@tarekelashmawy1072
Mistakes 1) starting with coding 2) becoming a data analyst before data scientist 3) believe that data scientist as a job title is the only available role in the field 4) moving backward without a roadmap 5) searching for jobs before practicing 6) avoiding generative AI 7) moving beyond regression 8) Math is important but not really 9) falling into tutorial trap (watching courses without applying on projects and practicing)
@sunilrskanda1588
For Data Science enthusiasts, this is one of the best reality check videos to ensure you take care of yourself while learning and becoming a Data Scientist.
@ola...o1009
You helped me get over my fear of coding. The way you speak enthusiastically about it 😅
@jahanzebali589
THANK YOU SO MUCH FOR AMAZING GUIDANCE! I REALLY ADMIRE YOUR WAY OF EXPLAINING WHEN YOU REPEAT THINGS FOR EMPHASIS AND SUMMARISING, THAT'S A VERY PRO MOVE! THANKS A LOT!
@bbmpiano
1. data science = statistics + macchine learning, coding is just a tool 2. you can learn coding through data science 3. job roles: MACHINE LEARNING ENGINEER AI ENGINEER DATA ANALYST DATA ENGINEER AI Product Manager 4.3 areas of data science: CODING BEHAVIORAL QUESTIONS STATISTICS AND MACHINE LEARNING FUNDAMENTALS 5. work from the back: find the dream job first, then stalk linkedin, build projects
@TigerCodingSchool123
Maths - Statics & probability Machine learning Programming
@athirab6183
Being a data scientist myself, I can see the traditional role evolving the last year. And what I say depends a lot on the company you work for too. You need to have in depth knowledge in ML and maths (and it’s not just statistics, which is still top priority, but also linear algebra, a bit of geometry etc) and unfortunately you still need to do a lot of coding. Being a good programmer gives you super power to learn and implement anything old and new in ML with greater ease. Now, if you’re into a research role, you might just need to code experiments. And things might be different in a big company vs a start up. But I think the trend right now is having in depth knowledge in ML/maths along with really good coding skills.
@gilbertobatres-estrada5119
Thanks for sharing. I have a strong background in applied math, statistics and theoretical physics so that I had with me from start and built with lots of machince learning and deep learning. For that reason I might be biased towards learning to code. But I think that maybe you might need to know both skills at the start and build in parallel. For instance if you know computer science it might help you to desing algorithms when it comes to computing space and time. One nice thing with coding is that you can test machine learning algorithms from start and see the messiness of working with data. Tank you!
@azipage
I can honestly say, there is no lie in this video. You literally shared all the traps that I was going to fall into (if I didn't watch this video). Thank you so much for warning us, the emerging data scientists. Biggest Takeaway from you - Take Action instead of watching others become data scientists!
@astatine0085
yes, it's so easy to lose the skillsets. which is why employers prefer employed folks.
@Neshneneh
I started with coding just for the reason you mentioned: it was completely new to me
@ArJunisAFK
make a video on different career paths in data science. Like you mentioned ai product manager and such, what others are there? what do these jobs do & and what skills are required for those roles.
@jamesrom6222
Have been a data analyst for 5+ years and always had aspirations to transition to data science but was always intimidated by the roadmap as it pertains to coding since I have minimal knowledge. I think I’m ready to take the step and this video is my launching point 🙏🏼🤞🏼
@ericfiumano4694
There's lot of theory...and when I say a lot I mean A LOT. In my country we're very used to study a lot of theory in any university field (and the issue is that it is usually too little practice) but in the case of D.Science it's very useful. Too many people on YT treat D.Science just like it's all about running some ML algorithms using R or Python... As a car mechanic knows how to use many tools, knowing many tools doesn't make you a mechanic 😉 The "Tutorial" chapter made me thing that in any subfield of D.Science there's smooth activities and difficult ones: tutorial are usually about "how to" concerning coding....but, you know what's one of the most time-wasting activity you have to be used to while coding (for anything) debugging, debugging, debugging and debugging. As designing algorithms (of any kind) can be challenging, you don't how much time you could waste if you lack the right mindset and calm to be used to "apparently ununderstandable" reasons that stop your projects.
@jvc776
I am diving in learning data science because i want to be an academic in the genetics field. I initially intended to do only wet work in a lab but the more i delve into data science the more i want to have this knowledge so i have more plasticity in my career. Unfortunately im coming from a 100% biology/chemistry background and zero informatics so its being tough, i hope i can be decent at it after a few years.
@758-s7j
This video is great and practical. You're a real data scientist who has been working at tech companies for many years. I'm glad you share the real experience. Keep up the great work! Thanks!
@allukaveri9138
You're a real data scientist who has been working at tech companies. Keep up the great work! Thanks!
@rjkeaveney4833
Thank you for this realistic and meaningful video! I’m trying to decide between a data science masters and a data analytics masters. A lot of videos are overproduced, lack substance, and performative (trying to be a comedy skit for no reason) and don’t really provide a discerning view. Really appreciate your advice here!
@_the_ordinary_
That was so helpful, thank you! 👍 I am near the end of a DS post grad course right now and I really enjoy it and I get what you are saying Re coding, and re tutorials, and doing your own projects etc It takes time to build confidence, and the best tutorials are those where you can do things yourself in parallel 👍😉
@SundasKhalid