Tips I would tell my Data Scientist younger self

Entering the Data Science field? Feeling stuck in the growth of your knowledge in your first years?
I've recently shared some practical advice in "Data Science FAQ" by Zuzanna | @aiflavours, a good Twitter friend's latest ebook on diving into Data Science ( / 1517277433002696704 .
This is the unscripted, longer version of my tips in the ebook, with some life lessons and personal stories. I hope this helps you stay motivated and keep grinding on the Data Professional journey!
I do recommend checking the ebook after the video, it's full of great snippets and a useful roadmap for your Data Science / ML journey!
I also try to show my beginner Jazz piano skills in the video...
👉 Links
- Zuzanna's ebook if you're entering the Data Science field: aiflavours.com/data-science-faq/
- Streamlit website: streamlit.io/
- Kaggle: www.kaggle.com/
- pip-tools to generate pinned versions of dependencies in requirements.txt: github.com/jazzband/pip-tools/
- CookieCutter Data Science: github.com/drivendata/cookiec...
- CookieCutter Data Science explanation: drivendata.github.io/cookiecu...
- Python typing: realpython.com/python-type-ch...
- Mypy - static type checker: mypy-lang.org/
🐦Follow my daily updates on Twitter: / andfanilo
🗣️ My other links: linktr.ee/andfanilo
☕ Want to support me? www.buymeacoffee.com/andfanilo
Want to start the UA-cam Grind too? Here are some of my tools (Affiliate links)
🎵 Music (Epidemic Sound) - www.epidemicsound.com/referra...
🪶 17th video out of 35 for the year. While I try to do a video a week, I'm trying to slowly up the quality (especially given the positive feedback on the Epic Walkthrough) and the weekly schedule is just not sustainable. So I'm glad the yearly milestone is 35 videos instead of 50, I got a big video idea for the next one and it'll possibly take 2-3 weeks to do.
Also...this video is a bit more intimate and story-driven, and I LOVED producing it, I hope you like it too :-). Now I want to do more videos about stories of struggles in the Data Science industry, so like the video if you want more of those too. If you have a story to share, hit me up on Twitter or in the comments!
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⚠️ Disclaimer: This video is not sponsored. Links included in this description might be affiliate links. If you purchase a product or service with the links that I provide I may receive a small commission. Thank you for supporting my channel so I can continue to provide you with free content each week!

[@flight5056]

Why do I feel like the student with a raised hand ✋.. Great advice as always, keep em coming

[@michaelomosebi6374]

Wonderful advice. It gives me great comfort to not worry how long it has taken me to achieve what I have so far.
I will take my strides gently and calculatively going forward.
Thanks Fanilo.
I have a question: What do you think is the advantage of learning DS in PostGraduate University studies over internet grinding/bootcamps/self-learning?

[@andfanilo]

Thanks for watching and for the positive feedback. Even I rewatch this video every now and then to remind myself I'm on a lifelong journey on learning Data Science, with all the trends and technologies that appear every 2 years.
On the case of Uni vs Internet, I regularly see people that will tell you "all the resources are for free on the Internet, just find and practice them", or you have the "bootcamp which curates a number of resources to help you start your learning journey"
I did learn A LOT on the Internet, but I had my very favorite learning experience back in the ML course at University of Queensland, where I got to consistently, deliberately practice with comrades for 6 months, and we would get weekly feedback and improvement suggestions from our teacher.
IMO there is nothing like learning something new 1/ for a long time with PEERS that you learn to trust and give feedback to, to share experiences with and not repeat the mistakes they did; 2/ get constant FEEDBACK on what you practiced, both by your peers and your mentor and 3/ going deeper into HARDER problems
This is an environment university generally* provides better than Internet resources or bootcamps (especially since "free" Internet resources generally don't go into the deeper, practical territories of ML that you'd find in industries or in the last phases of a postgrad). I just look at my masters students, they shared work and dug into sooo many Python/R libraries and research papers in classroom groups over 2 years, I believe they'll be able to collaborate with teams to push our world for the better :)
But the cool thing today, if you're in a university that doesn't provide this environment for X reasons, with Internet, forums and social media today you can create this environment for yourself: find like-minded people to work with remotely and a mentor to provide constant feedback, on industry-like datasets from Kaggle. Takes time to build (I'd say it took me around 1.5 years of Twitter+Streamlit forums to find all those people, whereas it should be less than a month in Uni) but doable.
So yeah, alone you may learn faster, but together we learn much more things :) and in my opinion Uni is better suited for this, but with a lot of energy you can recreate this on the net today. If you can do both, which is what I see some students do, I believe you can quickly become a very skilled and recognized expert
Good luck on your Data Science journey! Have a nice day
* I say generally because...well sometimes Universities have bad ML teachers and there's nothing we could do about it...

[@michaelomosebi6374]

@@andfanilo I am grateful. I'll give you a shout out in LinkedIn & Twitter

[@jorge1869]

I don't want to imagine the snowflakes CEO's face after paying 800M for streamlit and now seeing the newly released pyscript. 🤣😂🤣🤦‍♂️🤦‍♂️

[@andfanilo]

Ahah we’ll see in 3-5 years :) right now I’m playing with Pyscript inside Streamlit so I do think Streamlit can benefit from it too, like having a server less Streamlit app, all in one unique HTML file 🤭 wait & see

[@jorge1869]

@@andfanilo Indeed, streamlit's solution for fast and easy backend development is unique so far.