Very understandable approach to explaining the differences in these roles. It's definitely appreciated, especially with the blurred lines that companies often use to represent these fields.
Most of these companies don't even know the difference between Computer Science, Software Engineering and Information technology . They think it's a same thing.
I work as a Data Analyst / Engineer / Scientist as is the case with most people working in smaller companies. I don't excel at any of the three areas except maybe engineering because I'm the only one who does it on my team, not because I'm actually good at it. For every insight I find, someone else can find 5 more, for every model I build, someone else will always build a model with better performance. It drives me crazy but it is what it is I suppose. Another thing about working in smaller companies is that solutions are usually frowned upon because of the extra costs. It's annoying to have to write everything from scratch from pipelines to dashboards, but it's also a good learning experience.
is a bachelors in business data analytics worth it ( the program has machine learning business finance and a programming course aswell )or is something general like Econ or Computers better ? I wanna do a masters afterwards most probably in Computer science or IT
@@aena5995 Sounds like a good choice. I don't recommend going into pure Data Science courses simply because of how vast the field is. Having a narrower focus helps you carve out your own niche, and if business / finance is the area of Data Science you're personally interested in, go for it
Same here. Though I did work for a company that had a separate team of SQL programmers for preparing our data & another team that performed QA. By the time I got the data it was ready to plug into R or SAS.
Hello, Mr. Daniel! I am 17 years old boy with no tech background and bad math who wants to work as a data scientist in the near future. If it is no problem, could you give me some advices how I can start?
Thank you so much for giving us not only the differences, but also how the market uses the terms. You made everything very clear for me now. Congrats on the video!
Thanks for sharing such a valuable information on Google Software Engineers and their multifaceted roles as Data Scientist, Research Scientist, ML Engineers, Data Analyst. Brilliant explanation.
This video was incredibly helpful in clarifying job titles within the job market, and I no longer feel as though I'm struggling. Great explanation! Thanks !!
No real life difference, bro. Managers barely understand the difference between a programmer and a bartender, so they'll throw you into whatever tech role as long as you can turn a pc on.
Perfect Video explanation // having worked several Oracle Federal Projects as a Software Engineer ....i am moving on to the Next BIG Thing : "DATA Scientist" ...seems its all about Forecasting & Predictive Analytics //
Really nice video. I am a current data analyst at a large tech company, hoping to become a ML engineer eventually (similar to a google software engineer whose capable of everything in that chart). Love the content, keep it up.
No. If you want to become a data scientist, let others do the data cleansing and IT diagnosis things. I was a victim of this - ex company paid me only one person's salary, while I did MLOps job + i18n + PPT making. It's not worth it unless they pay you enough.
@@hanchengyu3119 not only that but "capable of everything" is not a good thing. Someone who is specialized will always perform better. Much better to make well functioning teams than thin out the skills of everyone and get sub par results for everything.
@@thefourthbrotherkaramazov245 is a bachelors in business data analytics worth it ( the program has machine learning business finance and a programming course aswell )or is something general like Econ or Computers better ? I wanna do a masters afterwards most probably in Computer science or IT
Companies nowadays prefer a generalist over a specialist who is super expert at one specific field only. This is why developers experience burn outs so often, they have almost no option but to spend a great portion of their time trying out new tech, and whatever they learn will most likely be replaced by "better" resources.
Most data professionals burn out even before they reach 30. But the sad part is they don't even get paid as well as quants or other similar mathematical professions do
As a software engineer I've been doing all this for 20 years, before the "analyst" position became normalize. My job title on one job was Programmer Data Analyst
subs.. I've been seeing your video on my feed for a while now since I started roaming around to study software development but my true goal was to go up to the data scientist role, that's just the goal I have in mind and you just revealed to me that I don't exactly NEED to become a software engineer first if I can land myself to a data analyst role, the hierarchy showed me I can still walk up slowly to reaching my goal.. you are a god send.. :)
Data Science/Machine Learning/Data Engineering/Data Analyst roles are incredibly overhyped and actually are not very rewarding. The competition is insane and getting a job takes almost 18 months of grinding to break into the industry. Plus you need a masters in Data Science to actually be considered for most roles. But even despite that, you are not considered for most jobs if you don't have work experience. To add on top of that, there are insanely long hours and crazy work pressure, but relatively low pay in these fields,, You must choose your field smartly for the future - Java and Cybersecurity roles along with, network engineer, VLSI, and networking roles are always hiring. So are, product management, Salesforce, and PPM type of roles. Core fields are great but they just aren't that much in demand. Always choose IT or CS or Electrical/Networking and learn to code in Java or Javascript. You are pretty much guaranteed to get a high-paying job.
I am a marketing project manager and i was wondering if i could shift my path to data scientist/analyst, and your amazing video made it clear for me, thanks very much
Data Science/Machine Learning/Data Engineering/Data Analyst roles are incredibly overhyped and actually are not very rewarding. The competition is insane and getting a job takes almost 18 months of grinding to break into the industry. Plus you need a masters in Data Science to actually be considered for most roles. But even despite that, you are not considered for most jobs if you don't have work experience. To add on top of that, there are insanely long hours and crazy work pressure, but relatively low pay in these fields,, You must choose your field smartly for the future - Java and Cybersecurity roles along with, network engineer, VLSI, and networking roles are always hiring. So are, product management, Salesforce, and PPM type of roles. Core fields are great but they just aren't that much in demand. Always choose IT or CS or Electrical/Networking and learn to code in Java or Javascript. You are pretty much guaranteed to get a high-paying job.
@@mohit4902i was thinking of going to the data science route since my field is research but seeing your ans make me doubtful 😅😅 either way i love computers.
A question, i wanted to study data sicentist in a bootcamp, it takes arround a year. Is this te best option? i would able me to work as a scientist or an analyst if it is the requeriment? And in terms of salary and future demand, is a good option? Or should i study another thing
hey fam! that was a great video!! i am bachelors student pursuing psychology, suddenly i have this urge to become a data analyst but i have no tech background! only thing to my support are the certification programs available online! i want to ask you that is this field for me and will i be able to compete with people who have dedicated degree's in this field if i rely only on certificates from various sources and work my way up? will the companies that i apply to select me over a person who has a degree just on the basis that i have better skill?
I basically have the same question because I am a post graduate from a commerce background (yes, absolutely non-technical) with knowledge of MS Excel, who just recently started her Data Science online course and is watching a bunch of youtube videos already!
Where does the role of Chatgpt fit in this chart? I read that entry level data analyst tasks can now be done in seconds by Chatgpt. Please elaborate on this.
So as someone who eventually one day wants to work in data science industry, is there an entry level job role that one can apply as a starting point and just get promoted/ level up to a data science in time rather than just trying to apply for a data science position from the get go? I dont mind starting at the bottom and working my way up the ladder.
I am curious as well I recently started using tableau to sort out datasheets to train myself for a data analyst and although im learning this video shows i still have a long way to go
Data Analyst is my title but Data Scientist is my work. But also, they want me to do software engineer stuff and I said "No, you need software engineers" 😂 as they were asking me if I can replicate a 3rd party software.
What I learned working at multiple companies is this, MOST COMPANIES DONT KNOW JACK SHIZNIT ABOUT DATA. Aside from finance & insurance companies, most places don’t have the discipline or stomach for investing in a true data first company. And that especially includes SW companies.
didn't expect joma to be this informative
I wanna laugh😂
Incroyable.
@@fatimaWr2 hahahaha
🤣
Good to know he can do It too. I was waiting the part to laugh, but surprise about how really usefull this information was for me.
25 hours a day are possible on days where you return from daylight saving time to standard time.
Uh-huh
Or if an individual moves from one timezone to another.
Or if you're a daywalker
Or just write a flipping program for a virtual 25 hours day.
Jeeezzzz
Possible But highly Unlikely.
Very understandable approach to explaining the differences in these roles. It's definitely appreciated, especially with the blurred lines that companies often use to represent these fields.
Most of these companies don't even know the difference between Computer Science, Software Engineering and Information technology . They think it's a same thing.
Mm.. but it kind of is. And tbh I think that there shouldn't really be a distinction made across the three, each should know a bit of the others.
great video! thanks for clearing that up and explaining the various elements of so many different roles in this field! very helpful.
Yep, I can confirm as a PhD research scientist that I do in fact have a side kick ML engineer (actually 3) to help me create all my crazy shit.
I work as a Data Analyst / Engineer / Scientist as is the case with most people working in smaller companies. I don't excel at any of the three areas except maybe engineering because I'm the only one who does it on my team, not because I'm actually good at it. For every insight I find, someone else can find 5 more, for every model I build, someone else will always build a model with better performance. It drives me crazy but it is what it is I suppose.
Another thing about working in smaller companies is that solutions are usually frowned upon because of the extra costs. It's annoying to have to write everything from scratch from pipelines to dashboards, but it's also a good learning experience.
is a bachelors in business data analytics worth it ( the program has machine learning business finance and a programming course aswell )or is something general like Econ or Computers better ? I wanna do a masters afterwards most probably in Computer science or IT
@@aena5995 Sounds like a good choice. I don't recommend going into pure Data Science courses simply because of how vast the field is. Having a narrower focus helps you carve out your own niche, and if business / finance is the area of Data Science you're personally interested in, go for it
Same here. Though I did work for a company that had a separate team of SQL programmers for preparing our data & another team that performed QA. By the time I got the data it was ready to plug into R or SAS.
thanks for sharing your experience. thats why i love the comments to a topic i m interested
Hello, Mr. Daniel! I am 17 years old boy with no tech background and bad math who wants to work as a data scientist in the near future. If it is no problem, could you give me some advices how I can start?
Thank you so much for giving us not only the differences, but also how the market uses the terms. You made everything very clear for me now. Congrats on the video!
That's a very nice explanation, I'd love to see each step in-depth
Thanks a lot, I'm working as Data Analyst and this content made it clear for me!
Your videos are a HUGE inspiration!! just started out my own youtube (from my experience as a data analyst) All the best!
Excellent video! I have been looking for this explanation. This also helps me to know where to start learning in terms of data science. Thanks!
Thanks for sharing such a valuable information on Google Software Engineers and their multifaceted roles as Data Scientist, Research Scientist, ML Engineers, Data Analyst. Brilliant explanation.
This is a really great video to clarify the various data needs companies usually have, and where each role comes in! Thanks a ton!
This video was incredibly helpful in clarifying job titles within the job market, and I no longer feel as though I'm struggling. Great explanation! Thanks !!
Jesus is your answer to every struggle!! Amen.
@@isaaclovesJesus bruh
Great video, now I can finally understand and compare different job listings more effectively
Thank you! It's so clear now! You're awesome!
I have seen many videos for the last few months within this topic, but THIS video is the most comprehensive for now.
Excellent video! The best video on this comparison
I've watched
No real life difference, bro. Managers barely understand the difference between a programmer and a bartender, so they'll throw you into whatever tech role as long as you can turn a pc on.
I like your formal videos much more than fun ones. Thank you.
Awesome video exactly what I needed! Thank you!
Thanks! Cleared the difference really well!
very valuable that it helps me to identify what project and skills in what order I have to learn first.
Thank you!! This video helped me a lot about this subject.
Perfect Video explanation // having worked several Oracle Federal Projects as a Software Engineer ....i am moving on to the Next BIG Thing : "DATA Scientist" ...seems its all about Forecasting & Predictive Analytics //
Really nice video. I am a current data analyst at a large tech company, hoping to become a ML engineer eventually (similar to a google software engineer whose capable of everything in that chart). Love the content, keep it up.
Is the salary good ?
Is the salary good ?
No. If you want to become a data scientist, let others do the data cleansing and IT diagnosis things. I was a victim of this - ex company paid me only one person's salary, while I did MLOps job + i18n + PPT making. It's not worth it unless they pay you enough.
@@hanchengyu3119 not only that but "capable of everything" is not a good thing. Someone who is specialized will always perform better. Much better to make well functioning teams than thin out the skills of everyone and get sub par results for everything.
@@thefourthbrotherkaramazov245
is a bachelors in business data analytics worth it ( the program has machine learning business finance and a programming course aswell )or is something general like Econ or Computers better ? I wanna do a masters afterwards most probably in Computer science or IT
This was very helpful information; thank you!
This was such a helpful video man you are the GOAT
Companies nowadays prefer a generalist over a specialist who is super expert at one specific field only. This is why developers experience burn outs so often, they have almost no option but to spend a great portion of their time trying out new tech, and whatever they learn will most likely be replaced by "better" resources.
you're right, "unicorn" Data scientist is a thing now.
Most data professionals burn out even before they reach 30. But the sad part is they don't even get paid as well as quants or other similar mathematical professions do
What tip do you have for those who don't want to go through that and are finishing their graduation in Computer Science? please answer me
A very good explanation on how to clarify those roles!
As a software engineer I've been doing all this for 20 years, before the "analyst" position became normalize. My job title on one job was Programmer Data Analyst
subs.. I've been seeing your video on my feed for a while now since I started roaming around to study software development but my true goal was to go up to the data scientist role, that's just the goal I have in mind and you just revealed to me that I don't exactly NEED to become a software engineer first if I can land myself to a data analyst role, the hierarchy showed me I can still walk up slowly to reaching my goal.. you are a god send.. :)
Data Science/Machine Learning/Data Engineering/Data Analyst roles are incredibly overhyped and actually are not very rewarding. The competition is insane and getting a job takes almost 18 months of grinding to break into the industry. Plus you need a masters in Data Science to actually be considered for most roles. But even despite that, you are not considered for most jobs if you don't have work experience. To add on top of that, there are insanely long hours and crazy work pressure, but relatively low pay in these fields,,
You must choose your field smartly for the future -
Java and Cybersecurity roles along with, network engineer, VLSI, and networking roles are always hiring. So are, product management, Salesforce, and PPM type of roles. Core fields are great but they just aren't that much in demand. Always choose IT or CS or Electrical/Networking and learn to code in Java or Javascript. You are pretty much guaranteed to get a high-paying job.
I know zero about tech yet I understood everything you explained. Bravo
Thank you for your advise dataiku
Brilliant explanation! THanks
Thanks for the vid, very instructive
The Data Science Herarchy of Needs. Excelente explicación de las diferentes necesidades y que hace cada rol en estas necesidades.
His sense of humor is legendary, especially on the data logging part.
This is very informatif, Dude🤩
Simple, concise and very good explained👍
I am a marketing project manager and i was wondering if i could shift my path to data scientist/analyst, and your amazing video made it clear for me, thanks very much
Data Science/Machine Learning/Data Engineering/Data Analyst roles are incredibly overhyped and actually are not very rewarding. The competition is insane and getting a job takes almost 18 months of grinding to break into the industry. Plus you need a masters in Data Science to actually be considered for most roles. But even despite that, you are not considered for most jobs if you don't have work experience. To add on top of that, there are insanely long hours and crazy work pressure, but relatively low pay in these fields,,
You must choose your field smartly for the future -
Java and Cybersecurity roles along with, network engineer, VLSI, and networking roles are always hiring. So are, product management, Salesforce, and PPM type of roles. Core fields are great but they just aren't that much in demand. Always choose IT or CS or Electrical/Networking and learn to code in Java or Javascript. You are pretty much guaranteed to get a high-paying job.
@@mohit4902 would CIS be in this category?
@@mohit4902i was thinking of going to the data science route since my field is research but seeing your ans make me doubtful 😅😅 either way i love computers.
what a great explanation! thank a lot
Thank you very much for this video!
Thanks for giving all this information.
The lines very easily get blurred! Thank you for the video!
the best video ever! on this topic.
This helped a lot, thank you
Great visualization and explaination
Great video. Thank you!
Right on point , Thx.
My fav TH-camr is back!
Thanks for sharing. Easy to understand.
thanks a lot for this great axplanation 🎉
Very easy to understand
Informative 👍🏻
Awesome! thank you
Amazing breakdown
well explained , thank you :)
that smooth music at the end really hit me
holy moly need more vids like this
Jesus loves you!
such a very approachable video!
Fantastic video thanks learnt soo much
Very good summarise info and helpful.
AMAZING VIDEO thanx man !!!!!!!
Awesome explanation, even my business analysts brain understood the difference :)
I'm studying UX and I just don't know whats going on in the engineer/developer/data world at all! Thanks for the easy to understand explainations
Excellent explanation
Thanks for the vid
Not really, sometimes discription is just for illustration, the company will ask you to do more or sometimes not related to your scope
Very insightful and placed in layman's terms. You have a new subscriber!
Tremendously helpful! Thanks!
This was very informative
A question, i wanted to study data sicentist in a bootcamp, it takes arround a year. Is this te best option? i would able me to work as a scientist or an analyst if it is the requeriment?
And in terms of salary and future demand, is a good option? Or should i study another thing
I need to share this video. You have great content, specially for beginners like me. Thanks
Sorry for my English
Great explanation
Very helpful. Thanks
hey fam! that was a great video!!
i am bachelors student pursuing psychology, suddenly i have this urge to become a data analyst but i have no tech background! only thing to my support are the certification programs available online! i want to ask you that is this field for me and will i be able to compete with people who have dedicated degree's in this field if i rely only on certificates from various sources and work my way up? will the companies that i apply to select me over a person who has a degree just on the basis that i have better skill?
I basically have the same question because I am a post graduate from a commerce background (yes, absolutely non-technical) with knowledge of MS Excel, who just recently started her Data Science online course and is watching a bunch of youtube videos already!
Good vide Joma :)
Very clear!!
Wow ty for explaining this
Where does the role of Chatgpt fit in this chart? I read that entry level data analyst tasks can now be done in seconds by Chatgpt. Please elaborate on this.
Thank you Stephen Chow
awesome!!
The only thing I care about when choosing between Data Scientist, Data Analyst & data Engineer is the amount of money i will get doing this job
Salary structure is like this:
Data engineer > data scientist > data analyst
@@har_d_rocks9987 Strange, I thought Data scientist would be on top
you get shitty conclusion from shitty data. Thats why data engineer more important
I slept for 48 hours a day and realised it was a dream 😢😢 anything is possible when you are a software engineer, Goodluck guys.
i didn't know joma had a second channel
such a great video.
What the difference between JomaTech and Recall by Dataiku?
So as someone who eventually one day wants to work in data science industry, is there an entry level job role that one can apply as a starting point and just get promoted/ level up to a data science in time rather than just trying to apply for a data science position from the get go? I dont mind starting at the bottom and working my way up the ladder.
I am curious as well I recently started using tableau to sort out datasheets to train myself for a data analyst and although im learning this video shows i still have a long way to go
Than you man
really cool vid' ; thanks a lot
Data Analyst is my title but Data Scientist is my work. But also, they want me to do software engineer stuff and I said "No, you need software engineers" 😂 as they were asking me if I can replicate a 3rd party software.
“Make sure you look at the description of the job you’re applying for” 😳
reading is important where ever you go
amazing video
Joma: ... and see which position is best for...-
Me : I WANT IT ALL!
Thanks!
very well explained
Thank you
What I learned working at multiple companies is this, MOST COMPANIES DONT KNOW JACK SHIZNIT ABOUT DATA. Aside from finance & insurance companies, most places don’t have the discipline or stomach for investing in a true data first company. And that especially includes SW companies.