Learning Data Science One Mistake at a Time

A few years ago, my job role shifted from Community Manager to... well, let's call it "ambiguous tasks" that eventually morphed into Social Media Analyst. Suddenly, my work became heavy on data and reports. And I mean SO MANY REPORTS; there were times I was literally buried alive in spreadsheets. My boss? He had zero filter when it came to letting the wildest requests land on my plate. (We'll revisit that story another time.)

At the beginning of last year, I began to see endless possibilities in my role, realising how data could help me achieve my goal of changing the way brands communicate. There was also the tantalising possibility of creating actual data-driven change that went beyond just brand metrics. What do I mean? Picture this: learning how Kenyan consumers really feel about brand communication and using that insight to see if brands are actually walking the talk.

The problem? I lacked the technical skills to execute these big ideas floating around in my head.

I spent much of the year diving into online classes, mostly around data analytics; that is, when I wasn't having mental breakdowns or fighting for change in the country (IYKYK). Enter my Python obsession: I was absolutely smitten, but apparently the feeling wasn't mutual; she was constantly out for blood.

During this time, someone introduced me to Moringa School, a tech school offering different courses. But I didn't take it seriously until I caught myself making excuses for not practising Python. As anyone who's learned a programming language knows: if you don't practice, you're toast.

So, in my "Never Give Up, Never Surrender" era, I circled back to Moringa. I liked their course structure and timelines, but what I found lacking were genuine testimonials from past students. The posts I found were frustratingly vague, despite clickbait titles like "MY TIME AT MORINGA" or "WHAT YOU NEED TO KNOW ABOUT MORINGA." That was... disconcerting.

As someone with a full-time job, I needed to understand the course format to figure out how to balance work and school. Despite not knowing what to expect, I took the leap and applied.

So here I am, writing the review I wish I'd found when I was researching. Consider this my gift to anyone considering Moringa: details that could actually ease your journey.

What You Should Know (If You're Considering Moringa)

If your job is intense and fast-paced, do NOT sign up for the full-time remote class. Take the part-time route.

The courses move fast — like, no-joke fast. Morning classes might run 1-2 hours, but on heavy days, they can stretch to four hours. Depending on your Teaching Mentor (TM), they might split this into manageable blocks throughout the day. Each month brings a new phase, each week a new topic, and each day a new subtopic. It's relentless.

Self-learning is absolutely key. The classes are guides, not exhaustive lessons. You're expected to use the provided materials, explore beyond them, and actively teach yourself. My advice? Plan, over-plan, and then over-over-plan. Do the labs (they actually test your understanding), and never hesitate to ask questions.

Learn some Python before you start. Seriously. Mosh Hamedani is a fantastic online instructor — check him out.

Brush up on basic statistics. Even if you just go through fundamental concepts, you'll appreciate how they break down complex formulas into simple for-loops. It honestly makes understanding what's happening in those intimidating equations so much clearer.

My Experience So Far

As I write this, I'm still knee-deep in the course, so I can't tell you if my strategy fully worked (yet). But the experience? Wild, enlightening, and occasionally terrifying.

It feels like bungee jumping; you're falling fast, and just when you think you're getting used to the speed, it ramps up again. You might have to sacrifice your social life or that side hustle you've been nurturing. Also, fellow millennials: stay open-minded, especially during the life skills sessions (yes, you'll be in a room full of younger folks, and that's okay).

Surprisingly, my least favourite part has been the group projects. I'd forgotten how chaotic school groups can be after being spoiled by working with A+ teams where workflows are smooth and everyone knows their role. Going back to school, group dynamics is... an adjustment. Still, I've made friends with a fellow weirdo, so that's a win.

But seriously, why are school groups always chaotic? If you're that person who ignores the agreed plan and picks chaos over collaboration, please stop. Our grades depend on this, and ADHD folks like me need some semblance of order, or it triggers anxiety. Contribute ideas, follow the flow, and remember: this is practice for the real world. Be a team player, not the final straw.

Where I'm Struggling

Statistics. Yes, I know data science is mostly stats — blah blah blah — but it's been a tough romance. I'm working through it by taking on practice projects during school breaks. One project I'm excited about: analysing what consumers actually enjoy in brand content versus what brands think consumers enjoy. I suspect the answers will surprise me in ways I'm not prepared for.

There were moments in statistics that I genuinely enjoyed before getting overwhelmed again, like hypothesis testing and A/B testing, topics I'd already encountered in real work, just in a different context and with different methods.

At The End Of The Day

I'm still learning, still stumbling, still figuring things out. But here's what I know: growth isn't linear, and even when the pace feels overwhelming, I'm holding onto my "never give up, never surrender" mindset. One day at a time.

I don't need to sell you on the benefits of taking courses like Data Science; I'm sure you already know them. If you're still hesitating, consider everything above, get organised, and leap like I did!

I'll be sharing more updates on this journey as I progress, so stay tuned for the next chapter of controlled chaos.