ML engineer is an AI engineer, but I prefer the former term! Data scientist is too vague of a role and it still hasn't quite found its place in the corporate world...if you have a way with math, advanced statistics & computer science then take the AI researcher route...unfortunately neither of these two options (ML engineer, AI researcher) are meant for a fresher! the only two viable entry level roles I know of in the data space are the data analyst and the BI consultant! these are solid choices with a fair share of technicality that would set you up for a career in AI or BI! that of course if you love working with data and have no problem messing with SQL code every day for the rest of your life! for Dev route I'm probably not the guy to speak on it accurately but I know you can't go wrong with such choice, it's more difficult than becoming a Data Analyst...but if you enjoy programming & building web apps then you should go for it...I've even had some friends who transitioned from Dev to a Data engineering role but they were so procifient in SQL.
@@datasensei21 as a fresher who has more job opening more package and future growth full stack developer vs data analyst vs data scientist because a wise man said to me in tech field be a builder not a user so please tell what field
Very usefully video... However, I just wanted to know the best resource to learn the ETL tool Informatica that has a clear understanding and implementation between SQL and Informatica.. Any suggestion on this???
Implement your own project regarding a topic that interests you! Create and model OLTP databases and OLAP warehouse inside of informatica cloud! Then use ETL to migrate data from point A to point B. The place to learn is Informatica itself, there u can make use of the documentation to assist you throughout your project! Here's a link to a post that helps you set up your free trial account to get started thinketl.com/free-iics-tutorial-registration-guide/ Good luck and happy learning!
Both are good! Windows has Power BI and the microsoft tech stack such as SQL Server SSIS SSAS... there are work arounds to run windows on mac but if you are more invested in Microsoft's tech stack then go for a windows machine! On the other hand Mac offers a great compatibility with a lot data science tools...if you're considering to make the switch later on! Final words, having worked on both systems I do prefer working on windows atm cu i can seamlessly switch between tools without having to worry abt compatibility or anything I only like Mac because it shares a lot of similarities with linux command-wise!
yes it is! currently it's the number one language for that but there are GUI tools like Dataiku who offer advanced features to build and train ML models
SQL was my favourite during my degree .i loved it so much and now i am on journey to master data analytics.
That's great! best of luck 👍it's the one skill you'll see everywhere when u work with data
That was a surprisingly damn goood Video! Thank you!
Thanks man! appreciate your support 🙏
Thanks for sharing 😊
Thanks for watching!
Great video! Thanks for reminding me of the significance of sleep too!
Thank you! Yeah...I'd say it's the most important thing out of all what's said in the video 😂
Great video!!! Loved it.
Could you please make one for data scientists as well? 🙃
@@talking444 Thank you for the support 🙏 I definitely will in the near future
Wow, what an amazing video! Thank you for the useful information. Great job ba said
Thanks man!
what a video 👏👏👏
Thank you 🙏 means a lot
As a fresher what to choose full stack developer vs data scientist vs data analyst vs ai engineer vs ml engineer
ML engineer is an AI engineer, but I prefer the former term! Data scientist is too vague of a role and it still hasn't quite found its place in the corporate world...if you have a way with math, advanced statistics & computer science then take the AI researcher route...unfortunately neither of these two options (ML engineer, AI researcher) are meant for a fresher! the only two viable entry level roles I know of in the data space are the data analyst and the BI consultant! these are solid choices with a fair share of technicality that would set you up for a career in AI or BI! that of course if you love working with data and have no problem messing with SQL code every day for the rest of your life! for Dev route I'm probably not the guy to speak on it accurately but I know you can't go wrong with such choice, it's more difficult than becoming a Data Analyst...but if you enjoy programming & building web apps then you should go for it...I've even had some friends who transitioned from Dev to a Data engineering role but they were so procifient in SQL.
@@datasensei21 as a fresher who has more job opening more package and future growth full stack developer vs data analyst vs data scientist because a wise man said to me in tech field be a builder not a user so please tell what field
@@mayankpatni5639 Then aim to become a machine learning engineer.
Very usefully video... However, I just wanted to know the best resource to learn the ETL tool Informatica that has a clear understanding and implementation between SQL and Informatica.. Any suggestion on this???
Implement your own project regarding a topic that interests you! Create and model OLTP databases and OLAP warehouse inside of informatica cloud! Then use ETL to migrate data from point A to point B. The place to learn is Informatica itself, there u can make use of the documentation to assist you throughout your project! Here's a link to a post that helps you set up your free trial account to get started
thinketl.com/free-iics-tutorial-registration-guide/
Good luck and happy learning!
Hello, very nice video, I would like to ask you, does macbook and macOS a good and suitable for data analyst, or windows is better?
Both are good! Windows has Power BI and the microsoft tech stack such as SQL Server SSIS SSAS... there are work arounds to run windows on mac but if you are more invested in Microsoft's tech stack then go for a windows machine! On the other hand Mac offers a great compatibility with a lot data science tools...if you're considering to make the switch later on!
Final words, having worked on both systems I do prefer working on windows atm cu i can seamlessly switch between tools without having to worry abt compatibility or anything I only like Mac because it shares a lot of similarities with linux command-wise!
But I want to do something in ML. Isn't python important for that?
yes it is! currently it's the number one language for that but there are GUI tools like Dataiku who offer advanced features to build and train ML models
Hi ! Thanks a lot for all the useful information 🫶🏼
My pleasure! thank you for your feedback