I know what data science is but how would you explain it , how do you feel about it?
I’m sorry if this post is breaking any rules on this subreddit.
I just wanted to know how each one of you Data Scientists would define this field.
Just a brief description of my background. I'm an education counselor in an Asian country where Data Science program is starting to trend. My job includes to explain to students what kind of degree would achieve their desired job or help them to choose the right career path.
Very often, I am able to explain what Data Scientist do (thanks to this subreddit and eli5) but I struggle to provide examples to high school student how is data science applied and shape our daily lives .
I've tried searching this subreddit but I am getting very technical answers which I struggle to understand. So to put it simple, how do you apply what you do in your job into our lives.
I have found Data science is a very broad term used to describe almost anything related to data, but if I was to explain it to my daughter I would be saying.
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There are criminals out there that get money from doing illegal things, (selling drugs etc), those people need to put their money in a bank to use it to buy things. Data science can be used by banks to find weird transaction behaviour and essentially those criminals.
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If you think about streaming services like Netflix, how do they know what show you would like? They basically find groups of ppl who have similar viewing patterns as you, and show you shows that your peers liked
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The last one I as this is likely just on the boarder of data science and comp sci, there are programs out there that can find out how much of what you have written has been plagiarised.
Senior dev here, about 10 years experience. There's a lot of focus on ML in the comments so far so I'll take a slightly different approach.
Every office job I've ever had has involved data to some degree, be it in Excel, a database, whatever. Likewise, all of those offices have had rooms filled with non-technical employees doing manual, menial tasks to squeeze value out of said data. The level of "true" understanding of the data and how it adds value to your organization that these employees achieve is very often limited, and to be honest their career trajectory is often determined by working harder instead of smarter for their lack of a technical background.
To me, the core function of a data scientist is to understand data on a much deeper level and create hypotheses on how to extract greater value from it using a variety of techniques. That sounds broad - and it's because it is. At the end of the day we're problem solvers, regardless of the problem (as long as it involves data, of course). The key difference is that our solutions scale to produce results that would literally take buildings full of non-technical people.
Examples:
Any given financial company has skyscrapers full of analysts doing due diligence on companies, looking for indicators of the public's sentiment about that company, applying basic financial analysis metrics, etc. One data scientist could train a neural network to pull thousands of news articles and understand public sentiment far better than those analysts ever could, or apply a variety of algorithms (that go way beyond their non-technical counterparts) to achieve better financial analysis on literally every company that the financial group covers. This is why Wall St has been hiring data scientists and engineers like crazy for the last few years.
Likewise, any logistics company will have teams of people trying to route shipments efficiently, make sure things arrive where they should be in a timely manner, make sure any necessary paperwork (customs) is submitted, etc. To a data scientist, this is just an application of the "traveling salesman" graph problem along with some basic automation, and then maybe some prediction of what will be needed where in the future. This played a huge part in Amazon's rise and is how they're able to provide 2-day shipping for so many products.
I’m really curious to understand the day-to-day life of a data scientist. They work with data, but what does that actually look like in practice? Specifically, I’m wondering how much of their work is focused on AI technologies.
Do data scientists work directly with advanced fields like AI, computer vision, natural language processing (NLP), and neural networks? For example, if I want to learn more about these areas, should I pursue a career as a machine learning engineer or is there room for that within the data scientist role as well?
In general: is it a great role to gain AI expertise to maybe found a startup one day or not so much?
I’m wondering what data science means to various people in this community? We argue about/discuss it a lot at my work, where we do “data science capacity-building”. Seems like everyone has a different take, so super curious to see the variety for definitions out there!
How businesses convinced PhDs to do analytics
Data science is a huge scam to get really smart people to work for dumbasses who paid their way to an MBA.
Notes:
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See "How to Climb the Corporate Ladder 101: Hire High IQ individuals to make you look good and then take credit for the success of their work"
I just started a data science master degree and as it's a field that hasn't been around for long I noticed that I struggle to make people understand what it is about. How do you go about explaining data science to other people?
Edit: this was more helpful than what I expected, some answers are quite straight to the point and useful and others were funny af, you guys never let me down
What are the languages I need to be specialized in to become one? Which topics should I cover? What's the situation of the job market for junior data scientist? Sorry for asking many questions.
I have 5 years of experience in this field, I've studied a lot of fancy stuff such as self organizing maps, boltzmann machines, tSNE, bayesian hyperparameter tuning, and a plethora of those cool paraphernalia. But in the most of cases the stakeholders only need some simple bar charts and line plots, some comparatives, some quantiles. And modelling a random forest or logistic regression do a preety good job in general for tabular data when there is predictive variables.
Don't get me wrong, I love those complicated models, and tried to apply in real life, sometimes with sucess and sometimes not, but in majority of cases is overkill.
I don't know if I'm working in late companies, and if in a modern startup a data scientist need to put a deep learning model coded in scala every week. Or if really there is a lot of fetishism in data science, and those cool stuff is rarely applied.
Been a handful of posts with this recently, hell for the last few years. And in each one it seems like there's some frustration about the role. But more than that, there's an important question lingering behind the scenes that only occasionally gets asked.
What the hell even is a data scientist?
Is it a programmer with some math and stats skills? Is it a statistician with some programming skills? Is it someone who knows SQL and Hadoop and took an intro stats class? Is it a guy who knows how to use the slicer tool in Excel and can pivot?
Honestly this is my biggest beef with the term. Wtf does it even mean? I would be very curious to hear people's views on what it actually is.
I'm soon done with my bachelor's in Software Engineering and considering working as a data scientist or getting a master's degree as a data scientist.
My questions:
What do you like about being a data scientist?
What don't you like about being a data scientist?
Does it ever feel like a grind/work?
Did you have another passion you regret not following?
After 20+ years in the field, I'm not sure what I should call myself 🙂
What does data science do that computer science or statistics already doesn't do?
Please explain it to me.
I know they code and use statistical models on huge amounts of data to come to conclusions or train the models even more but what do they ACTUALLY do? What do they spend most of their time doing? What do they rarely do?
I'm thinking of getting a master's at DS but I'm overwhelmed by the amount of tasks "data science" includes and I'm trying to figure out what the day-to-day of a data scientist looks like and how would that interest me.
I’m thinking in a corporate and/or corporate marketing context. It seems like analytics is more interpreting the data and data science is actually building out the databases. Is that correct? Analytics is more sales and marketing and data science is more engineering?
But then I see some “analytics” positions seem to require both data interpretation and coding languages and the like.
Are these skills equal in value but different? Or is data science more valuable than the other? Is the person who contains both skill sets rare?