1:22 What does the future of AI look like? 2:48 Becoming a data scientist @Uber 5:15 What's the difference between data scientist and ML engineer? 10:13 Do you need a CS degree? 12:04 Can a data scientist become a ML engineer? Vice versa? 12:42 What skills do you need to become a data scientist/ML engineer? 19:35 Who has a cooler job? 20:54 Tips and advice 22:33 Kickstart your data science/ML engineering career
What a shame ! she got a phd in transportation and join a taxi company while the Indian highway traffic is more interesting and much needed problem to solve. Waste of Berkeley money
My favourite part is the 80/20 rule: if the model is 95% accurate, that's brilliant, get that out. Improve for the next iteration of the product, not this time. That's business Vs academic. Business people know 80% is enough, don't waste time with the exponentially harder stuff.
She seems like she could be a very good teacher. She explains everything in detail and uses vocabulary that’s understandable to those who are not familiar with her industry. Would love to learn from her
really shows who's the data scientist amongst the two, data science requires good story telling and communication skills whereas for aa ML engineer not as much
In my organisation, data scientists are just paper tigers, more on talking(which they call story telling) and blabbering their statistical knowledge. No focus on solving the problems. While the ML Engineer does all the stuff, from selecting model, implementing things and optimizing problems. They are the real builders of products.
I totally understand, but isn’t it the same as the financial industry where the traders are the ones doing all the technical and fundamental analysis and the managers are the ones earning the big buck.
You guys are so inspiring. Thank you for making this video! In next 10 years if ML systems get smart enough to pick the models on their own, what do you think the human work will be about the ML?
I think it'll open more opurtunities to more engineers instead. A human would be always required to select and create the best algorithm to train the models and for the direction.
Data Science requires critical thinking skills. MLE does not. If you can think critically and are capable/enjoy creative work, then you'll likely enjoy being a Data Scientist. If you are weak in critical thinking, but can compensate by being good at following instructions, then MLE is probably a better suited role for you. MLE's without Data Scientists do not create value. Data Scientists are like the professors who generate the vision and define what is/isn't possible, MLE's are like their graduate students that do the tedious work to realize such a vision. Data Scientists can easily become MLEs if they are interested, but often MLE's cannot become good Data Scientists (since MLE work does not lend itself to teaching the required critical thinking skills). That's mostly because you can learn Engineering skills on the job rather quickly through exposure, but you cannot learn how to think critically just by being around people who can. An MLE turned Data Scientist would need to have additional experience that demonstrates they can think critically. Having had a few experiences doing original research in some domain in arguably the best way to cultivate those skills. In my organization, the ML Engineers care more about building models than actually creating something useful for the business. I call it "Model building without purpose." Models are built all the time that end up serving no functional purpose, costing the organization lots of wasted $$$. Unfortunately, the MLE culture here is "allergic" to business culture. My company is wising up to this and now MLE roles no longer exist that allow one to skirt their responsibilities to the business that pays them.
You should be ignorant out of your nuts. There is nothing "scientist" about a data scientist. Majority of the time they spend involve in cleaning the data, setting up pipelines and crunch some number which no one gives a shit about. I have couple of friends who are Data Scientists at google and facebook and they envy the ML engineering team as they get to do real life impactful stuff. In FAANG, most of the ML engineers are basically Research Engineers who involve in the end-end design and delivery pipeline. I don't know which company you work for. But that's how most of the real ones work here in bay area. I am part of FAANG and trust me when I tell you - No one gives a shit about Data Scientists other than the fact they are super overhyped for what they do. Data analysts in top fintech firm would suit your definition of having "critical" thinking and "analytical" abilities. Not in facebook and uber. I mean.... "uber".... seriously? Data Scientists are essentially the mushrooms sitting and costing millions in payroll for the company. Let's keep this real. Call them data analysts... Give them the pipelines... Or better.... Stitch the workflow with the help of a real Data engineer and business gets all the real analytics needed to run their daily ops. Are you freakin kidding me?
"Data Scientists can easily become MLEs if they are interested" - strongly disagree. Carrying your analogy further professors can't code shit, graduate students are the real geniuses who manifest results for mere philosophies with actual experimental evidence in the form of algorithmic implementations. the devil always lies in the practical details, however fancy and 'critically though-out' the theory is
@@sukhbir24 Thanks for sharing your misinformed sales pitch. You are clearly selling something -- likely your skillset -- sounds like you are threatened by highly skilled people and the only way you can survive is by spreading misinformation and profiting from it. Best of luck with that. Reality always wins in the end. And that's exactly what's happening now with the change with GenAI. SWE jobs are getting automated.
hahaha looks like I touched a nerve, sorry I took you down from your high horse. those who stick to the cliché that software jobs are going to be automated by generative AI often don’t know the first thing about transformers, how they are designed/implemented or their failure cases. I do applied ML research so know where I am coming from. Highly skilled technical people will agree with what I said atleast partially, certainly you are not one of them based on your biased arguments. I am not selling anything and not at all threatened by the likes of you, just amused by your hubris and reality distortion field. So keep on patting yourself at the back and pretending to be an expert.
I think the cooler job really depends on which part you are more interested in. In the video I feel the lady has developed better communication skills interacting with Stake holders which could make her feel more confident. On the other end, as a ML Engineer Nikunj can feel more confident that DS is relying on their implementation. Overall, made of each other couple.
Amazing video! It has really help me to decide what I want to do and where I am! Where I can give more justice. Thank you so much! Keep on giving such amazing content!
Springboard I am facing difficulties in getting job because the job description of data science or ML engineer differ a lot. Sometimes they asked for full stack with ML engineer, data structure. It’s not possible to remember everything. So if possible one video for how to vulnerable to such things and how to prepare minimal so it should not cause problems? Thanks
Kids are itself confused between Data engineer, DS , ML , Statistician and Mathematician ( like traffic problem is a problem of multi point linear optimisation with constraints not data science ) Common sense and attitude is more important than degree Transition of role should be based on natural progression algorthimically : mathematics -- statistics -- ML -- AI Know-how if database solution design etc require technical architecture stack not ML engineer There are 180+ classification algorithm hence if one learn maths equation of each it will take ages to master just need to have common sense it's more than suffice or use cat-boost by yandex
Springboard, can you show some people who had a CS degree from unknown Indian college (Not any IIT or NIT BIT..) and did not have any experience and became Data Scientists by online education and then reached (after working at small companies as Data Scientists maybe) Facebook, Uber, Amazon? It's easy to be Data Scientist when you are from IIT, UC Berkley etc, they could be in NASA too, not a big deal. The real struggle and insight can only come from people who started from nothing.
I see a lot of people considering both as same job which is totally wrong. Although they wear many hats but their core job is totally different. A DS is based on discovery and ML is based on development.
@@springboard Oh, I don't have anything particular in mind. You guys are very likable & smart, I sincerely enjoy listening to you. That said, even if you don't have one particular topic, and you just chat about interesting books you read, conferences you attended, technical challenges you are wrestling with ... anything worth sharing with the world ... along the lines of "The world of ML engineering & data science according to Sreeta & Nikunj". I think a lot of people would tune in, share their thoughts, ask questions and engage. What do you think?
@@SpokenStuff Cool, thanks for the suggestion! You should check out our new Real Talk video, and let us know what you think: ruclips.net/video/lUaKgFvf42A/видео.html
ML is cooler bcos they lead the blackbox n people use, but wait data scientist is way cooler bcos they tell stories to leaders, as a pair they are coolest bcos highly rewarded for their pioneering work👍😎thanks sharing
Being an civil engineer can i study for programming...i mean does it make sense in reality....or am i just going to mess up everything....i really want to know please reply anyone
Then why statistician are more demanding degree and ml is all based on statistical systems and why data scientist are statistician? In india 90% ppl having degree in engineering but 90% of ppl are having 15000 to 20000 jobs I want this answer
Economics , demand and supply .. supply of graduates is way higher and the demand for workforce have reduced overtime, due to incredible growth of tech industry supporting all fields with less human work while computers do the most. U have to exceptionally stand-out and show that u can provide value for the company to land big prize. Ofcourse there are other factors , if say variables effecting this issue too .. but I feel what I mentioned is major one
Looking at both of their profiles - the ML engineering guy has way more of real cred quals than the "Data Scientist" here. Her entire Ph.D. tenure was of two years(go figure) and they pubs are seriously hilarious. Go ahead and call me arrogant but there needs to be an ounce of humility that comes with your knowledge(looking at how the "data scientist" talks over here). I am part of FAANG and I tell you - ML is still growing; lot of things are being defined at the moment. Data Scientists are hyped up data analysts. At least the real ones in fintech (east companies) are fantastic and real. They have amazing creds in terms of aptitude. Their interviews literally set and raise the bar for being data analysts. Uber Data Scientists are a joke. A very good friend of mine (Who is a DS at Uber btw) laughs every time I ask her how would she compare Uber's DS interviews with the likes in real fintech ones. Seriously. The ML Engineer's LinkedIn profile in this video has quantifiable creds. Masters from Berkeley. Computer Science - Neat. Knows his fundamentals. Worked as an ML engineer in other comps before joining facebook. And now a Senior in FB. That's a neat trajectory. You don't do a namesake PhD and get into Data Science. I stress on this people - There's no freakin science there. You know where's real Science? - "COMPUTER SCIENCE". There's a real reason why someone has put "SCIENCE" there. Go learn your DS, algo - be extremely thorough in these. Using the example she has referred to in the video: Point A to Point B? Freakin Travelling Salesman. I wonder what Data Scientists do over there? All these are glamorous roles which you will definitely learn as you specialize in your career role. Personal suggestion(not an advice) - STAY AWAY from Data Science as your career. It's hogwash. ML engineering - Definitely a linear trend ahead in terms of career progression. You get to work on real life impactful problems. You can measure your career success when you look back and quantify clearly what you have achieved. And there's a difference between schooling and education. Indians at least are starting to understand the spectrum of how IITians end up in their career. There are these actual top AIR folks (IITD CS and IITM CS) - talking only about CS - And they end in up amazing academic tracks at Caltech or MITs(true to what they have started doing in their career). And you have these "Data Scientists" with a namesake Ph.D. starting their track in random metallurgy and civil and ending up in these hogwash roles because the economy is well enough to pay them money. Sheesh!
What a lot of nonsense I just read. 'Data Science' is indeed an overhyped term, just as 'machine learning' is an overhyped term. In the end it is all computational mathematics and most of it computational statistics. And as far as I am concerned these are still SCIENCES. As a PHYSICIST and STATISTICIAN I say pray on your knees that physicists invented so much, otherwise computer scientists would still be playing with their little vacuum tubes. The ego of computer scientists is SOOO BIG. It takes one great innovation from physicists to put you guys all out of business.
@@michaelpieters1844 Completely agree with you. The argument over here is about Data Science only and about the folks sitting in this video. None of these two are physicists/true statisticians. The focus isn't there ergo I didn't comment on that perspective. You have just raised an argument to take a step back and think about the bigger picture. I get it and agree. And personally in terms of ego for CS folks - I agree with you as well. But with the track record in terms of money making stats in the past two decades - you gotta give it to us man. C'mon :) Even bullshitters like the ones sitting in this video make tons. Trust me.
First you need to learn solid Mathematics , become master of data structure Algorithm . You should be able to read and understand complex research paper and implement it. ML is not only about programming language . There is a reason that ML engineers are highest paid in the world bcoz they are doing highly complex work. You must invest urself on the learning curve of years to be a ML engineer .
@@anon-tp4ns I strongly disagree there bro. You don't need to have to strong a knowledge of math or stats or data structures. You start by learning how to program and implementing stuff, getting a high level overview of different algorithms and implementing them(None of which entails reading research papers at the start), implementing these algorithms to solve different problems and doing fun projects. Just start doing stuff and learning. Don't focus too much on the math or the stats part, things will come slowly and they will become clear with time. Just start programming and you will get there if you want to.
@@ish694 Bro , respect your opinion . My point is Machine Learning , Computer science are huge field and they deserve respect . You cannot take it casually and claim to master it. I have to read a lot of paper during my Projects in Deep Reinforcement learning , Machine learning etc to accomplish the project and course . Fir my internship I have spent 2 weeks understanding single paper . My point is , lots of people are taking ML very casually while floating on the surface of the field . Slowly slowly there will be lot of shallowness black boxing around this field bcoz ppl did not focus on understanding nuts and bolts . You imagine you got 50 million data points and you have to run a KNN on it , how would you do it ? You must come up with some efficient data structure Algorithm to implement it . If you don't understand diff between bias and variance . If don't understand probablities how you would do generative models . Similarly if you don't have idea of Finite State machine how you would understand Hidden Markov model or Baysean network . In industry ML requires great level of software engineering skills also to expose ML algis through API. All I am saying that , don't miss on fundamental basic otherwise your knowledge will be uncoocked and it would take many years to learn simple stuff , your approach will be like black boxing . But if focus on foundation init8ally it will be tough but later on your skill will be fruitful. ML is not just python library , 90 percent ML is conceptual intuition based on solid computational and Mathematical foundation validated of peer reviewed proofs.
Machine Learning Engineering is huge field . 90 percent ML engineers have very solid Software Engineering back ground . Maths , DSA , solid concept of computer science is essential . Once you have foundation then u can get in ML . Lots of youths are misguided because ot fancy RUclips videos doing cool things. But in reality it is very complicated field which requires lots of dedication , focus . There are hardly 10000 people in the world who can claim to be real AI engineer . You should get a computer science degree first . It will give you foundation to achieve ML goals . Fact - ML engineer are more than data scientist because their role is highly technical and manifests real data product .
@@chandrarama1970 I have studied Machine Learning and Deep Learning at Carnegie Mellon University and still I am learning everyday . I was software engineer in past as well. I don't think I can claim myself to be expert on ML yet. Lots of ppl on RUclips are projecting ML as some fancy field , run some python library do some small project and you are good to become AI engineer . Youths are getting lured with the idea if big money . In my understanding , you need a solid intellectual caliber to grow in field of AI and ML . This field is very intense and you need to learn everyday . If you are not good at analytical thinking and intuitively mathematical in nature , this field is not for you .
You would have to know programming basics and Mathematics. I don't really think you need a computer science degree,but do take a couple of online courses that teach programming.
You should also know SQL and bash; the rest typically vary depending on the job. If you're building applications, you will likely need Java or C. If you are working with big data, it would be good to know how to work with distributed systems using Scala. If you're doing a lot of matrix manipulation/calculation, MATLAB is great to know.
@John Bello's advice is very good. Also, do a Google search for influential data scientists, then look up those scientists' LinkedIn profiles and the skills they have listed there. You'll get a good idea of the essentials you need to know. 😊
The field of machine learning is still evolving. Conventional managers were heavily dependent on their experience for decision-making.think of a manager who has domain knowledge of the business problem, machine learning and practical experience of ground .you can think how effective this manger can be.
1:22 What does the future of AI look like?
2:48 Becoming a data scientist @Uber
5:15 What's the difference between data scientist and ML engineer?
10:13 Do you need a CS degree?
12:04 Can a data scientist become a ML engineer? Vice versa?
12:42 What skills do you need to become a data scientist/ML engineer?
19:35 Who has a cooler job?
20:54 Tips and advice
22:33 Kickstart your data science/ML engineering career
Have some actual experienced persons, kids have limited experience 🤪
Pickle file serialisation is ship to user what ML guy does 😁
What a shame ! she got a phd in transportation and join a taxi company while the Indian highway traffic is more interesting and much needed problem to solve. Waste of Berkeley money
Yes we are FRIENDS
Eventually we are married
yes we are married, RIGHT NOW
😅😅
Lmao they didn't seem like friends
Indeed she forgot.
She was like "oh fukc he just revealed the secret. How am I gonna get laid now."
😂
feminism made her devalue it
My favourite part is the 80/20 rule: if the model is 95% accurate, that's brilliant, get that out.
Improve for the next iteration of the product, not this time.
That's business Vs academic.
Business people know 80% is enough, don't waste time with the exponentially harder stuff.
The moment she says IIT-Bombay I am like damn there it ends half of my aspirations
why?
Same hpnd with me
It doesnt matter at all ,
You will realised this thing once you are technically stronger
hahaha
did you hear "civil engineering" after that
Nothing is more attractive in a person than his/her passion for what they are doing...
All I see is the big paycheck in one family 😍
Win
Lol
Exactly $$$$ 🤑
Win- win situation
BIG BIG LOL...
She seems like she could be a very good teacher. She explains everything in detail and uses vocabulary that’s understandable to those who are not familiar with her industry. Would love to learn from her
really shows who's the data scientist amongst the two, data science requires good story telling and communication skills whereas for aa ML engineer not as much
That's good advice. Make a model that's just good enough and deploy it, then iterate.
In my organisation, data scientists are just paper tigers, more on talking(which they call story telling) and blabbering their statistical knowledge. No focus on solving the problems.
While the ML Engineer does all the stuff, from selecting model, implementing things and optimizing problems. They are the real builders of products.
It's called irony of fate
I totally understand, but isn’t it the same as the financial industry where the traders are the ones doing all the technical and fundamental analysis and the managers are the ones earning the big buck.
Thanks for the video, i found it to be so useful!
Who disliked such an amazing content..so good to hear you both.
They itself are confused 🤪 and giving generic gyan
You guys are so inspiring. Thank you for making this video! In next 10 years if ML systems get smart enough to pick the models on their own, what do you think the human work will be about the ML?
I think it'll open more opurtunities to more engineers instead. A human would be always required to select and create the best algorithm to train the models and for the direction.
Data Science requires critical thinking skills. MLE does not. If you can think critically and are capable/enjoy creative work, then you'll likely enjoy being a Data Scientist. If you are weak in critical thinking, but can compensate by being good at following instructions, then MLE is probably a better suited role for you. MLE's without Data Scientists do not create value. Data Scientists are like the professors who generate the vision and define what is/isn't possible, MLE's are like their graduate students that do the tedious work to realize such a vision. Data Scientists can easily become MLEs if they are interested, but often MLE's cannot become good Data Scientists (since MLE work does not lend itself to teaching the required critical thinking skills). That's mostly because you can learn Engineering skills on the job rather quickly through exposure, but you cannot learn how to think critically just by being around people who can. An MLE turned Data Scientist would need to have additional experience that demonstrates they can think critically. Having had a few experiences doing original research in some domain in arguably the best way to cultivate those skills.
In my organization, the ML Engineers care more about building models than actually creating something useful for the business. I call it "Model building without purpose." Models are built all the time that end up serving no functional purpose, costing the organization lots of wasted $$$. Unfortunately, the MLE culture here is "allergic" to business culture. My company is wising up to this and now MLE roles no longer exist that allow one to skirt their responsibilities to the business that pays them.
You should be ignorant out of your nuts. There is nothing "scientist" about a data scientist. Majority of the time they spend involve in cleaning the data, setting up pipelines and crunch some number which no one gives a shit about. I have couple of friends who are Data Scientists at google and facebook and they envy the ML engineering team as they get to do real life impactful stuff. In FAANG, most of the ML engineers are basically Research Engineers who involve in the end-end design and delivery pipeline. I don't know which company you work for. But that's how most of the real ones work here in bay area.
I am part of FAANG and trust me when I tell you - No one gives a shit about Data Scientists other than the fact they are super overhyped for what they do. Data analysts in top fintech firm would suit your definition of having "critical" thinking and "analytical" abilities. Not in facebook and uber. I mean.... "uber".... seriously?
Data Scientists are essentially the mushrooms sitting and costing millions in payroll for the company. Let's keep this real. Call them data analysts... Give them the pipelines... Or better.... Stitch the workflow with the help of a real Data engineer and business gets all the real analytics needed to run their daily ops. Are you freakin kidding me?
"Data Scientists can easily become MLEs if they are interested" - strongly disagree. Carrying your analogy further professors can't code shit, graduate students are the real geniuses who manifest results for mere philosophies with actual experimental evidence in the form of algorithmic implementations. the devil always lies in the practical details, however fancy and 'critically though-out' the theory is
@@sukhbir24 Thanks for sharing your misinformed sales pitch. You are clearly selling something -- likely your skillset -- sounds like you are threatened by highly skilled people and the only way you can survive is by spreading misinformation and profiting from it. Best of luck with that. Reality always wins in the end. And that's exactly what's happening now with the change with GenAI. SWE jobs are getting automated.
hahaha looks like I touched a nerve, sorry I took you down from your high horse. those who stick to the cliché that software jobs are going to be automated by generative AI often don’t know the first thing about transformers, how they are designed/implemented or their failure cases. I do applied ML research so know where I am coming from. Highly skilled technical people will agree with what I said atleast partially, certainly you are not one of them based on your biased arguments. I am not selling anything and not at all threatened by the likes of you, just amused by your hubris and reality distortion field. So keep on patting yourself at the back and pretending to be an expert.
Damn Nikunj, you beauty, Malay Sir would be proud of you! I just got a nostalgia on old times, Cheers mate!
He almost got friend zoned
Bro they r already married
Love that you said at 16.25, that one needs to understand the math behind the scenes
I think the cooler job really depends on which part you are more interested in. In the video I feel the lady has developed better communication skills interacting with Stake holders which could make her feel more confident. On the other end, as a ML Engineer Nikunj can feel more confident that DS is relying on their implementation. Overall, made of each other couple.
great overview, really appreciated the different perspectives
Many thanks to both of you for this talk, really helpful ! I am an MLE and Nikunj's advice is very helpful.
That's great to hear! Let us know if we can help any further.
15:45 What skills do you need to become a data scientist/ML engineer?
The most wholesome thing I saw today ❤
ML+DS>> .......Great!
Wishing you both a very Happy Stunning Life!
ML + DS = 💸
Transitions that make sense :Software Engineer-> Machine Learning Engineer |Data analyst->Data Scientist
"It's one thing to make cool models for the coolness" love that
Amazing video! It has really help me to decide what I want to do and where I am! Where I can give more justice.
Thank you so much! Keep on giving such amazing content!
Rashmi Pandey Hi
Thanks for your comment! What other kind of content would you like to see from us?
Springboard I am facing difficulties in getting job because the job description of data science or ML engineer differ a lot. Sometimes they asked for full stack with ML engineer, data structure. It’s not possible to remember everything. So if possible one video for how to vulnerable to such things and how to prepare minimal so it should not cause problems?
Thanks
Kids are itself confused between Data engineer, DS , ML , Statistician and Mathematician ( like traffic problem is a problem of multi point linear optimisation with constraints not data science )
Common sense and attitude is more important than degree
Transition of role should be based on natural progression algorthimically :
mathematics -- statistics -- ML -- AI
Know-how if database solution design etc require technical architecture stack not ML engineer
There are 180+ classification algorithm hence if one learn maths equation of each it will take ages to master just need to have common sense it's more than suffice or use cat-boost by yandex
interesting how you added RIGHT NOW after we are married
I agree, fishy statement....who says RIGHT NOW
Very helpful presentation of ML and DS! thanks a lot!
Last question made me smile :)
It's still a job and you work for someone else. You know what is cooler, to work for yourself, to be free!!
Springboard, can you show some people who had a CS degree from unknown Indian college (Not any IIT or NIT BIT..) and did not have any experience and became Data Scientists by online education and then reached (after working at small companies as Data Scientists maybe) Facebook, Uber, Amazon?
It's easy to be Data Scientist when you are from IIT, UC Berkley etc, they could be in NASA too, not a big deal.
The real struggle and insight can only come from people who started from nothing.
Oh my God..You both are couple..Awesome!!!!!👌👌👌👌👌
Whoa this is a useful content.
love the way they look at each others
i.e. a data scientist is an engineering while a machine learning engineering is a scientific
I see a lot of people considering both as same job which is totally wrong. Although they wear many hats but their core job is totally different. A DS is based on discovery and ML is based on development.
Nice explanation of contrast between two. It has always been a confusion for me
immense info with real life experience...very very helpful!!
Good stuff for clearing interviews
Nice conversation to make us know the data science and machine learning difference
Its chits but how exact ml questions are picked by her. And ds questions are picked by him?
These are general questions which can be changed to context!
Its ML lol
so a mchine learning engineer has pretty much the same principles as a software engineer thats nice to know
Super conversation
I can keep listing all day.
I am s Senior Data scientist who doesn't wanna transfer to ML Engineer but I am learning to be both
That was a good interview..
This video was excellent. Thank you very much.
The Data Scientist fetches, filters and feeds the data into the models created by Machine Learning engineer.
Not necessarily correct. Typically a DS would do that part too
I really like listening tou you guys, do you have any plans to take this conversation further, like a regular podcast?
Thanks for your comment, Rafael! What would you want us to feature in a regular podcast?
@@springboard Oh, I don't have anything particular in mind. You guys are very likable & smart, I sincerely enjoy listening to you. That said, even if you don't have one particular topic, and you just chat about interesting books you read, conferences you attended, technical challenges you are wrestling with ... anything worth sharing with the world ... along the lines of "The world of ML engineering & data science according to Sreeta & Nikunj". I think a lot of people would tune in, share their thoughts, ask questions and engage. What do you think?
@@SpokenStuff Cool, thanks for the suggestion! You should check out our new Real Talk video, and let us know what you think: ruclips.net/video/lUaKgFvf42A/видео.html
Very nice :-) . Thank you for sharing
Adorable couple & nicely presented video💙
That guy looks like Varun dhawan :D, but such a cool content and cute couple
ML is cooler bcos they lead the blackbox n people use, but wait data scientist is way cooler bcos they tell stories to leaders, as a pair they are coolest bcos highly rewarded for their pioneering work👍😎thanks sharing
Very informative
Great talk. Really useful information.
i wondered if they fight often lmao
Being an civil engineer can i study for programming...i mean does it make sense in reality....or am i just going to mess up everything....i really want to know please reply anyone
Really inspired with her!!!🤩🤩
So what does this have to do with UBER?
Where has this channel been all along?
Civil Engineering seriously 🤓🤗
i wonder whats about indian culture that pushes education at that level.There must be something they do right
Wait...what are their names??
How can AI be bad for society and what ethical guard rails can we implement now and plan for ?
Deep Love engineers. You both are cool. .
I droped MBA to persue this course
What is the Roadmap of data scientist
Then why statistician are more demanding degree and ml is all based on statistical systems and why data scientist are statistician?
In india 90% ppl having degree in engineering but 90% of ppl are having 15000 to 20000 jobs I want this answer
Economics , demand and supply .. supply of graduates is way higher and the demand for workforce have reduced overtime, due to incredible growth of tech industry supporting all fields with less human work while computers do the most. U have to exceptionally stand-out and show that u can provide value for the company to land big prize. Ofcourse there are other factors , if say variables effecting this issue too .. but I feel what I mentioned is major one
Looking at both of their profiles - the ML engineering guy has way more of real cred quals than the "Data Scientist" here. Her entire Ph.D. tenure was of two years(go figure) and they pubs are seriously hilarious. Go ahead and call me arrogant but there needs to be an ounce of humility that comes with your knowledge(looking at how the "data scientist" talks over here).
I am part of FAANG and I tell you - ML is still growing; lot of things are being defined at the moment. Data Scientists are hyped up data analysts. At least the real ones in fintech (east companies) are fantastic and real. They have amazing creds in terms of aptitude. Their interviews literally set and raise the bar for being data analysts. Uber Data Scientists are a joke. A very good friend of mine (Who is a DS at Uber btw) laughs every time I ask her how would she compare Uber's DS interviews with the likes in real fintech ones. Seriously.
The ML Engineer's LinkedIn profile in this video has quantifiable creds. Masters from Berkeley. Computer Science - Neat. Knows his fundamentals. Worked as an ML engineer in other comps before joining facebook. And now a Senior in FB. That's a neat trajectory. You don't do a namesake PhD and get into Data Science. I stress on this people - There's no freakin science there. You know where's real Science? - "COMPUTER SCIENCE". There's a real reason why someone has put "SCIENCE" there.
Go learn your DS, algo - be extremely thorough in these. Using the example she has referred to in the video: Point A to Point B? Freakin Travelling Salesman. I wonder what Data Scientists do over there? All these are glamorous roles which you will definitely learn as you specialize in your career role. Personal suggestion(not an advice) - STAY AWAY from Data Science as your career. It's hogwash. ML engineering - Definitely a linear trend ahead in terms of career progression. You get to work on real life impactful problems. You can measure your career success when you look back and quantify clearly what you have achieved.
And there's a difference between schooling and education. Indians at least are starting to understand the spectrum of how IITians end up in their career. There are these actual top AIR folks (IITD CS and IITM CS) - talking only about CS - And they end in up amazing academic tracks at Caltech or MITs(true to what they have started doing in their career). And you have these "Data Scientists" with a namesake Ph.D. starting their track in random metallurgy and civil and ending up in these hogwash roles because the economy is well enough to pay them money. Sheesh!
What a lot of nonsense I just read. 'Data Science' is indeed an overhyped term, just as 'machine learning' is an overhyped term. In the end it is all computational mathematics and most of it computational statistics. And as far as I am concerned these are still SCIENCES. As a PHYSICIST and STATISTICIAN I say pray on your knees that physicists invented so much, otherwise computer scientists would still be playing with their little vacuum tubes. The ego of computer scientists is SOOO BIG. It takes one great innovation from physicists to put you guys all out of business.
@@michaelpieters1844 Completely agree with you. The argument over here is about Data Science only and about the folks sitting in this video. None of these two are physicists/true statisticians. The focus isn't there ergo I didn't comment on that perspective. You have just raised an argument to take a step back and think about the bigger picture. I get it and agree. And personally in terms of ego for CS folks - I agree with you as well. But with the track record in terms of money making stats in the past two decades - you gotta give it to us man. C'mon :) Even bullshitters like the ones sitting in this video make tons. Trust me.
The guy looks like Ambi from Aparachit.
I am a simple graduate in bachelor of arts degree. Is it suitable to learn data science or AI ? If it's ok then which is suitable for me?
Bro that's up to you to decide, don't decide your path based on a stranger's opinion.
First you need to learn solid Mathematics , become master of data structure Algorithm . You should be able to read and understand complex research paper and implement it.
ML is not only about programming language . There is a reason that ML engineers are highest paid in the world bcoz they are doing highly complex work.
You must invest urself on the learning curve of years to be a ML engineer .
@@anon-tp4ns I strongly disagree there bro. You don't need to have to strong a knowledge of math or stats or data structures. You start by learning how to program and implementing stuff, getting a high level overview of different algorithms and implementing them(None of which entails reading research papers at the start), implementing these algorithms to solve different problems and doing fun projects.
Just start doing stuff and learning. Don't focus too much on the math or the stats part, things will come slowly and they will become clear with time. Just start programming and you will get there if you want to.
@@ish694 hey do you work as a machine learning engineer??
@@ish694 Bro , respect your opinion .
My point is Machine Learning , Computer science are huge field and they deserve respect . You cannot take it casually and claim to master it.
I have to read a lot of paper during my Projects in Deep Reinforcement learning , Machine learning etc to accomplish the project and course . Fir my internship I have spent 2 weeks understanding single paper .
My point is , lots of people are taking ML very casually while floating on the surface of the field . Slowly slowly there will be lot of shallowness black boxing around this field bcoz ppl did not focus on understanding nuts and bolts .
You imagine you got 50 million data points and you have to run a KNN on it , how would you do it ? You must come up with some efficient data structure Algorithm to implement it .
If you don't understand diff between bias and variance . If don't understand probablities how you would do generative models . Similarly if you don't have idea of Finite State machine how you would understand Hidden Markov model or Baysean network .
In industry ML requires great level of software engineering skills also to expose ML algis through API.
All I am saying that , don't miss on fundamental basic otherwise your knowledge will be uncoocked and it would take many years to learn simple stuff , your approach will be like black boxing . But if focus on foundation init8ally it will be tough but later on your skill will be fruitful.
ML is not just python library , 90 percent ML is conceptual intuition based on solid computational and Mathematical foundation validated of peer reviewed proofs.
Thanks for this video, Such an interesting info from these two #inspired
Nice to see modern generation of Americans speaking fluent English
What coding languages do you need for data science???
Start with python
Start with notepad
@@piyush6631 lol...,😆
assembly language
jk
start with basics of Python and get a firm grip on Numpy, Pandas, Matplotlib (libraries of Python)
aisha sartaj Python or R
so data scientist are the reasons we have DLCs ,okay thanks
Can we do ml engineer after 12th commerce?
Get a computer science undergraduate degree bro
Machine Learning Engineering is huge field .
90 percent ML engineers have very solid Software Engineering back ground . Maths , DSA , solid concept of computer science is essential . Once you have foundation then u can get in ML .
Lots of youths are misguided because ot fancy RUclips videos doing cool things. But in reality it is very complicated field which requires lots of dedication , focus .
There are hardly 10000 people in the world who can claim to be real AI engineer .
You should get a computer science degree first . It will give you foundation to achieve ML goals .
Fact - ML engineer are more than data scientist because their role is highly technical and manifests real data product .
@@anon-tp4ns Well said.Lot of superficial stuff on youtube is misguiding a lot of people.
@@chandrarama1970 I have studied Machine Learning and Deep Learning at Carnegie Mellon University and still I am learning everyday . I was software engineer in past as well. I don't think I can claim myself to be expert on ML yet.
Lots of ppl on RUclips are projecting ML as some fancy field , run some python library do some small project and you are good to become AI engineer . Youths are getting lured with the idea if big money .
In my understanding , you need a solid intellectual caliber to grow in field of AI and ML . This field is very intense and you need to learn everyday . If you are not good at analytical thinking and intuitively mathematical in nature , this field is not for you .
You would have to know programming basics and Mathematics. I don't really think you need a computer science degree,but do take a couple of online courses that teach programming.
Either you should be informal or someone should ask questions to you.
Could learn a few things about ML & DS (not ML vs DS)
What coding languages do you need for data science other than python,r??!
aisha sartaj R
You should also know SQL and bash; the rest typically vary depending on the job. If you're building applications, you will likely need Java or C. If you are working with big data, it would be good to know how to work with distributed systems using Scala. If you're doing a lot of matrix manipulation/calculation, MATLAB is great to know.
@John Bello's advice is very good.
Also, do a Google search for influential data scientists, then look up those scientists' LinkedIn profiles and the skills they have listed there. You'll get a good idea of the essentials you need to know. 😊
Notepad
This video really help me a lot
Sir how can I get training in ML and AI .
why does she keep repeating everything she said?
Great
There is a significant pay gap tho
who gets paid more? and any citation for that claim? genuinely interested
PRATIK1900 machine learning engineering is definitely more lucrative. And for good reason
Hey did she forget that they were married. @1:05
Dude wo stree hai she don't want to allounce that guys as husband on sm,......
Can mba guy become machine learning engineer?
If you have knowledge. ..can...but it hard to get a chance. ..
The field of machine learning is still evolving. Conventional managers were heavily dependent on their experience for decision-making.think of a manager who has domain knowledge of the business problem, machine learning and practical experience of ground .you can think how effective this manger can be.
Nice Couple
Machine learning and data science are the same thing...
No... They aren't...
The man talks anything but machine learning
Shes very pretty
Good video and smart people but the accent just kills me.
Hate these couples. DS/ML looks more like love story. The girl is beautiful though.
That is utter stupid. Everything is important.
No one
Fake Accent!! OMG
all these h1bs have ruined IT