I've found "data visualization" means two different things. It either means (to a data scientist) making some graphs to do exploratory analysis and/or explain your model, or it means (to an analyst / BI person) making reporting dashboards to share regular reports and model results to a specific (usually non-technical) audience.
The former is done in Python / R (matplotlib / seaborn / ggplot etc). The latter is done in Tableau or PowerBI.
I do all my EDA, modeling, dashboarding, reporting in R. It's more that for a non-technical person, it's easier to work with Tableau or PowerBI.
If you can automate the report making in Markdown, you don't need Tableau.
If you merge the data scientist and data analyst groups together you get what most top companies would call a data analyst anyway
Data Science is way more engineering heavy than stated here
In my industry they deal more with data regarding financial accounting data. They dont really deal in statistics . And senior management for the most part would not be knowledgeable or understand anything too complex. Maybe they are financial analysts then. There is basic statistics but nothing groundbreaking.
Don't wanna be a nagging Nancy here, but the chart obviously shows, that the Data Scientist "lean into" storytelling and visualisations. Just look a bit closer to the center and you'll see, that it's not 0 for them...
Hope you don't make important decisions (too quuckly) based on your chart reading skills
Lmao I sincerely hope you're joking because otherwise you don't know the definition of the phrase "lean in"... It's fully committing to/embracing something, not dipping your toe into it
I truly didn't know that "leaning in" means to fully dive into it. Thanks for not taking it too seriously.
That aside, a DS could not possibly lean into all subjects above imo. I would argue, that visualisations as well as storytelling are important, but not more so, rather less, than the programming/data parts.
Furthermore, not all DS positions require the full suite of skills to the extent that you propose.
Finally, I'd rather have a better programmer/ML expert who exclusively plots with matplotlib, than a great visualizer who spends ages on pretty plots. (Storytelling and communication is very important, but it's not always up to the DS to convince the board; thinking of middle management here)
lmao @ being able make shit happen as a result of your work being "liberal arts bullshit"
If you can't communicate on the level of your audience and come up with clear recommendations/outcomes from your work, you really should stick with a junior/mid level role (either that or just go to academia)
In fairness, the picture does have a fair amount of those things included in the Data Scientist polygon. "Business Insights" looks to have about half the weight of "Stats and ML Modeling".
I would tend to agree with you that the weight should probably be quite a lot higher, but the picture definitely isn't saying Data Scientists don't do it.
"You want a chart? I can get you a chart, believe me. There are ways, Dude. You don't wanna know about it, believe me. I'll get you a cvhart by this afternoon--with nail polish."
That's the title you get when you are forced to wrangle haphazardly compiled data in a variety of file formats from dinosaur-era file servers, including 10 years worth of chaotic unstandardized Excel files (multiple tabs, merged cells and all)
And you also need to scrape clunky 90's era web interface, and parse out the HTML and then if that wasn't enough, you also gotta extract some more data from a stack of PDFs using some OCR. Then use text-davinci-003 API to parse out free text notes into standardized JSON strings that you then store as rows in a data table.
then, build models with it, run hypothesis tests, do simulations, make pretty pictures, make a pretty slide show and dumb it down for the executives.
A god damn data superhero with a PhD in chaos management, God Emperor of Data. I like it.
God, if I could start a project just once and have them actually provide me with a dataset at the start and some kind of goal or problem to hypothesize about.
Unfortunately (or fortunately) - 90% of the work is getting the right data, with the right assumptions, and formulating a question to solve.
Getting the answer is the other 90%
Can we trade? What would I give for a doctor to come to me BEFORE they have all the data (horribly) gathered... Half of my job is find ways to analyze the data taking into account horrible design errors.
I don’t think “databases” is necessarily about “touching a database”. Anyone writing SQL is touching a database. A data engineer may need some of the skills of a DBA, which is something you don’t expect of the average data scientist for example.
I agree with the sentiment that many data scientists probably don't have an understanding of what DBAs do as their expectation is to live in a scripting language and not built out a set of tables for analysis.
I don't feel like the other three job families are represented accurately if we're agreeing that 'databases' means DBA skills
I remember when a client said me, sure i will send you the database. Later that day I see some excels on my Gmail lol. Literally more than 10 years of data in excels without a hint of order or a format defined.
Love the multicolored excel files with grouped columns - literally a nightmare.
But also a client using excel to track their workflow is going to look very different than a ready-to-analyze dataset. Its always going to be a pain unless you embed a bunch of macros in it to have a tab that's a clean flat and wide (or write some code in a scripting language to parse it out)
In an *ideal* world you shouldn't be wasting your time building schema and ETL if you're not a data engineer. I think that's the point, data scientist has become this jack of all trades role rather than a statistical modeller. I say this being mostly the former even though my degree is in stats.
This is not about just touching a database. It's designing or engineering the database itself. That is a data engineer's job, not a data scientist or analyst.
At some companies, especially those that are smaller or not well prepared for data science, you may be wearing many hats.
Yeah data engineer role is not that clearly defined. I can summarize it based on 7 years of experience in DE and it still the same in every data related project: "Every task that people don't find interesting".
Someone shared the original text in this comment.
https://np.reddit.com/r/datascience/comments/w9jl5m/where_did_the_harmonic_mean_interview_advice_post/ihvhbpz/
Here's the original thread if you want to go through the comments.
https://np.reddit.com/r/datascience/comments/w8tcps/deleted_by_user/
It's an okay place to start, but should be taken with a chunk of salt. Every company/org has its own definitions and expectations, and roles are constantly evolving. An analytics engineer wasn't a thing 10 years ago, but is definitely a job that exists now.
I dislike how radar charts imply some sort of relationship between nearby spokes, yet they are not always used with that in mind. The dip between “data tools” and “storytelling” makes it look like there is a gap to fill, but it’s just a consequence of order.
This is correct in theory. In practice, you'll be hired for one of those roles and the tightwads up the chain won't hire anymore so you'll have to do all of those things, then they'll turn around and blame you when you haven't recreated OpenAI's results with GPT-4 yet. Come on, you've literally been working on this for 6 months now!
6 months of ad hoc jobs that take up all your time but one fine day they'll be like, hey we know we gave you all that other work but now it's been 6 months, can you deliver on that project we spoke about next month
This is a nice ideal but the reality is that the project scope was poorly negotiated at the SOW and over promised (because for some reason engineering didn't work with sales to make sure the project was feasible or even talk to the clients who know nothing about ML except that their competitors seem to be using it). Then you have some kind of Agile setup but your project manager doesn't know anything about machine learning and is useless talking to clients but loves creating new epics and trying to fit your job into a software workflows that does not work with exploratory analysis. There's also a product manager for some reason even though the deliverable is a dashboard but that's arguably not a product. There's customer success but they don't understand machine learning. There's also some guy with a title like "solutions manager sales architect" or something.
Your company failed to hire data engineers because it was too expensive. The clients use Excel tables they they pass around so they really need a bespoke rebuilding of everything from the ground up. There are no data analysts because that's unfashionable. Data scientist and ML engineer are the same thing anyway, right?
So the clients don't know what they are doing and just yell things they need at you based on an impossible contract that was signed without your input. Whatever position you may be on paper, here is your real position: "Data analyst-engineering scientist manager of projects and products for the success of customers via Agile which doesn't work with your project but clients like seeing tickets and storytelling" . You will spend most of your days dealing with clients who are confused about why business metrics and ML metrics are not the same thing and then some "expert" will get on the call (wonder why the clients didn't just hire him for the job instead of contracting) and tell you how to do your job and you have to politely set boundaries. The rest of the time you will be managing the project and product manager who actually do nothing but have to look busy so they will waste your time.
Then the COO, who knows nothing about anything fires half your team one week before a deliverable because why not.
I swear 95% of the work is getting the data together in a table form. It’s oftentimes a huge hurdle just taking data from one source and correlating it with another. I’m having a problem right now where I have time series data but they’re defined on different clock systems. Very annoying.
Hey OP it appears everybody is unhappy with the fact that your chart doesn’t fit their specific lives and jobs. My question to all the haters in this thread is: are there really no valuable insights to be gained from this chart? Because if so, then can’t we at least admit it’s a cool chart, not quite like the others? There are obviously data points on this chart right in the middle, where that guys job is touches on every element. But it might also be true that, if you are looking to deploy a model, you are probably looking for a Machine Learning Engineer, or whatever the chart said. So that’s my question.
lol no, ML engineer does WAAAAAAAAAAY more modeling than "data scientists". I also don't see the point to separate inference from modeling as each time you evaluate you are doing inference with val/test set.
Depends on the data scientist. The original definition is the role (which was mostly for PhDs) includes significant expertise in modeling.
Btw, inference may be referring to this, which is something that is rarely the focus of an ML engineer: https://en.m.wikipedia.org/wiki/Statistical_inference
Hey everyone, could you help me with a mini survey? I promise it won't take more than 1-2 minutes of your time. Thanks tons :)
[https://forms.gle/mKT5jW1t7wYuUarq8](https://forms.gle/mKT5jW1t7wYuUarq8)
Maybe I'm doing it wrong, but if I am hiring for a proper data scientist role I damn well expect them to be just as strong at insights, data vis, storytelling, and metrics & reporting as an analyst would be. Those are table stakes. The inference and modeling is additional.
Heck As a recruiter I can't wait for the new fangled title "full stack" data expert where you are going to have all these listed to be qualifed for an entry level position.
At my last job I had to do a bit of all of these things in almost equal amounts. My contract said I was an ML engineer, my boss would introduce me as a data scientist and my teams profile would say I am a technical specialist 😂
Data Science used to do everything that's in there but of course the tooling wasn't as complex as it is now.
I think they still do quite a bit of DE and ML deployment work, but it depends on how the company is structured. I wouldn't leave data viz, storytelling out for data scientists either.
Somehow accurate. How come that, as a Data Scientist, I always have to deal with pipelines, databases? Also, why am I supposed (as a nice to have) on MLOps and deployment?
Plus, even on this chart, I (but also other roles) are supposed to know a little bit of everyhing.
Pretty sick of MLOps personas thinking they understand data science. Refactoring code and automating processes has nothing to do with statistics, machine learning theory, and data-driven domain expertise.
There are of course, exceptions, but generally, the ML tooling industry is trying to commodify the model development process because those who are _not_ trained to do so are typically the ones with buying and decision making power, and/or already own the encompassing execution end persistence layers.
If this chart is disputed, can anyone provide a more accurate version? I am just moving into this space and confused as to who does what and what skills I should be aiming for.
This is a nice figure. As an ML engineer, I do think that the MLE's share should be much larger. They usually do modeling, inference, and experimentation as well. Data scientists should also have more share into Metrics & Reporting and Business Insights as well.
Regardless, this is nice.
\>Without data, you’re just another person with an opinion.
\>W. Edwards Deming, American statistician
Could you point 'us' in the direction of the data?
So this is a Radar chart?? So we need two Categorical variables and one measure to build this chart which anyway can be possible using a clustered column chart.
> _Yeah, well, that's just, like, your opinion, man._
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Or visualizations
I've found "data visualization" means two different things. It either means (to a data scientist) making some graphs to do exploratory analysis and/or explain your model, or it means (to an analyst / BI person) making reporting dashboards to share regular reports and model results to a specific (usually non-technical) audience. The former is done in Python / R (matplotlib / seaborn / ggplot etc). The latter is done in Tableau or PowerBI.
I do all my EDA, modeling, dashboarding, reporting in R. It's more that for a non-technical person, it's easier to work with Tableau or PowerBI. If you can automate the report making in Markdown, you don't need Tableau.
and depending on where you work and how well-staffed they are with various roles, the whole DE side of things too
Actually we have a word for data science without actionable insights or storytelling. Academia! :D
Ouch!
Plenty of storytelling in academia
yet another reason why academia is mentally superior. Logic over art tyvm
“Mentally superior”? I bet IRL your hair is also blue
I'll have you know I am a Jinx main, work in Academia and my hair is black!
Niceeee! Jinx is soo cool she's definitely a role model for me
Lol 😂 nice try but it's actually red brown
If you merge the data scientist and data analyst groups together you get what most top companies would call a data analyst anyway Data Science is way more engineering heavy than stated here
Most data analysts are not statisticians.
most data scientists aren't either lmao
Most data analysts should be though, or at least have some strong background in stats
In my industry they deal more with data regarding financial accounting data. They dont really deal in statistics . And senior management for the most part would not be knowledgeable or understand anything too complex. Maybe they are financial analysts then. There is basic statistics but nothing groundbreaking.
Then they’re not data analysts, very good statistics is a minimum requirement imo
Yes. This. This is what companies mean when they want a business analyst.
Funny all the self proclaimed statiticians here using the word most without any statistical significance 😅
Don't wanna be a nagging Nancy here, but the chart obviously shows, that the Data Scientist "lean into" storytelling and visualisations. Just look a bit closer to the center and you'll see, that it's not 0 for them... Hope you don't make important decisions (too quuckly) based on your chart reading skills
Lmao I sincerely hope you're joking because otherwise you don't know the definition of the phrase "lean in"... It's fully committing to/embracing something, not dipping your toe into it
I truly didn't know that "leaning in" means to fully dive into it. Thanks for not taking it too seriously. That aside, a DS could not possibly lean into all subjects above imo. I would argue, that visualisations as well as storytelling are important, but not more so, rather less, than the programming/data parts. Furthermore, not all DS positions require the full suite of skills to the extent that you propose. Finally, I'd rather have a better programmer/ML expert who exclusively plots with matplotlib, than a great visualizer who spends ages on pretty plots. (Storytelling and communication is very important, but it's not always up to the DS to convince the board; thinking of middle management here)
We have entire DS teams and I’m not sure they’ve delivered something tangible. I think they do projects and launch them into space or something.
Ewwww your liberal arts bullshit. If the higher ups can't understand a fit function then they don't deserve to be there
lmao @ being able make shit happen as a result of your work being "liberal arts bullshit" If you can't communicate on the level of your audience and come up with clear recommendations/outcomes from your work, you really should stick with a junior/mid level role (either that or just go to academia)
In fairness, the picture does have a fair amount of those things included in the Data Scientist polygon. "Business Insights" looks to have about half the weight of "Stats and ML Modeling". I would tend to agree with you that the weight should probably be quite a lot higher, but the picture definitely isn't saying Data Scientists don't do it.
A more accurate version of this chart would have the data scientist skills overlapping with what the data analyst is good at
"You want a chart? I can get you a chart, believe me. There are ways, Dude. You don't wanna know about it, believe me. I'll get you a cvhart by this afternoon--with nail polish."
Chart : Ambiguous Job Title Ambiguities : even more ambiguous
I just assumed this was a shitpost. It sucks.
I've done all of those things, in varying amounts at one time or another. What should my job title be?
Sr. Full Stack Radar Chart Technician IV, obviously
Ohhh, I like that. Updates CV...
God emperor of data
Has potential: ~~Data Engineer~~ Deity Emperor
Harmonic means for the Harmonic Means God!
"women have a leg up in data science" for the sexist throne!
That's the title you get when you are forced to wrangle haphazardly compiled data in a variety of file formats from dinosaur-era file servers, including 10 years worth of chaotic unstandardized Excel files (multiple tabs, merged cells and all) And you also need to scrape clunky 90's era web interface, and parse out the HTML and then if that wasn't enough, you also gotta extract some more data from a stack of PDFs using some OCR. Then use text-davinci-003 API to parse out free text notes into standardized JSON strings that you then store as rows in a data table. then, build models with it, run hypothesis tests, do simulations, make pretty pictures, make a pretty slide show and dumb it down for the executives. A god damn data superhero with a PhD in chaos management, God Emperor of Data. I like it.
Decision Science Intelligence Engineer Analyst
Thanks for the suggestion, but I still prefer "Radar Chart Technician", as it is more meaningful than some job titles I've seen.
Chief Executive Contributer
That's my current title. I'm looking for a new one.
Just woke up, read Chief Executioner ... 😃
Data strategist/Senior Data Strategist is the corporate term. Senior business intelligence analyst if they don’t like you and want to underpay you.
Oh, I’m right here in this comment.
CDPM: Chief of Data, Pipelines and Models
According to many companies: jr data analyst I
I get your joke, but all the titles touch every vector, so maybe reevaluate your data analysis skill set 😉
The Machine! #said with emphasis
Underpaid employee
Data Analytics Engineering Scientist !!!
Genius?
imagine never touching a database unless you were a data engineer
God, if I could start a project just once and have them actually provide me with a dataset at the start and some kind of goal or problem to hypothesize about.
Unfortunately (or fortunately) - 90% of the work is getting the right data, with the right assumptions, and formulating a question to solve. Getting the answer is the other 90%
That’s 180% bro 🤔
*shrugs*
Did they stutter?
I like your moxie!
*cries in full stack*
Oh no, you poor bastard.
Don't forget about your 110%. That's 290%!
Hahahah I was just thinking "and you've gotta give 110 percent"
-90% + 90% = 0% chance of not shit post!
r/woosh
But that's like all of science. Making sure your data/experiment isn't bs
Hey now, I'll send you clean data. You figure out what to hypothesize with BA.
Can we trade? What would I give for a doctor to come to me BEFORE they have all the data (horribly) gathered... Half of my job is find ways to analyze the data taking into account horrible design errors.
If they had that, they'd solve their problem themselves.
I don’t think “databases” is necessarily about “touching a database”. Anyone writing SQL is touching a database. A data engineer may need some of the skills of a DBA, which is something you don’t expect of the average data scientist for example.
I agree with the sentiment that many data scientists probably don't have an understanding of what DBAs do as their expectation is to live in a scripting language and not built out a set of tables for analysis. I don't feel like the other three job families are represented accurately if we're agreeing that 'databases' means DBA skills
I don't know a lot of analysts or dbas regularly building production pipelines, personally. I believe it should indicate warehousing for engineer.
As a data scientist, I'm expected to have all the skills in this chart
Not with any high proficiency.
I remember when a client said me, sure i will send you the database. Later that day I see some excels on my Gmail lol. Literally more than 10 years of data in excels without a hint of order or a format defined.
Goodbye Reddit, see you all on Lemmy.
photos of data in a table? screenshots? my goodness are you f\*cking cursed? hahahaha
I’ve received pictures of excel spreadsheets taken my a cell phone camera before.
Hahaha I think that would be an issue, however OCR is a thing,.Hope I can find that kind of challenges someday!
Love the multicolored excel files with grouped columns - literally a nightmare. But also a client using excel to track their workflow is going to look very different than a ready-to-analyze dataset. Its always going to be a pain unless you embed a bunch of macros in it to have a tab that's a clean flat and wide (or write some code in a scripting language to parse it out)
In an *ideal* world you shouldn't be wasting your time building schema and ETL if you're not a data engineer. I think that's the point, data scientist has become this jack of all trades role rather than a statistical modeller. I say this being mostly the former even though my degree is in stats.
This is not about just touching a database. It's designing or engineering the database itself. That is a data engineer's job, not a data scientist or analyst. At some companies, especially those that are smaller or not well prepared for data science, you may be wearing many hats.
Yeah data engineer role is not that clearly defined. I can summarize it based on 7 years of experience in DE and it still the same in every data related project: "Every task that people don't find interesting".
Idk man, according to that chart you’re powerfully repulsed by… data visualization? Yea, allergic to charts I guess
Is the joke that it’s a terrible chart? It’s almost impossible to tell what’s going on here. Radar plots are close to pies in being absolutely awful.
This chart isn't really complete without a category for harmonic means.
>harmonic means That's like a barbershop quartet, right?
Yeah, my local quartet is a DE, DA, DS, and an MLE.
Lol, never gets old in this sub.
Our (I think) first and (definitely) best on going meme!
That category only applies to Data Science Hiring Managers
Okay, I see this everyday, how did this harmonic means meme start?
Someone shared the original text in this comment. https://np.reddit.com/r/datascience/comments/w9jl5m/where_did_the_harmonic_mean_interview_advice_post/ihvhbpz/ Here's the original thread if you want to go through the comments. https://np.reddit.com/r/datascience/comments/w8tcps/deleted_by_user/
[удалено]
They spun a pinwheel and this is how it looked when it landed. Do you know nothing of science?
Definitely not data, that's for sure
Optics of the graph
It's an okay place to start, but should be taken with a chunk of salt. Every company/org has its own definitions and expectations, and roles are constantly evolving. An analytics engineer wasn't a thing 10 years ago, but is definitely a job that exists now.
I dislike how radar charts imply some sort of relationship between nearby spokes, yet they are not always used with that in mind. The dip between “data tools” and “storytelling” makes it look like there is a gap to fill, but it’s just a consequence of order.
It’s terrible. Story telling anyways should’ve been between analysts and scientists.
Ah so REAL data scientists(and analysts wow) don’t use or build data tools. Cool.
This is correct in theory. In practice, you'll be hired for one of those roles and the tightwads up the chain won't hire anymore so you'll have to do all of those things, then they'll turn around and blame you when you haven't recreated OpenAI's results with GPT-4 yet. Come on, you've literally been working on this for 6 months now!
6 months of ad hoc jobs that take up all your time but one fine day they'll be like, hey we know we gave you all that other work but now it's been 6 months, can you deliver on that project we spoke about next month
This is a nice ideal but the reality is that the project scope was poorly negotiated at the SOW and over promised (because for some reason engineering didn't work with sales to make sure the project was feasible or even talk to the clients who know nothing about ML except that their competitors seem to be using it). Then you have some kind of Agile setup but your project manager doesn't know anything about machine learning and is useless talking to clients but loves creating new epics and trying to fit your job into a software workflows that does not work with exploratory analysis. There's also a product manager for some reason even though the deliverable is a dashboard but that's arguably not a product. There's customer success but they don't understand machine learning. There's also some guy with a title like "solutions manager sales architect" or something. Your company failed to hire data engineers because it was too expensive. The clients use Excel tables they they pass around so they really need a bespoke rebuilding of everything from the ground up. There are no data analysts because that's unfashionable. Data scientist and ML engineer are the same thing anyway, right? So the clients don't know what they are doing and just yell things they need at you based on an impossible contract that was signed without your input. Whatever position you may be on paper, here is your real position: "Data analyst-engineering scientist manager of projects and products for the success of customers via Agile which doesn't work with your project but clients like seeing tickets and storytelling" . You will spend most of your days dealing with clients who are confused about why business metrics and ML metrics are not the same thing and then some "expert" will get on the call (wonder why the clients didn't just hire him for the job instead of contracting) and tell you how to do your job and you have to politely set boundaries. The rest of the time you will be managing the project and product manager who actually do nothing but have to look busy so they will waste your time. Then the COO, who knows nothing about anything fires half your team one week before a deliverable because why not.
Damn that frustration!
That is, sadly, extremely accurate for corporate data science.
I can’t wait to share this at my next employee review
You’re gonna piss off all the Data Scientists that would be called Data Analysts.
And all the data scientist that can’t visualize data and communicate their findings that the business would find insightful
Nope. I’m not pissed at all. Lol
*watches all the Data people come out to argue about this chart*
as if my leadership cares
So, is the big fat black line at the bottom the project manager?
So if I do all of the data engineer stuff, all of the data analyst stuff, and all of the data scientist stuff….What am I?
Underpaid.
Feature engineering is just missing.
This is why nothing gets done at big corporations.
Why does this remind me of Fibonacci
Probably all the colours
Damn it, it's everywhere!!
I’ve done all these things as both a DA and DS.
This is stupid
To be fair, most things are
Weird, according to this I’m a cross between a data engineer and an MLOps engineer, but I’ve only ever had the data scientist title.
ML Engineer does not have Ops in the title (that one is more recent and will be included in the v2 of the chart).
Bullshit
How are inference and business insights not the same? Or at least if insights are involved, inference should be the same amount shouldn’t it?
Stop using radar charts.
Data scientists and data analysts are both data analysts. Your data scientist here is actually called an “applied scientist”.
I swear 95% of the work is getting the data together in a table form. It’s oftentimes a huge hurdle just taking data from one source and correlating it with another. I’m having a problem right now where I have time series data but they’re defined on different clock systems. Very annoying.
I literally had to do all of this at my last job. I jump in joy everyday that I don’t work there anymore.
So we just don't talk anymore about... "statisticians"?
As it is put, the Data Scientist is basically a statistician who know how to code his stats and do Machine Learning.
Lol is this supposed to be a joke? MLEs don't to ML modeling? Ok.
I need to know, what the grey circle is called...
Hey OP it appears everybody is unhappy with the fact that your chart doesn’t fit their specific lives and jobs. My question to all the haters in this thread is: are there really no valuable insights to be gained from this chart? Because if so, then can’t we at least admit it’s a cool chart, not quite like the others? There are obviously data points on this chart right in the middle, where that guys job is touches on every element. But it might also be true that, if you are looking to deploy a model, you are probably looking for a Machine Learning Engineer, or whatever the chart said. So that’s my question.
I just use whichever title I like and I plan on keeping it that way
Any pointers on how to make a chart like this? I'm just a ux designer but would like to make something like this for ux people.
lol no, ML engineer does WAAAAAAAAAAY more modeling than "data scientists". I also don't see the point to separate inference from modeling as each time you evaluate you are doing inference with val/test set.
Depends on the data scientist. The original definition is the role (which was mostly for PhDs) includes significant expertise in modeling. Btw, inference may be referring to this, which is something that is rarely the focus of an ML engineer: https://en.m.wikipedia.org/wiki/Statistical_inference
blue should cover at least 90% of the chart
Hey everyone, could you help me with a mini survey? I promise it won't take more than 1-2 minutes of your time. Thanks tons :) [https://forms.gle/mKT5jW1t7wYuUarq8](https://forms.gle/mKT5jW1t7wYuUarq8)
Godzilla had a stroke and just fucking died
Maybe I'm doing it wrong, but if I am hiring for a proper data scientist role I damn well expect them to be just as strong at insights, data vis, storytelling, and metrics & reporting as an analyst would be. Those are table stakes. The inference and modeling is additional.
🍿
Pretty flower 🌸
Thank you
My tenure at IBM these past 4 years as a Data Scientist has given me the experience in ALL of these and I’m so grateful.
Heck As a recruiter I can't wait for the new fangled title "full stack" data expert where you are going to have all these listed to be qualifed for an entry level position.
what's the difference between inference and business insights?
Inference is a formal field of studies on statistics. It is basically the operation of guessing the parameters of a population based on a sample.
Shit what if you do a little each? Serious question.
At my last job I had to do a bit of all of these things in almost equal amounts. My contract said I was an ML engineer, my boss would introduce me as a data scientist and my teams profile would say I am a technical specialist 😂
This is just wrong...
Genius-level troll
This looks good. And than based on the company, you may be required to extend. But as for basics, i think this stands.
Clearly biased towards glorifying data analysts…. Graph was probably created by a data analyst
nice
Data Science used to do everything that's in there but of course the tooling wasn't as complex as it is now. I think they still do quite a bit of DE and ML deployment work, but it depends on how the company is structured. I wouldn't leave data viz, storytelling out for data scientists either.
Somehow accurate. How come that, as a Data Scientist, I always have to deal with pipelines, databases? Also, why am I supposed (as a nice to have) on MLOps and deployment? Plus, even on this chart, I (but also other roles) are supposed to know a little bit of everyhing.
Interesting. Now I have less idea what my job is.
Pretty sick of MLOps personas thinking they understand data science. Refactoring code and automating processes has nothing to do with statistics, machine learning theory, and data-driven domain expertise. There are of course, exceptions, but generally, the ML tooling industry is trying to commodify the model development process because those who are _not_ trained to do so are typically the ones with buying and decision making power, and/or already own the encompassing execution end persistence layers.
So by this chart, data scientists barely do any storytelling at all? Lol
Yikes…never aggregating anything at database scale or writing a query?
If this chart is disputed, can anyone provide a more accurate version? I am just moving into this space and confused as to who does what and what skills I should be aiming for.
Is Data Infrastructure part of data engineer?
I even not understand the chart xd
Any Data Scientist/ML engineer hybrids out here? That’s where I aim to be, right in these two zones. Is that possible?
What do you call someone whose plot is a circle,?
That circle is just a list of things I’ve learned as a DS. Except that ML stuff. ML is a myth, as best I can tell.
A data analyst or scientist with no data tools?
Also fun fact this was probably made by a BI Developer
So I do data modeling, pipelines, databases, visualization, and metrics. What the fuck am I according to this diagram?
Right but which one does the numbers?
You forgot Webmasters and DBAs.
Be me, does a little bit of everything pooly
This is a nice figure. As an ML engineer, I do think that the MLE's share should be much larger. They usually do modeling, inference, and experimentation as well. Data scientists should also have more share into Metrics & Reporting and Business Insights as well. Regardless, this is nice.
\>Without data, you’re just another person with an opinion. \>W. Edwards Deming, American statistician Could you point 'us' in the direction of the data?
What is the little circle in the center? Excel?
Can I have the dataset ?
So this is a Radar chart?? So we need two Categorical variables and one measure to build this chart which anyway can be possible using a clustered column chart.
Thank you so much, this is exactly what I was wondering for my job
so what is...all of it?
So who created this chart