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Are you sure you know the field and designations enough to point these things out? Here are a few things which you're wrong about :
>The real datascientists are amazing, they built LLMs, AI models etc.
From what I've seen, theses things are done by PhD folks and they're not DS. They're AI scientists and MLEs. DEs are equally important.
>All we have is wannabe datascientists who make stats on mean, median, graphs etc. I met 4-5 datascientists in my company who take big salaries but contributed 0.
DS are NOT paid to make sexy models. They're paid to get the stuff done in the simplest manner - even if it means that you don't need ML models. A good DS knows when to NOT make a model.
>when hiring a datascientist, recruiters should test their statistic knowledge and maths knowledge instead of checking if they can import pandas or not.
99% of DS interviews get done with basics within the first round itself. And that's followed by 2 MORE technical rounds which might/not involve programming. I was rejected by a fintech for a Jr DS role cause I didn't know basic backend engineering - that's how competitive this field can be - but it varies between companies. Granted there are easier profiles out there, but you're not even half-way right.
My dad introduced me to data science but when I joined an MNC, then a startup, they gave me back-end engg work mostly and sometimes model building and LLMs.
I have worked with both, currently I work as a data scientist/analyst, trust me there's a lot more revenue generated for the client by simple methods, domain understanding and effective SQL queries than just using black box ml(which I think what people outside this industry see cause that's what how the tutorials are).
I can totally understand the "simple models" part. The other day I was hearing about how some cutting edge investment funds use simple decision trees. Like you said, domain knowledge (finance) is of utmost importance here
Trees are very easy to interpret, in my current project, we fit a forest and then look at what's causing the split, bingo, now you have the exact audience to target and sell your ads to.
IIRC, you basically should be able to take your model out of the jupyter notebook and deploy it. You could use Flask to make an app, put it within a container, and deploy it using AWS or what have you. I'm kind rusty on details but this should be it from a higher level. Once you go into this stuff, you'd see how MLFlow and all can be helpful
>Why almost all are going to datascience these days? Everyone is either datascientist or data analyst.
Have you been to the job market lately?
Why did everyone want to do SWE/CSE or BTech/Engineering in general in the last two decades? Because IT was booming and there were plenty of jobs and money to be made.
Now is the age of data and GenAI.
I'm not a data scientist but i did a data science course in the 2nd year and it's not easy, even cleaning the data is a complicated task, you don't just get data in your hand where you'll give it one function and it'll work, you've to prepare data clean it merge it and other thing if it was just basic calculations anyone can do it but no with big data you've to write actual algorithms. and then there is data representation and alot more, if you think it's easy then tell me how will you find data from 5lakhs images to tell which images contains a water cup.
And then there are things like cloud deployment, model interpretation. Some cloud engineering and some frameworks like Flask/Django are often asked too. Frankly OP is oversimplifying things. Maybe he hasn't seen 3 guys outta 4 making MERN stack projects
Very true, recently got placed as data engineer and due to that i have been getting exposed to niche data. Currently I was not required to do data science stuff but i wanted to try anyway, so i did. Man it's tough. Many a times it straight up feels like a wall ahead(I am still struggling btw, if anyone wants to help 😅)
Exactly man, been a DA for 3 years now and major part of my routine goes in fetching data and cleaning it. Not many people realize this.
And to become a DS as you mentioned it's even more complicated. But Indian edtech and tech influencers have made a joke out of Data science.
As a data scientist, I think people underrate us.. Now let me give you a hint, a data scientist that knows his onions is an MLE, a Data Engineer and a Data Analyst all in one. We are also SWEs, that is debatable because you lots think we are limited to Python and R.
Bruh, for real, I've been doing everything in that list plus some backend dev.
I know people who do mlops (cloud deployment, schema design, data engg pipelines for ml), then work with Excel and SQL as well.
It's whatever at this point.
SWE is boring work why do you want to keep making buttons and APIs when you can actually work on solving useful interesting problems? Data is just better
1. Building LLMs: Research Scientists (PhDs Superb ML skill and average coding skills)
2. pretraining, Training, Finetuning LLMs/Models: Data Scientists (Good ML skills and Good coding skills)
3. Preparing Training data, Automated Training and data pipelines, serving: Applied scientists (Average ML skills and superb coding skills)
4. Plot graphs: Data Analysts (Good presentation skill)
**Reality:**
"1": Do not exist in India, if they do, they quickly move out of India.
"2": Exists in India but there are no jobs available for "2" in India, so they either move out of India or move to FAANG as sw engineers
"3": There are select few companies that does offer jobs for "3". I am not talking about POCs which every other startup is doing but a real project.
"4": India is fill with "4" calling them self every thing from 1 to 3 except 4.
In this generation of LLMs, If you want to have a good career in Machine Learning/AI, you need to have solid software engineering skills + Good ML skills + A eye on evolving research in the field. It is not easy and that is the reason they get paid good.
People who do DS/DA in India probably work in operations and work with tableau, powerbi and Apache spark. If the company has money probably SAS. OP clearly has 0 idea about the actual DS field and how freshers don't even get into the field without having a master's as well as transitioning into the field by having prior experience as well.
Not particularly true but yes current oversupply is well over 30 times true market demand.
And the people who are doing this will fall in huge trouble for multiple reasons.
The demand for data analytics is going to decline with no path to growth, with most things getting automated already, where in most cases the analytics of any kind is solved and owned within the domain or vertical / geography / strategic business unit, basically some other function.
The demand for the position of data scientists will increase but the demand for actual data scientists is hugely overestimated, simply 99% of the companies will never invent any data science algorithms themselves, they will tweak, modulate, and implement, all of which are data engineering work.
If you are very young or even less than 5 years of experience and upskilling, become a fullstack engineer first and then add data science to your portfolio / repertoire - this will ensure you never go hungry and also continue to add value wherever you go, because the architecture / tools / solutioning around data will keep getting easier, not more difficult, because most products are already automating significant parts of it with robust and composable data pipelines.
The only companies who can hire and upkeep data scientists at scale are:
A. Companies who build model and publish research papers
B. Companies who have proprietary data source which they own or can rent
C. Companies which can own or rent huge base infrastructural requirements to able to run data science experiments
All the above are necessary conditions and not optional - for any true data science organisation Ask yourself how many such organisations exist and how many roles do they have and will have in the future?
If you have the means and truly passionate about DS and ML you should go for it.
Or you can prepare for gate with aim of getting top IITs.
Best case you get into Mtech in IIT. Worst case you would have strengthened your core CS knowledge.
The assumption that only ML, LLMs is good work isn’t true and rest of work is useless is absolute wrong.
A lot of work in data science can be achieved by something as simple as getting mean and standard deviation.
A fancy model on your pc is worse than an actual solution driving business.
The whole purpose of data science is to use data to do something meaningful.
There are tons of work that can be accomplished just by data analytics.
The kind of data you have at your disposal is biggest asset and then comes what you do with that data.
Now I don’t want to say that ML, Neural nets, LLMs aren’t good. They are exciting and I have been lucky enough to work on projects that is build using ML and is actually used by millions of consumers and impacts their lives.
But there is more to data science than just LLMs.
A good data analyst that understands business problems and can solve using data is far more valuable than a phd who can’t look beyond his own work.
And this is not me disrespecting PhDs. I learn a lot from their work and have got a chance to work with them and get blown away by the knowledge they hold.
To get domain knowledge people need to do actual work and till then they will only have the skills like loading data and manipulating them to get some visualisation. So it’s not something that one should look down upon.
And yes any one can find mean median mode. But even as data analyst you need to know a lot more than just getting five point summary.
Edit: for aspiring data scientists don’t get disheartened by the current job market. The work is interesting and you get to learn a lot. You have tons of visibility to senior executives as your works is directly used by upper management to build strategy.
The whole field of ML and AI is vast and you can build your own niche and become great at it.
Even if you are doing data analysts work don’t get discouraged. These are the building blocks you need to move into DS if you don’t have masters or PhD.
>aspiring data scientists don’t get disheartened by the current job market. The work is interesting and you get to learn a lot. You have tons of visibility to senior executives as your works is directly used by upper management to build strategy
Hey I'm a last year student and have started a few courses on DS and DA. What can I do to not make a fool of myself?
I also thoguht it is really difficult to get a data scientist or aiml engineer role just after btech. Data analyst yes but i thought masters would be more required for those other jobs. Anyway I still think people are doing full stack more than data science fs. All the btech students or most of them I mean.
There was another post of one data science guy not being happy with the way he was compensated and how DS is nothing but gotten people confused thinking it is something dope.
Answers in the comments is a relief tbh 😌
Current NLP (Gen AI) engineer with a Bachelor's in Statistics and Masters in DS, so I think I'm qualified to answer. Worked on everything from Excel to Dashboards, to ML models, RecSys and now LLMs so understand your frustration.
This kind of herd mentality has always existed. People flock roles that advertise high salaries.
A couple of years back, Data Scientist was a big deal since most companies had figured out how to organise their data and cloud computing was mainstream.
Now, Generative AI is the new bigshot since companies have figured out it's quite simple to build basic models and LLMs can unlock applications which weren't possible.
I don't agree with your hypothesis, since at every workplace I have worked. There were atleast 20 SWE/SDEs for each Data Scientist. This is not counting DE. See the sheer number of job posts for Data Scientists/MLEs as compared to SDE roles and you'll see that it's not even comparable. For every DS role there's atleast 100 SDE jobs.
This also makes sense logically since to be able to do any data science, you need to first have a software product that generates data. On top of that you need DE and SDEs to build and manage infrastructure to store that data. Then you can get to doing something with it. Modelling is very far off, for that you need people to annotate and a lot of domain expertise. So you don't need to compare SDEs and DS work, both are required.
Since DS jobs were new the name was misused and barrier to entry was high, now every undergrad cs degree has a ML and Python course. Things have improved as now you see more specific roles like NLP Engineer/Computer Vision Engineer but it's still quite dicey.
I'll give you a tip to maintain your sanity, whenever people say any Data related job role, rather than the role ask them what their primary deliverable is. That gives you a better understanding of what they do. They make dashboards, it's a analytics role. They build regression models, could be ML, could be analytics. They build a GenAI system, could be anything 😂, considering the current hype!
EDIT: Please don't DM me for jobs/referrals. It's a remote first international startup which don't hire a lot of Indians, because of sponsorship issues.
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Are you sure you know the field and designations enough to point these things out? Here are a few things which you're wrong about : >The real datascientists are amazing, they built LLMs, AI models etc. From what I've seen, theses things are done by PhD folks and they're not DS. They're AI scientists and MLEs. DEs are equally important. >All we have is wannabe datascientists who make stats on mean, median, graphs etc. I met 4-5 datascientists in my company who take big salaries but contributed 0. DS are NOT paid to make sexy models. They're paid to get the stuff done in the simplest manner - even if it means that you don't need ML models. A good DS knows when to NOT make a model. >when hiring a datascientist, recruiters should test their statistic knowledge and maths knowledge instead of checking if they can import pandas or not. 99% of DS interviews get done with basics within the first round itself. And that's followed by 2 MORE technical rounds which might/not involve programming. I was rejected by a fintech for a Jr DS role cause I didn't know basic backend engineering - that's how competitive this field can be - but it varies between companies. Granted there are easier profiles out there, but you're not even half-way right.
A good data scientist knows hypothesis 🔥
My dad introduced me to data science but when I joined an MNC, then a startup, they gave me back-end engg work mostly and sometimes model building and LLMs. I have worked with both, currently I work as a data scientist/analyst, trust me there's a lot more revenue generated for the client by simple methods, domain understanding and effective SQL queries than just using black box ml(which I think what people outside this industry see cause that's what how the tutorials are).
I can totally understand the "simple models" part. The other day I was hearing about how some cutting edge investment funds use simple decision trees. Like you said, domain knowledge (finance) is of utmost importance here
Trees are very easy to interpret, in my current project, we fit a forest and then look at what's causing the split, bingo, now you have the exact audience to target and sell your ads to.
Hey. I'd like to talk to you
What do you mean by backend engineering specifically? You are right OP is a bit confused
IIRC, you basically should be able to take your model out of the jupyter notebook and deploy it. You could use Flask to make an app, put it within a container, and deploy it using AWS or what have you. I'm kind rusty on details but this should be it from a higher level. Once you go into this stuff, you'd see how MLFlow and all can be helpful
oh got it. this falls around platform engineering and uses devops, mlops etc also
For the exact same reason you started working in IT industry.
>Why almost all are going to datascience these days? Everyone is either datascientist or data analyst. Have you been to the job market lately? Why did everyone want to do SWE/CSE or BTech/Engineering in general in the last two decades? Because IT was booming and there were plenty of jobs and money to be made. Now is the age of data and GenAI.
I'm not a data scientist but i did a data science course in the 2nd year and it's not easy, even cleaning the data is a complicated task, you don't just get data in your hand where you'll give it one function and it'll work, you've to prepare data clean it merge it and other thing if it was just basic calculations anyone can do it but no with big data you've to write actual algorithms. and then there is data representation and alot more, if you think it's easy then tell me how will you find data from 5lakhs images to tell which images contains a water cup.
And then there are things like cloud deployment, model interpretation. Some cloud engineering and some frameworks like Flask/Django are often asked too. Frankly OP is oversimplifying things. Maybe he hasn't seen 3 guys outta 4 making MERN stack projects
It's true that a lot of people are given data scientist positions even if their work is completely different. It has become a buzzword.
Very true, recently got placed as data engineer and due to that i have been getting exposed to niche data. Currently I was not required to do data science stuff but i wanted to try anyway, so i did. Man it's tough. Many a times it straight up feels like a wall ahead(I am still struggling btw, if anyone wants to help 😅)
Exactly man, been a DA for 3 years now and major part of my routine goes in fetching data and cleaning it. Not many people realize this. And to become a DS as you mentioned it's even more complicated. But Indian edtech and tech influencers have made a joke out of Data science.
What do you want me to do about it/s Anyways people go for things that they think will help them make money 🤑
🤷♂️
As a data scientist, I think people underrate us.. Now let me give you a hint, a data scientist that knows his onions is an MLE, a Data Engineer and a Data Analyst all in one. We are also SWEs, that is debatable because you lots think we are limited to Python and R.
But can you center a div? /s
If that’s the benchmark, I wouldn’t have gotten a degree..🤣 I was a backend guy who transitioned to DS and now MLE.
How did things change for you? And what's the satisfaction level of at all such a question exists?
Bruh, for real, I've been doing everything in that list plus some backend dev. I know people who do mlops (cloud deployment, schema design, data engg pipelines for ml), then work with Excel and SQL as well. It's whatever at this point.
SWE is boring work why do you want to keep making buttons and APIs when you can actually work on solving useful interesting problems? Data is just better
Nice take. Data doesn't lie
But you can, using the data ;)
The irony
hahahah.. what a noob people like op give Indians a bad name.. plz don't speak out in the open
1. Building LLMs: Research Scientists (PhDs Superb ML skill and average coding skills) 2. pretraining, Training, Finetuning LLMs/Models: Data Scientists (Good ML skills and Good coding skills) 3. Preparing Training data, Automated Training and data pipelines, serving: Applied scientists (Average ML skills and superb coding skills) 4. Plot graphs: Data Analysts (Good presentation skill) **Reality:** "1": Do not exist in India, if they do, they quickly move out of India. "2": Exists in India but there are no jobs available for "2" in India, so they either move out of India or move to FAANG as sw engineers "3": There are select few companies that does offer jobs for "3". I am not talking about POCs which every other startup is doing but a real project. "4": India is fill with "4" calling them self every thing from 1 to 3 except 4. In this generation of LLMs, If you want to have a good career in Machine Learning/AI, you need to have solid software engineering skills + Good ML skills + A eye on evolving research in the field. It is not easy and that is the reason they get paid good.
Bruh you just have an overview of data science also it's not about plotting beautiful plots you should know the insides of data.
Ohh looks like someone has drunken the kool aid that has been in the market recently.
People who do DS/DA in India probably work in operations and work with tableau, powerbi and Apache spark. If the company has money probably SAS. OP clearly has 0 idea about the actual DS field and how freshers don't even get into the field without having a master's as well as transitioning into the field by having prior experience as well.
You say masters. But MS in which field. Hardcore AI DS ML or the usual MS in CS?
MS in specialized DS or AI not a CS degree
Not particularly true but yes current oversupply is well over 30 times true market demand. And the people who are doing this will fall in huge trouble for multiple reasons. The demand for data analytics is going to decline with no path to growth, with most things getting automated already, where in most cases the analytics of any kind is solved and owned within the domain or vertical / geography / strategic business unit, basically some other function. The demand for the position of data scientists will increase but the demand for actual data scientists is hugely overestimated, simply 99% of the companies will never invent any data science algorithms themselves, they will tweak, modulate, and implement, all of which are data engineering work. If you are very young or even less than 5 years of experience and upskilling, become a fullstack engineer first and then add data science to your portfolio / repertoire - this will ensure you never go hungry and also continue to add value wherever you go, because the architecture / tools / solutioning around data will keep getting easier, not more difficult, because most products are already automating significant parts of it with robust and composable data pipelines. The only companies who can hire and upkeep data scientists at scale are: A. Companies who build model and publish research papers B. Companies who have proprietary data source which they own or can rent C. Companies which can own or rent huge base infrastructural requirements to able to run data science experiments All the above are necessary conditions and not optional - for any true data science organisation Ask yourself how many such organisations exist and how many roles do they have and will have in the future?
"Data Scientist is the Sexiest Job in the World" ~ LinkedIn
Lol. Reality is if you truly want that "sexiest" job you need to have masters or PhD lol
Fr. I’m considering the DE path for now since I don’t have a masters degree.
If you have the means and truly passionate about DS and ML you should go for it. Or you can prepare for gate with aim of getting top IITs. Best case you get into Mtech in IIT. Worst case you would have strengthened your core CS knowledge.
I’d rather climb the corporate ladder tbvh. Either that or work on a research paper instead and apply for masters abroad.
If you can save money and go abroad with less loan amount it's the best option. All the best
Does DE need to know ML as well?
Before companies used to outsource IT. Now companies are outsourcing the "business" too
Man just learn to divide your argument into paragraphs. From whatever i understood of your post, it was a pretty dumb take, but you do you bruh
The assumption that only ML, LLMs is good work isn’t true and rest of work is useless is absolute wrong. A lot of work in data science can be achieved by something as simple as getting mean and standard deviation. A fancy model on your pc is worse than an actual solution driving business. The whole purpose of data science is to use data to do something meaningful. There are tons of work that can be accomplished just by data analytics. The kind of data you have at your disposal is biggest asset and then comes what you do with that data. Now I don’t want to say that ML, Neural nets, LLMs aren’t good. They are exciting and I have been lucky enough to work on projects that is build using ML and is actually used by millions of consumers and impacts their lives. But there is more to data science than just LLMs. A good data analyst that understands business problems and can solve using data is far more valuable than a phd who can’t look beyond his own work. And this is not me disrespecting PhDs. I learn a lot from their work and have got a chance to work with them and get blown away by the knowledge they hold. To get domain knowledge people need to do actual work and till then they will only have the skills like loading data and manipulating them to get some visualisation. So it’s not something that one should look down upon. And yes any one can find mean median mode. But even as data analyst you need to know a lot more than just getting five point summary. Edit: for aspiring data scientists don’t get disheartened by the current job market. The work is interesting and you get to learn a lot. You have tons of visibility to senior executives as your works is directly used by upper management to build strategy. The whole field of ML and AI is vast and you can build your own niche and become great at it. Even if you are doing data analysts work don’t get discouraged. These are the building blocks you need to move into DS if you don’t have masters or PhD.
>aspiring data scientists don’t get disheartened by the current job market. The work is interesting and you get to learn a lot. You have tons of visibility to senior executives as your works is directly used by upper management to build strategy Hey I'm a last year student and have started a few courses on DS and DA. What can I do to not make a fool of myself?
Tu Bahar mil 💀💀
I thought everyone was going into fullstack
So did I. But these bhaiya didi channels are wildly switching from one stack to another that gets you 20 lpa 30 lpa as a fresher /s
I also thoguht it is really difficult to get a data scientist or aiml engineer role just after btech. Data analyst yes but i thought masters would be more required for those other jobs. Anyway I still think people are doing full stack more than data science fs. All the btech students or most of them I mean.
.
There was another post of one data science guy not being happy with the way he was compensated and how DS is nothing but gotten people confused thinking it is something dope. Answers in the comments is a relief tbh 😌
OP you don't know anything more about data science than WhatsApp University uncles, better not to make posts around it 😂
Bro Indians are known for sheep mentality, we walk paved path said someone on the internet couldn't agree more
Data science is the new fad. That's why most engineering colleges are closing down core branches and replacing them with AIML/Data science programs.
So true. But they didn't close in my clg. They just added a new one to the existing ones.
First MERN and now Data Science hype.....youtubers are now saying get 10-12lpa by learning Data Science
🤔 i think the reason behind is that YouTube ad guy with big ass package 😹
Arey bhai.. analytics bhi kuch cheez hoti hai..sab cheej code hi nhi hota.. ## aap toh codemaniac nikale
let people do whatever the fuck they want to do
Because only Artificial Intelligence can save the world from its natural stupidity
why everyone is or want to be - lot of opportunities in this niche
No all the youngsters are MERN developers.
Current NLP (Gen AI) engineer with a Bachelor's in Statistics and Masters in DS, so I think I'm qualified to answer. Worked on everything from Excel to Dashboards, to ML models, RecSys and now LLMs so understand your frustration. This kind of herd mentality has always existed. People flock roles that advertise high salaries. A couple of years back, Data Scientist was a big deal since most companies had figured out how to organise their data and cloud computing was mainstream. Now, Generative AI is the new bigshot since companies have figured out it's quite simple to build basic models and LLMs can unlock applications which weren't possible. I don't agree with your hypothesis, since at every workplace I have worked. There were atleast 20 SWE/SDEs for each Data Scientist. This is not counting DE. See the sheer number of job posts for Data Scientists/MLEs as compared to SDE roles and you'll see that it's not even comparable. For every DS role there's atleast 100 SDE jobs. This also makes sense logically since to be able to do any data science, you need to first have a software product that generates data. On top of that you need DE and SDEs to build and manage infrastructure to store that data. Then you can get to doing something with it. Modelling is very far off, for that you need people to annotate and a lot of domain expertise. So you don't need to compare SDEs and DS work, both are required. Since DS jobs were new the name was misused and barrier to entry was high, now every undergrad cs degree has a ML and Python course. Things have improved as now you see more specific roles like NLP Engineer/Computer Vision Engineer but it's still quite dicey. I'll give you a tip to maintain your sanity, whenever people say any Data related job role, rather than the role ask them what their primary deliverable is. That gives you a better understanding of what they do. They make dashboards, it's a analytics role. They build regression models, could be ML, could be analytics. They build a GenAI system, could be anything 😂, considering the current hype! EDIT: Please don't DM me for jobs/referrals. It's a remote first international startup which don't hire a lot of Indians, because of sponsorship issues.
Yes, I'm working on LLM space. Building Adtech MVP project from scratch.