Data scientists who use Mac instead of Linux, what other bad decisions have you made in life?

I don’t get it, some people who works only in excel and have no knowledge of mathematics behind ML AI models. Also have no background in coding are calling themselves Data scientists. Is this really a justice with the role of data scientist??

I spoke to an experienced guy in the IT sector who has worked in many FinTech firms and consulting companies. He said when the data science hype started several years back, companies started getting funds from venture capitalists to set up data science wings . Companies went on a hiring spree with this money to hire data science professionals. However, as the years passed, these data science departments failed to generate revenue as expected. Also, some aspects of data science will very likely be automated. So companies will probably hire less data science professionals and may even fire the resources hired earlier. source: https://www.reddit.com/r/developersIndia/comments/zzji46/is_data_science_a_bubble_about_to_burst

AMA. How does a day to day pan out for a data scientist, Who is data science not really for, Does FOMO really affect my career in DS

I work for a growing startup in Bangalore. The data team size is very small with no data lead. Not a single query we hit the DB gives any result. Owing to cost cutting my firm has cut down the instance size and they're planning to downgrade the DB too. We data analysts are relying on the data engineering team to fetch us results for our queries which takes a long time to get a single table. This is highly inefficient. The data size is growing at a massive scale however the analytics efforts are trickling down due to poor infrastructure. Is this happening in your data team too? How can I solve this problem given there are no experienced data leaders in our firm to voice out.

Talking to my nephew nowadays seems like the AI bug has gotten to him. Even going back during the early parts of my own career and even undergrad, ML and stats was still a fledgling field. Most of the smart folks went into SDE roles at top tech. I still remember that even back then, hiring for roles like these were extremely theoretical. You would test even experienced candidates with undergrad topics which was slightly outdated tbh because most of the functions were abstracted away and it is obvious that you would not remember the matrix approach to finding weights for a simple linear regression model. Has this changed now? Because I don't think interviews like that actually evaluate the accumulated wisdom that experienced professionals bring to the table. I mean it is generally a waste of time for anyone to brush up manual derivations or remember SQL niche intricacies. Usually it is better to test for the 80% knowledge that is actually useful on a day to day basis. I mean if the main things that companies test in experienced candidates is something that can be rote-memorized from a book then they are not testing the right things no? What do you all think? Just wondering about this for some time now especially considering the hype around AI?

Why majority SDE , Data engineers , tech guys prefer mac over windows List your tech reason in detail please , in comment Planning to switch to mac from windows

Hi, I am a software engineer with 12 years of experience. I need some suggestions. 1: Is it too late now to learn Data Science and pursue a career in it? 2: If I want to learn the necessary concepts of ML what path I should follow? 3: I find the maths behind the ML models a bit confusing can anyone please suggest any good resource for that?

Skillset : SQL, PowerBI, Python, Advance Excel, ML, Very basic Azure (Just started learning) 1.5 yrs of experience in TCS in PLSQL DB Team I am confused as to which field should I venture into, Big data or data engineering or cloud architect? I like coding but not way too much. (7 on a scale of 1 -10) I love communicating and organising/managing stuff(9 on a scale of 10) I love working with data but I dont want to be a generalist/data analyst. I would like to make good money