AbruvaTech

✨ Comment "ROADMAP" and I'll send you my free guide, Your First Year in Data Science, which shows where and how to start, what to learn first, and what to build so employers take you seriously.

🙋‍♀️ Make sure you're following me .datascience so Instagram can send it to you

I got 3 data role job offers in 3 months. In THIS tough market.
Most people spend 6+ months on random tutorials and get nothing.
I shrunk that timeline.
✅ No more Googling "what should I learn?"
✅ No more building random projects that don't get interviews
✅ No more applying to 100 jobs and hearing nothing
I'm handing you the exact roadmap to break into data. The right skills. The right projects. The resume that works.
Presale: $97 USD (goes to $197 at launch).
Comment OFFER and I'll send you the link. 🍁
Canada 🇨🇦 🇺🇸 USA Europe Ireland 🇮🇪 uk 🇬🇧 Australia 🇦🇺

Uncomfortable truths about data science jobs that no one warns you about before 👇
A data science job isn't just building models. Most of it is the messy human work around them.
📍 You rarely get a clear problem. The real skill is asking the right questions before you touch any data.
📍 No one fully understands your job: not your family, not your manager, not your product manager.
⚠️ Stakeholders may not trust your work, and you still need to explain it in plain English, calmly.
✅ About 80% of the job is thinking, communication, and problem framing. Machine learning modeling is the smaller part.
✅ You'll wear multiple hats: managing stakeholders, listening to your manager, and still making the right analytical call.
These are the skills that separate a good data scientist from a great one, and the ones nobody teaches in a data science course.
Send this to a data scientist who needs to hear it, and follow .datascience for more honest talk about working in data 🍁

The real reason I get 2-3 job offers every time I switch, even in a hard and competitive market. 👇
It's not luck. An interview is an exam you can study for, if you know what's on the test.
📍 SQL that holds up under pressure
📍 A/B testing questions, explained step by step
📍 Statistical principles you can say out loud
📍 Product and business sense, the round that separates candidates
✅ One full cheat sheet to review before every interview
⚠️ Random studying feels productive but doesn't get you hired
That's the difference between hoping for an offer and choosing between them.
The 5 data science interview guides are linked in my bio 🔗
Save this and follow for more data science career concepts made simple.

You're not failing DS interviews because you're not smart enough. You're failing because nobody told you what they actually test. 📍
Most people open their laptop, don't know where to start, and burn weeks studying the wrong stuff — then freeze the second the interview actually starts.
I've spent 13 years in data science. I've sat on both sides of the table — hundreds of interviews given, hundreds taken. I know exactly what separates a "thanks, we'll follow up" from an offer.
So I built 5 guides that cut through it: ✅
📍 SQL — basics to advanced
📍 A/B Testing — stats to real product decisions
📍 Statistics — descriptive to inference
📍 Product & Business Sense — metrics to trade-offs
📍 The complete interview-day cheat sheet
All 5 guides are linked in my bio. Grab them before your next interview, not after you've already frozen in one. 🍁
Which one are you most unprepared for right now: SQL, stats, or the product sense round? 👇

The habit that kept me stuck for 1 year: watching tutorials instead of building. ⏳

I thought I was learning. I was just consuming.

📍 Learn SQL inside a real project — comparing home prices, optimizing suggested orders, analyzing pay equity by salary, position, level.
📍 Learn Python on that same problem — same data, new questions, like predicting house prices.
✅ Learn GitHub while you build, not after — one repo per project, push any code, even one line.
⚠️ A messy repo with real commits beats a perfect tutorial certificate.

Pick one problem. Build it messy. Push it anyway.

Save this and follow for more real ways to break into data science. 🍁

Some of you won't like this... but tutorials are wasting your time. ⏳
Watching ≠ learning. Doing = learning.
📍 Learning SQL? Learn it with a real project, solving an actual problem — comparing home prices across cities, optimizing suggested orders in an app.
📍 Learning Python? Apply it to those exact same questions — then go further, like predicting house prices.
✅ Learning GitHub? Do it while you build all of the above, not separately. Create a repo for every project. Push any code, even if it's one line.
⚠️ Tutorials feel like progress. A shipped project actually is progress.
That's the difference between people who talk about data science and people who get hired for it.
Save this and follow for more real ways to break into data science. 🍁

The one thing that is killing your job search as a data scientist.

Last night I was at an LP concert. Her voice, her performance, the whole show — genuinely one of the best I've ever seen. And the first thing I thought was: why are her tickets so cheap? Why doesn't everyone know about her?

Meanwhile, singers with way less talent are selling out arenas.

It's not talent. It's visibility.

📍 LP is a full package. Unique voice, real artistry, unforgettable live performance.
✅ What she's missing isn't skill. It's the visibility other artists are actively building.
⚠️ That's the exact same reason your job search is stuck — you're not less qualified, you're less seen.

Being good at your job isn't enough if no one knows you exist. Visibility isn't optional in data science — it's part of the work.

Save this and follow for the data science lessons nobody else is sharing. 🍁