibly diverse—almost half of my classmates were Latina—and conversation sometimes drifted from Python packages to dating app recommendations during late-night study sessions. If you’re curious about where to start, this data-driven rundown of the best Latina dating apps breaks down safety features, pricing tiers, and real-user reviews so you can decide which platform is worth your swipe-time. One tangent even involved a classmate from Florida who swore by a classifieds-style site instead of traditional apps; she pointed us to an in-depth guide to Skip the Games in Sebastian that walks through local etiquette, vetting tips, and which listings are worth replying to if you’d rather cut past endless swipes.
Career help that wasn’t a snooze
SMC’s career folks ran resume clinics and mock interviews. I brought a messy resume with too many bullet points. They cut it down and told me to add my projects with short problem-solution lines. That simple change got me a callback. I landed a small, paid summer internship with a local startup. I used Python, did some SQL, and learned how to talk about results with non-tech people. Scary at first. Better by week two. I also shared my real take on data science jobs in Los Angeles if you want a street-level view of the market.
If you want to transfer, the counselors know the UC and CSU paths. I got a clear plan for prerequisites and GPA goals. But you must book early. Appointments fill fast near deadlines.
Campus life, buses, and the beach pull
The Big Blue Bus pass in the student fees? I used it a lot. Parking is tight. The bus solved it. I liked studying outside near the palm trees, but—real talk—the beach pulls you. I had to set rules: work first, sand later. If you’ve got a busy life like me, SMC’s night and online options help. I stacked two in-person classes and one online each term. That balance kept me sane.
What bugged me (because nothing’s perfect)
- Some classes filled fast. I waitlisted twice. Get your add code ready and email early.
- Skill levels varied. Group work got tricky when half the team was brand new and half had coded for years. We managed, but it was uneven.
- A few campus computers felt old. Google Colab saved me more than once.
- One course had heavy homework with light feedback. I learned, but I wanted more notes on what I did wrong.
Who should try this
- You want to switch careers without spending a fortune.
- You learn by doing, not just listening.
- You can handle a bit of math. Not scary math, but steady math.
- You’re okay asking questions. Quiet is fine. Silent forever is not.
Still deciding whether data science is even the right major? I wrestled with that question in this candid breakdown of the major’s pros and cons.
Quick pros and cons
Pros:
- Affordable, especially for California residents
- Real projects you can show
- Supportive tutors and helpful career services
- Flexible schedules and solid transfer guidance
Cons:
- Popular classes fill early
- Mixed skill levels in group work
- Some aging lab gear and Colab timeouts
- Feedback quality varies by instructor
My bottom line
Santa Monica College gave me real skills I use. I learned Python, built models, and shipped small things that worked. I made friends who geek out about charts. I got help when I asked. It wasn’t fancy. It was steady. And steady wins.
Would I do it again? Yeah. I’d register earlier, grab study buddies sooner, and start my final projects two weeks before they were due. You know what? I’d also bring snacks. Long labs go better with pretzels.
If you’re on the fence, sit in on a class, peek at a syllabus, and talk to a counselor. If the projects make your brain light up a little, that’s your sign.