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5 online courses by Harvard University, from introductory to Intermediate, free to access. ๐๐ง๐ญ๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง ๐ญ๐จ ๐๐ซ๐ญ๐ข๐๐ข๐๐ข๐๐ฅ ๐๐ง๐ญ๐๐ฅ๐ฅ๐ข๐ ๐๐ง๐๐ ๐ฐ๐ข๐ญ๐ก ๐๐ฒ๐ญ๐ก๐จ๐ง https://lnkd.in/gygaeAcY ๐๐๐ญ๐ ๐๐๐ข๐๐ง๐๐: ๐๐๐๐ก๐ข๐ง๐ ๐๐๐๐ซ๐ง๐ข๐ง๐ https://lnkd.in/gUNVYgGB ๐๐ข๐ ๐ก-๐๐ข๐ฆ๐๐ง๐ฌ๐ข๐จ๐ง๐๐ฅ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ข๐ฌ https://lnkd.in/gv9RV9Zc ๐๐จ๐ฆ๐ฉ๐ฎ๐ญ๐๐ซ ๐๐๐ข๐๐ง๐๐ ๐๐จ๐ซ ๐๐ฎ๐ฌ๐ข๐ง๐๐ฌ๐ฌ ๐๐ซ๐จ๐๐๐ฌ๐ฌ๐ข๐จ๐ง๐๐ฅ๐ฌ https://lnkd.in/g8gQ6N-H ๐๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐๐ฌ ๐๐ง๐ ๐ https://lnkd.in/gUY3jd8v 2 more on Coursera, you can choose 'Auditย the course' on Coursera, which is free for all the learning videos and materials. ๐๐๐๐ - ๐๐ง๐ญ๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง ๐ญ๐จ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ข๐ฌ ๐ฐ๐ข๐ญ๐ก ๐๐ฑ๐๐๐ฅ https://lnkd.in/gBVHWzvR ๐๐จ๐ก๐ง๐ฌ ๐๐จ๐ฉ๐ค๐ข๐ง๐ฌ - ๐๐๐ฏ๐๐ง๐๐๐ ๐๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐๐ฌ ๐๐จ๐ซ ๐๐๐ญ๐ ๐๐๐ข๐๐ง๐๐Specialization: https://lnkd.in/g_n-3Wn5 Thanks for the nice visual, Kevin Rosamont Prombo _____________________ I share my learning journey into AI and Data Science here, join me and let's grow together. For more learning materials, please check my previous posts. Alex Wang #machinelearning #datascience #dataanalysts
Very nice graph. Thanks, but a very basic question. What each level means ? Which are the legends for each of these charts? Sorry to be a nitpicker, but i believe its worthy to be clear when presenting any kind of information.
While interesting to think about, I am not sure how much it makes sense attaching yourself to a particular role name really helps you define value for people you (want to) work with. I just tell people "I help people take better decisions using data" instead of saying "I'm in MLOps" and generally speaking, discussions are more fruitful when introduced that way. Also: funny that you post this type of visualisation. I started making a similar "spider" diagram of my personal skills 4-5 years ago, I attached the results here (it's not up to date)
I have never seen a DE who does not have a hand as Data Analyst and hence this Image does not reflect reality at all for a DE.
The lines between them are gradually getting blurred with access to technologies that enable seamless data operations for data professionals in their niche.
Thank you very much for sharing this list. When trying to learn something new in such a vast field, it's always nice to have an idea of where to start!
I'm an Everything-DATA+AI Analyst Steward Practitioner Developer Scientist, but here in Canada using that word starting with "Eng" is a huge "not allowed" because I'm not registered with the engineering institute.
Coding Data Analyst | R | SQL | Stats | Audits
3moNone of them to be honest. And I am sure I am not alone. The problem with these kinds of segmentations is that they are largely artificial. They might be applicable to the largest of organizations with a very high level of maturity but thatโs about it. In reality there is a huge amount of overlap mostly due to the fact it is simply not feasable to hire someone for each role. Or not even practical for that matter. This especially goes for data science vs data analytics. They actually complement each other very well in real life use cases. This is because many of the DA work leads to all kinds of new questions which require statistics, algorithms, scripting, etc to answer. Being able to do this yourself gives one of the most important advantages one can have: SPEED. Managers often require answers fast but also solid because proper decision making depends on it. And this is where you can deliver if you do not limit yourself to the โstandard DA roleโ. (please do not take offence) There is a trade-off though and that is in-depth knowledge which you will (probably) lack in the end. Broading yourself means you cannot go as deep as someone who is an absolute expert in field X.