Being in IT industry for more than 18 years I can confirm that Data Science is just another trend which will be over soon. Few years back same was the trend with Big Data (which now has seen saturation). I work for Fortune 500 organization but I have hardly seen openings for Data Science, AI & ML. There are not good enough use cases for most of scenarios. Even if the use case is there, the cost of building such systems is too high to invest. I recommend people to opt for Web Technologies, Cloud or ERP like SAP which will be in demand for many years to come.
Valid points , however my counter argument would be people with big data skills may not be needed to have domain expertise, lot of trial and error with what goes well with which type of Industry etc whereas data scientist need to do all these in addition to their implementation skills. My view is these additional learnings will be in decent demand. Happy to be corrected
I knew this before but you made it much clearer. I fully understand this concept now. Its like someone who wants to build a office tower. They will first contact an architect to design and give domain knowledge. After that they will contact construction people who will implement the design.
Started to study Machine Learning in 2022, finished in end 2023, started learning deep learning in 2024. Now they saying that i need to learn LLM, Gen AI, how to keep my motivation? Where is the end? Is the effort worth it? After working 10 hours per day, how can we study for 4 hours per day? Some so called pandit say 1. Maths is not important. The maths involved in ML is easy 2. Non technical people can be very good in ML 3. Chatgpt will not impact your job. All bullshit. AI has very difficult maths. SVM, PCA, XGboost has such maths , if you want to learn , will take 1 year alone. It is not worth it.
This is the same situation I am facing. Completed machine learning. but now as I was started to learn NLP I am seeing LLMs, gen AI is compulsory apart from deep learning, NLP concepts.
do u think all data scientists especially in service based companies good at maths, algorithms ? learn while you do the job. you never know which project requires which skills. For sure all DS/AI/ML jobs doesnt require everything..
@@sateeshum8342 even in service based companies, they are asking purely mathematics based questions, I know they don't know it. But they are there to reject a candidate. It is all about demand and supply. Huge number of experienced software engineering professional are upskilling to Data science, making the job market really difficult.
@@sateeshum8342 means I should apply for junior data scientist jobs on the basis of all of the knowledge I have till now and learn side by side with job? That is what I am executing but still there is a fear of not checked all of the points.
The word scientist in Data Science is associated for a reasons. Scientists are supposed to think and use their intellectual capabilities to solve a problem. Data science is a subject not a tool. It is as evergreen as operating system. Rest all are tools to facilitate our ability to solve a problem. While i agree every technology can reach a point of saturation, but at this era, data driven decision-making is almost irreversible. The key is to continuously seek optimization of your own skillset. Upgradation is an unwritten rule in the field of tech.
am a 2020 passed out mechanical engineer & i started learning data analytics last yr and now am in ML algos.. am about to start applying for jobs.. but am constantly seeing many people rants like ' they asking LLM, gen ai, deep learning etc etc' thats so demotivating..
Even if they are asking what they are asking is important. These things being new the interviewer also may be asking for just the sake of it. Nothing to get demotivated. Get a high level understanding and move on with job hunting. From your experience in job search identify areas of improvement and work on it.
Sir I am a working professional having 2.5+ yr of work ex and want to switch my carrier to Data science field. I have learnt NLP ,ML ,Stats and made some projects but i dont have relevant work ex related to DS in my resume so not getting any calls . What should I do please suggest.
So if anyone planning to start learning new skills..what all are the main skills we should focus on..btw really nice video as always you do..really appreciate the way you explain things
Six Sigma is all about using data to solve problems, which is a core aspect of data science. Highlight your experience in using data to make decisions and optimize processes. In addition, core data skills are must to learn
Hi Aman , I just want to know how does a ML model handle data once its deployed in production.? Like when we build a model we scale the data , remove nulls ,transform it and then use it , but how does all this happen in already deployed models? Because a normal day to day life will have all the uncleaned data. Pleas help , I m really confused. I can build the ml , dl , transformers etc but am confused how is data preprocessing tackled after model is deployed . Basically how is all preprocessing captured in the model to be used after deployment , is it through columntransformers and pipelines or are there any other steps or is it under mlops umbrella ?
Good question. I am glad you are thinking this. There will be automated pipelines to take care of everything right from data import to clean to transform and finally produce predictions. To keep it short, whatever processing done while training model. All those need to happen on new incoming data but in an automated way
Being in IT industry for more than 18 years I can confirm that Data Science is just another trend which will be over soon. Few years back same was the trend with Big Data (which now has seen saturation). I work for Fortune 500 organization but I have hardly seen openings for Data Science, AI & ML. There are not good enough use cases for most of scenarios. Even if the use case is there, the cost of building such systems is too high to invest. I recommend people to opt for Web Technologies, Cloud or ERP like SAP which will be in demand for many years to come.
Data science is not trend it's evergreen field, the trend is AI
Valid points , however my counter argument would be people with big data skills may not be needed to have domain expertise, lot of trial and error with what goes well with which type of Industry etc whereas data scientist need to do all these in addition to their implementation skills. My view is these additional learnings will be in decent demand. Happy to be corrected
Sabse best govermanent job
@@tejas4054 they pay like peanuts
I knew this before but you made it much clearer. I fully understand this concept now.
Its like someone who wants to build a office tower. They will first contact an architect to design and give domain knowledge. After that they will contact construction people who will implement the design.
Started to study Machine Learning in 2022, finished in end 2023, started learning deep learning in 2024. Now they saying that i need to learn LLM, Gen AI, how to keep my motivation? Where is the end? Is the effort worth it? After working 10 hours per day, how can we study for 4 hours per day?
Some so called pandit say
1. Maths is not important. The maths involved in ML is easy
2. Non technical people can be very good in ML
3. Chatgpt will not impact your job.
All bullshit. AI has very difficult maths. SVM, PCA, XGboost has such maths , if you want to learn , will take 1 year alone.
It is not worth it.
This is the same situation I am facing. Completed machine learning. but now as I was started to learn NLP I am seeing LLMs, gen AI is compulsory apart from deep learning, NLP concepts.
do u think all data scientists especially in service based companies good at maths, algorithms ?
learn while you do the job.
you never know which project requires which skills.
For sure all DS/AI/ML jobs doesnt require everything..
@@sateeshum8342 even in service based companies, they are asking purely mathematics based questions, I know they don't know it. But they are there to reject a candidate. It is all about demand and supply. Huge number of experienced software engineering professional are upskilling to Data science, making the job market really difficult.
@@sateeshum8342 means I should apply for junior data scientist jobs on the basis of all of the knowledge I have till now and learn side by side with job?
That is what I am executing but still there is a fear of not checked all of the points.
We are in a rat race
The word scientist in Data Science is associated for a reasons. Scientists are supposed to think and use their intellectual capabilities to solve a problem. Data science is a subject not a tool. It is as evergreen as operating system. Rest all are tools to facilitate our ability to solve a problem. While i agree every technology can reach a point of saturation, but at this era, data driven decision-making is almost irreversible. The key is to continuously seek optimization of your own skillset. Upgradation is an unwritten rule in the field of tech.
am a 2020 passed out mechanical engineer & i started learning data analytics last yr and now am in ML algos.. am about to start applying for jobs.. but am constantly seeing many people rants like ' they asking LLM, gen ai, deep learning etc etc' thats so demotivating..
Even if they are asking what they are asking is important. These things being new the interviewer also may be asking for just the sake of it. Nothing to get demotivated. Get a high level understanding and move on with job hunting. From your experience in job search identify areas of improvement and work on it.
@@UnfoldDataScience absolutely true
Use analytics in mechanical engineering. People are just selling DS courses and making money. I have been in the field since 10+ years
Your videos are very informative, thank you for sharing. Can you please share a video on future of Business Analytics
Sir I am a working professional having 2.5+ yr of work ex and want to switch my carrier to Data science field. I have learnt NLP ,ML ,Stats and made some projects but i dont have relevant work ex related to DS in my resume so not getting any calls . What should I do please suggest.
So if anyone planning to start learning new skills..what all are the main skills we should focus on..btw really nice video as always you do..really appreciate the way you explain things
I will create a separate video for new comers based on current market situation
In future data science has scope 👍
I m also of same opinion mostly :)
Very valid points ..Thank you
Thanks Sathish
As the newbie in DS, but with 10 years of non technical exp. how to get into DS? Have a good knowledge in six sigma though
Six Sigma is all about using data to solve problems, which is a core aspect of data science. Highlight your experience in using data to make decisions and optimize processes. In addition, core data skills are must to learn
Hi Aman ,
I just want to know how does a ML model handle data once its deployed in production.? Like when we build a model we scale the data , remove nulls ,transform it and then use it , but how does all this happen in already deployed models? Because a normal day to day life will have all the uncleaned data. Pleas help , I m really confused. I can build the ml , dl , transformers etc but am confused how is data preprocessing tackled after model is deployed .
Basically how is all preprocessing captured in the model to be used after deployment , is it through columntransformers and pipelines or are there any other steps or is it under mlops umbrella ?
Good question. I am glad you are thinking this. There will be automated pipelines to take care of everything right from data import to clean to transform and finally produce predictions. To keep it short, whatever processing done while training model. All those need to happen on new incoming data but in an automated way
@unfoldDataScience do you have a code as a sample to show the automatic data preprocessing? GitHub code or anywhere I can view. I’m curious.
Thanks for the info😎
Any time!
Data science is research field
Part of it. But not everything
Is it worth to leran data analytics now ?
Yes - why not
Data engineering?
Yes but not tool depending
Create a youtube channel, alternate source of income
useless field
I will disagree:)
@@UnfoldDataScience good done