I do not understand. Have never understood. "Data Science" is, surly, newspeak. The appropriate term, surly, is "statistics".
A new title means a new opportunity to ask for more money and influence. See also, "devops".
Data Science: Reality Doesn't Meet Expectations
21–30 of 168 posts
Re: Data Science: Reality Doesn't Meet Expectations
#22I do not understand. Have never understood. "Data Science" is, surly, newspeak. The appropriate term, surly, is "statistics".
Re: Data Science: Reality Doesn't Meet Expectations
#23I've seen a few similar articles now. Does this represent the general view of folks working in data science? "Data Science" is such as meaningless catch all term. The reality is in many organizations it's simply advanced business intelligence or advanced business analytics. There are some industries that lend themselves well to this whole practice, and they tend to be industries that have been borne out of the internet age (e.g. social media, internet advertising, etc.)
Some other industries have been doing "data science" for ages. Credit Risk Modelling, insurance and so on.
Every time I read one of these articles, I feel it's just an individual who entered a kind of crummy situation and they're learning what it means to work in a corporate environment. Some are better than others. Some are more motivated than others. Some have better cultures than others. Some are more willing to make technology a key part of their business strategy. Some are more data driven than others.
My recommendation is to always ask the fundamental question before joining: what are you trying to achieve with data science, and is it actually achievable?
Re: Data Science: Reality Doesn't Meet Expectations
#24I do not understand. Have never understood. "Data Science" is, surly, newspeak. The appropriate term, surly, is "statistics".
There are some differences between stats and data science.
At least initially, DS was a lot about machine learning. While those methods may be statistical, it was the computer science field that drove and embraced the ML revolution. Currently, it’s mostly ML engineers who make the impact (deploy) ML and these are mostly CS folks. Statisticians still can’t code themselves out of a box (2013 MS Statistics here from top school)
Also, there has been a lot of innovation in managing data at scale (tools, infra , etc) This, again, has been done by engineers not statisticians. But this is still related to the science of data.
So the difference between the new (data science) and the old (stats) is about culture and about some of the methods for dealing with data at “scale”.
In other words, statistics is just a part of data science, but not the whole.
Re: Data Science: Reality Doesn't Meet Expectations
#25Work reality rarely meets expectations. I am sure a lot of “UX” people also got a little disappointed that reality is much more mundane than the fancy title suggests. Or the people whose last algorithm work was the interview.
Re: Data Science: Reality Doesn't Meet Expectations
#26Teams being small, data being crummy, infra being hard, and yet expectations being high aren't so much complaints as the they are the job description. The point of data scientists and the related roles listed in the article are not to just churn out the fun stuff, but to wade through the institutional and technical muck and mire it takes to bring the fun stuff to bear on a relevant business problem and to communicate…
As somebody in an ML Engineering role, i.e. somebody who could be asked to either fix the logging infrastructure or build some models, I would have agreed with this. But even in this day and age with ML being the new hotness, you will find people who are quite happy to work on infrastructure and don't have a huge amount of interest in training models themselves, and it is probably a lot easier to hire them than peopl…
I wrestle with this too, there's a lot of context to determine what skillset is better.
I suspect, if there are lots of relatively simple ML problems, then a generalist with integration chops will be more effective in getting them out quickly and "good enough". The specialist may take too long on models that are too heavy and impractical.
If there's one big ML problem (Google search, Netflix recommender, Amazon search, etc), where 1% additional makes a difference, then yes, specialist DS/modeler is probably preferred.
Larger, older org/heavier existing infra/more specialized culture will also tilt the scale towards specialists.
Re: Data Science: Reality Doesn't Meet Expectations
#27Disclaimer: I use the term Data Scientist throughout this post; however, popular titles such as Data Analyst, Data Engineers and BI analyst are randomly applied by people who know nothing, and these people share none of the responsibilities of a Data Scientist.
I have never had hopes about the potential impact of being a Data Scientist. I felt every company should be a “data company”, but everything I knew told me that companies are political institutions bounded by the pressures of late stage capitalism. Anyone who things different is dim, anyone who blogs about it is a moron.
My expectations did meet reality.
Where did my expectations come from?
I attended a four year Computer Science degree, followed by four and a half years of earning a Ph.D. I then spent 20 years in industry. 19 of the 20 weeks’ focus were not on machine learning (ML) and artificial intelligence (AI).
I figured I’d spend most of my time buried in code and data, I was right, I had to find shit buried in it, and dig it out with my teeth. Executives hated me because I was a threat, but they needed me so I continued to get paid. I continue to be able to create insight and predictions that almost no one else can, and until this stops I will get a 200k a year salary, benefits and a Tesla.
All of this happened, I can't be bothered to waste my time commenting on this moronic blog post.
Re: Data Science: Reality Doesn't Meet Expectations
#28I wrote a blog post along similar lines in 2018 ( https://minimaxir.com/2018/10/data-science-protips/ ); unfortunately, the industry hasn't changed much since then. As noted in the submission, there's a lot of flexibility in what a "data scientist" is. Normally that's good and healthy for the industry. However, it contradicts a lot of optimistic bootcamps/Medium/YouTube videos, and many won't be prepared for the diff…
My industry (information security) is the same way. Far too broad of a category, leading a lot of people to get confused and frustrated. I see a lot of analysts (defensive security) who thought they were going to be pen testers (offensive security) and didn't realize those jobs were in two completely separate career paths.
Re: Data Science: Reality Doesn't Meet Expectations
#29I've seen a few similar articles now. Does this represent the general view of folks working in data science? "Data Science" is such as meaningless catch all term. The reality is in many organizations it's simply advanced business intelligence or advanced business analytics. There are some industries that lend themselves well to this whole practice, and they tend to be industries that have been borne out of the intern…
100% agree with the article. The top misconceptions are spot on. I’m at a big place where data science hype among leadership couldn’t be bigger.
Re: Data Science: Reality Doesn't Meet Expectations
#30> I attended a 12-week data science bootcamp in mid-2016. ...
Yeah, well there's your problem, my dude. I've been doing what might be described as "data science" since I quit physics in 2004. Aka before the term existed. It's a great area to work in for intelligent people who want to use their brains to impact the real world; vastly better than what people get paid to do in physics. If customers don't know what the tools can do, it's because you as the data scientist have failed to explain it to the customer. If your work product isn't in front of the decision makers, you've also failed: they can tell the bottom line impact and will reward you accordingly. Sometimes there is no data in their data; they should know that up front.
As for whining about poor data quality: n00b. What do you think they're paying you for? Nobody gives a shit what people do in Kaggle competitions.