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Selected in a Big4 firm and one MNC shortlisting is going on, hopefully will be selected within 7-8 days.The MNC will be offering good package,but Big4 is just giving 20% hike in my salary and waiting for my decision to say yes or no,to proceed further with offer letter. Query :1. If I say yes to the Big4, and when they issue the offer letter,and then I reject, can they blacklist me. 2. Also if accept the offer and say no, in that case can they blacklist me from their firm.EY Deloitte KPMG PwC
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Analyst yes, and some DS positions don’t need any true DS work so you can get in and out the title on your resume.
It really depends what you want to do. General data work, dashboards, ETL, or other analysis? That’s data analyst.
Machine learning models and other predictive or advanced analytics is much more difficult and relies on a solid foundation in statistics as well as rules on how ML models should be built and what to account for. Still attainable, but you would have to work pretty hard to get that foundation.
Seconding this, I wanted to be a scientist but wasn’t cut out, become a data analytics management consultant and pays very well
I did econ/finance undergrad and an econometrics heavy MSBA. Landed as a DS on an advanced analytics team doing modeling in a marketing org in big tech. Most classmates became analysts at different places, a handful of smarter folks became DS's.
To highlight that outliers are always possible, one person who wasn't exactly pulling stars from the sky and got semi-caught cheating, somehow made his way into MS and then Google. Turns out he's just resourceful, who knew lol. And one person who wasn't at all mathy but had PE in his sights all along got into that. Probably outearns all of us now 🤷♂️
Some caveats, all this was some years ago. In addition to new grad DS becoming even more impacted, a LOT has changed in the last year with LLMs and tech layoffs. These days, tbh idk what to recommend for aspiring ML folks degree-wise. Mostly because idk what the job will look like in a few years. Maybe a stats heavy CS degree will still be optimal, maybe it'll be PhD or bust, or maybe MBAs will run the show and tech skills will only matter for researchers. Still probably not a bootcamp though.
Thanks for the insight!
I'm aware that every country / uni is different.
I'm my experience (DS at a consulting firm atm), the maths / stats you generally see at an Econ / Finance degree is more than enough to get you around 90% of the ML concepts out there (by this I mean Multivariate Calculus, algebra and linear algebra and probability and statistics, including statistical tests + some time series, etc) -> where I come from this is part of the coursework for the majority of degrees, unless you major in something like history or literature. It also sets you up to learn more advanced concepts (numerical methods for example) for particular things that would generally appear in some ML algorithms.
What I do think is very important and not generally taught in all degrees is programming and systems experience. Today training an ML model is extremely easy (assuming you are also comfortable with concepts such as data leakage, data drift and generally knowing what you are doing). The DS position (excluding research) IMO no longer adds value by training models and shifting parameters, rather by delivering production services. In my company we're starting to expand the team and other than general DS, we will be looking for some strong foundations on programming, docker, databases, systems and data engineering in general. It is likely that in DA positions you could be doing some ML without really focusing on these bits.