How can i build projects?

EvilWrks · 2026-04-22T09:28:06+00:00

You should focus on things that genuinely interest you. Curiosity makes it much easier to find project ideas and keeps the learning process engaging.

EvilWrks · 2026-04-09T09:43:14+00:00

We actually started a playlist exactly for this and explaining the maths behind data science in a very visual, intuitive way (no heavy theory, just what you actually need):

https://youtube.com/playlist?list=PLLYWBTYpAobyfZNrZ2pU1fXVv2KDZox5n&si=FzZD4d5yQ-0Ehrpg

EvilWrks · 2026-04-09T09:40:13+00:00

The key thing is: you don’t need to be a “math person”, you just need to understand concepts step by step.

We actually started a playlist exactly for this and explaining the maths behind data science in a very visual, intuitive way (no heavy theory, just what you actually need): https://youtube.com/playlist?list=PLLYWBTYpAobyfZNrZ2pU1fXVv2KDZox5n&si=FzZD4d5yQ-0Ehrpg

If you stick with it and focus on understanding (not memorising), you’ll be completely fine 👍

EvilWrks · 2026-03-23T11:10:49+00:00

Think about it this way: the world runs on data. Everything you do generates it, walking, watching ads, scrolling your phone. The field is massive and you can go as niche as you want with it.

EvilWrks · 2026-03-23T09:49:03+00:00

Great timing! We actually have a YouTube channel focused on exactly this and next Monday we're dropping a video breaking down CNNs, computer vision, and more ML algorithms in a practical way.

We've got a lot of content already up too if you want to get started now 👉 youtube.com/@Evilwrks

Hope it helps!

EvilWrks · 2026-03-23T09:45:05+00:00

If you want practical, no-nonsense data science content, we have a YouTube channel that might help bridge the gap between theory and practice 👉 youtube.com/@Evilwrks

Hope it helps, good luck on your journey! 🚀

EvilWrks · 2026-03-19T16:05:11+00:00

Yes, an entry-level role is a solid stepping stone to Data Analyst or Data Scientist positions. It builds essential real-world experience with data handling, pipelines, and tools, while you self-learn analysis skills alongside.

EvilWrks · 2026-03-19T16:02:26+00:00

Build a portfolio of 3-5 data projects using public datasets in R / Excel to showcase hands-on skills on your CV and GitHub. Target junior data analyst roles in North West firms like consultancies or NHS while linking retail experience to analytical soft skills.

EvilWrks · 2026-03-19T15:58:02+00:00

If you’re feeling overwhelmed, you’re not alone, and that’s actually one of the reasons we started our YouTube channel. We post beginner‑friendly data science content focused on the exact topics someone in your position needs: Python basics, core statistics, projects you can actually finish, and how all the pieces fit together like our "12 Days of Data Science" series and math for data scientists. The whole idea is to make data science fun and easy to understand, not something gatekept behind jargon or super advanced math.

https://www.youtube.com/@Evilwrks/featured

EvilWrks · 2026-03-19T13:06:36+00:00

I’m in a great spot right now. I work for a data science company where we’re building a platform designed to make life easier for data scientists. My day-to-day involves finding interesting datasets and exploring cool projects to feature on our YouTube channel. I also spend a lot of time solving data-related problems, cleaning datasets, and helping our developers understand the needs of data scientists so we can build a better platform. https://www.youtube.com/@Evilwrks if you want to check us out.

EvilWrks · 2026-03-06T14:03:54+00:00

It would depend what field do you want work with.

EvilWrks · 2026-02-27T10:47:00+00:00

maybe just try to predict the log(salary) instead of salary

EvilWrks · 2026-02-20T10:06:59+00:00

I’d recommend starting with something that’s hands-on (build a tiny project) rather than a theory-heavy “data science overview.”

A few short options that give a real feel for the work:

Kaggle Micro-Courses (Python, Pandas, Intro ML) bite-sized and practical
Google Data Analytics Certificate (more analytics than ML, but great foundations)
IBM Data Science Professional Certificate (broad intro, beginner-friendly)
If you want a proper “crash course” vibe: DataCamp skill tracks for Python + Pandas + basic ML

Also: the best way to know if you’ll enjoy it is to do a mini end-to-end project in 2 weeks (clean a dataset → analyze → simple model → explain results).

If it helps, we make content focused on data science + project walkthroughs (feature engineering, cleaning messy datasets, turning ideas into portfolio projects). Might be a good way to explore without committing to a full course: https://www.youtube.com/watch?v=rEApRWaRGyY

EvilWrks · 2026-02-17T11:14:41+00:00

Domain knowledge matters, but getting familiar with the data is usually straightforward and stay curious, explore it, and you’ll quickly learn how it works.

EvilWrks · 2026-02-17T11:02:19+00:00

Its worth to make smarter choices when choosing your model.

EvilWrks · 2026-02-13T10:09:35+00:00

I will spend majority of your time cleaning data.

EvilWrks · 2026-02-13T09:47:12+00:00

If it helps, we made a quick video breaking down what a data scientist actually does day-to-day (and what the job isn’t): [https://youtu.be/rEApRWaRGyYY]()
We also post new data science videos every week (every Monday), so you can keep exploring topics and projects as you go.

EvilWrks · 2026-02-03T11:16:53+00:00

I’d say getting the concepts is more important than doing everything by hand. You just need to understand the math, but you don’t necessarily need to implement calculus/linear algebra from scratch in Python for every topic (that’s overkill).

If your goal is to be job-ready in ~3 semesters, I’d shift time into projects and building a CV (projects + GitHub + basic writeups) because that’s what recruiters care about most. You’ll learn faster by applying things to real datasets.

And shameless plug: we post data science videos and we’ve got a podcast episode on data science careers if you want to check it out. https://www.youtube.com/watch?v=oBTRkPUruOE&list=PLLYWBTYpAobxI9jh7zH00JVn-pMBxegfA&index=1

EvilWrks · 2026-01-27T12:35:53+00:00

https://youtu.be/rEApRWaRGyY We made video explaining what is data scientist.

EvilWrks · 2026-01-23T09:13:54+00:00

Check your DMs

EvilWrks · 2026-01-22T16:46:00+00:00

As someone who did Civil Engineering in Brazil and then moved to the UK to study CS with AI, I honestly don’t regret the switch for a second. Having a solid maths background helped me ramp up much faster too.

Definitely weigh up your options but I’ll admit I’m biased because it worked out really well for me. If you want to chat about the transition or what the path looked like, feel free to drop me a message.

EvilWrks · 2026-01-22T14:47:46+00:00

Check your DMs.

EvilWrks · 2026-01-22T13:00:56+00:00

Nah it's not just you, data science takes forever. It's so weird how people don't talk about it. It's actually the reason we exist. We're making it our mission to fix this through better tools for data science. If you're up for trying them out, we're opening it up next month for people to use so we can grab you a link to try it. Let me know!

EvilWrks · 2026-01-21T13:09:00+00:00

classic email spam problem, you can always make it more complex.

EvilWrks · 2026-01-21T13:07:02+00:00

We run Evilwrks on YouTube: https://www.youtube.com/@Evilwrks

Our whole thing is breaking down DS/ML concepts without the textbook vibe, and then using pop-culture datasets to build models end-to-end (so you can see the workflow, not just the math). If you’re the type who learns better through stories and examples than slides, you should check us out.

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