Sorry, the statement I made wasn't intended to be connected that way. Data science uses a bunch of math beyond trig. I meant that in general math beyond trig becomes much less useful. I was talking about the general usefulness of different levels/types of taught math for white collar jobs/living. Not what is of use for data science.
Disagree, at least in this instance. Math isn't a bad thing to learn but it's not helpful for data engineering. Data engineering is focused on data integration (data pipelines), not on any type of analysis or data science.
Data Science and Machine Learning are buzzwords with little jobs behind them.
There's work to do around data (databases, logs, analytics, marketing, business intelligence, etc...) but none of it has to do with stats/maths. The realization is quite hurtful for those who wished to pursue data science.
The only field that recruits maths folks is finance, specifically quants, who are doing financial analysis all day. It's only a few roles in a few places and highly selective.
It is not my impression that Data Science mixes programming and maths. Unless in a limited field of finance where all data and analysis are maths heavy.
Your comment was downvoted presumably because it is flip, but I think there's a kernel of truth here. The blog post correctly highlights that limited math is needed to "do" data science, but this is why we have so many irresponsible data scientists.
I'm currently teaching a data science class intended for mid-career professionals. It requires no college level math. And virtually every topic, I feel like I am doing them an injustice by not being able to delve into the mathematics behind what we're doing. They'll be able to put on their CVs that they "do data science", but I don't trust that my instruction gets them to the level that they really understand much about it.
The author clearly has a bias (I'm a data scientist so respect me and pay me a lot). He's then gone on to describe some standard programming and maths skills that a huge number of people have (taught to engineers/scientists/programmers). I'm going to get downvotes and be labeled troll but I just have to plainly disagree. Data science isn't some magic new career field, it's simply the application of standard scientific tools to tables of numbers. As netflix and kaggle competitions have clearly demonstrated, literally anyone from anywhere has a shot at be the best on any particular spreadsheet of numbers (that what it boils down to, (possibly large) spreadsheet of numbers).
> A few of my friends work on Data science and they said that maths isn't that important
Without the math you won't understand anything in ESL.
Which might be okay if the job doesn't require you to go into that much depth - some data science jobs are more focused on research (very math-heavy), some on ETL and/or engineering, others on business understanding and communication; it's a really broad title.
I concur. The OP says data science doesn't sound like feel like doing math. On the other hand, job postings for "data scientists" where I live don't sound like they have much to do with programming. They have long lists of math and other theoretical requirements, and throw in "oh, and by the way it would be nice if you had basic Python skills".
I would argue a data science degree is less a Computer Science degree and more Mathematics degree. Programming is simply a tool. The real meat of it is the maths.
Right, so defining data science as 90% sklearn+DL+numpy is just as silly as saying that it's 90% table manipulation. That's exactly my point.
Still, if anyone here has managed to find a data science job in which tabular data management is not a sizable piece of what you do, I'd like to know some details!
I think that's a fair point, but the issue is that most people stop with the application and don't pick up on the underlying math. So, we get articles[1] discussing the lack of data science talent that point to a lack of math as the reason.
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