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Is the analytics career path in tech a dead end? What can you pivot to? #data #dataanalytics #datascience
What do you expect when you ask the question on a platform where the majority (at least that is what I feel like) of people are into Software Engineering? Asking the same question on like Kaggle forum will give you entirely different results.
Except Kaggle does not do DA either. DA people eventually get an MBA and move to business side. All talking, cooking data to serve their purpose.
^I felt personally attacked by that. It described me to a T
Yes yes and yes. Low ceiling, you deal with shitty PM’s and you won’t ever get the ownership you need to grow.
You are at a wrong place.
What's a good pivot?
An analytics background is a good place to be if you ever want to pivot into a leadership position. MBAs can be a dime a dozen, but understanding both data and the business nuances behind decisions in data is a primo transferable skill.
If only things worked that way.
Yes, I moved from analytics to a CSA role. Next target is to move to a PM role in couple of years.
Yes, Microsoft's point is the most relevant on this thread. Data analysts can go into product management easily.
You could technically pivot anywhere. Product , engineering, Leadership or go deeper into data science or ML/AI. I’d say keep growing your skills both leadership and product as well as DS skills or whatever you are interested there’s low ceiling if you don’t grow your skills or take initiative then low ceiling
is this a serious question lol
Modern analytics sets a person up for PM or even GM since it’s all about business decision making. Excellent career path and limitless growth. Much more upside than a back office SWE.
Then why not just be a PM in the first place. So analytics itself is not a good long term move then
Because lackluster PMs with zero hands-on technical background are a dime a dozen
Evergreen field, easy to pivot anywhere I.e product management, leadership roles, Data engineering, MLE ( software engineering side), data Scientist
Low barrier to entry. Accountability limited to pulling data, perhaps maintaining the environment, and deriving insights. The relationship with your customers and consumers of your output largely dictates potential for impact.
Wrong here! In fact getting into SWE much easier nowadays because of demand and supply. Data related positions are limited ( 1 position even might get upwards of 1500 applications ) and competition is very stiff to get into it. List of things you gotta know is very wide which makes it more difficult. Unlike SWE where you need to know only Data Structures and algorithms and System Design
You just made Amazon's point for him. The reason there are 1500 applications to one position is because the entry level market is saturated, which is because the barrier to entry (in the sense of having basic qualifications, not a job) is low. It's the new business major
Same here; I'm in a postdoc data science course designed for people with non-CS PhDs who want to be data analysts. I'm looking forward to possibly becoming a data scientist some day. The money is a _lot_ better than generic office work.
@tooQ10 is it Insights?