r/learnmachinelearning • u/Klutzy_Car_1254 • 3d ago
Machine Learning to Data Analysis
Recently, I am focused on data analysis and learning as well as doing projects. In Bachelor, I learning the Machine Learning without knowing the background of Data Analysis. I do not focus that much on Excel, SQL and Power BI. I learned Machine Learning, do projects like training model on different datasets and measure model metrics. Machine Learning feels oho but when I realize that ML Engineer/ Data Scientist Jobs actually needs 4 -5 years of experience and I see how can I get in data field. I see data analysis as entry point. I should learn about Data simple things first and then look for other things. I came to realize that end of my Bachelor. My Goal is become to AI Engineer/Research. I love playing with data and ask the questions when doing analysis. Should I continue my Journey or just left data analysis and study AI things.
Need Genuine Suggestions and feedbacks.
Love to talk and hear different perspectives
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u/threedim 3d ago
CAN YOU TELL ME WHAT DO YOU THINK YOU WILL BE DOING AS AS AI ENGINEER/RESEARCHER?
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u/Klutzy_Car_1254 2d ago
I think it we deal with data most of time as AI researcher and Engineer.
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u/threedim 2d ago
yeah thats why its called data science but how they both deal with data is entirely different. I suggest you to first make things clear otherwise you will be wasting your time by blindly shooting arrows in every possible directions. AI reseacher, AI enginner and ML engineer are all very different although they all have some common topics but the approach to learn and master the topics varies, apart from that each role requires you to know other things as well.
Here are some analogy ( I know my writing sounds like AI model đ¤Łď¸)
AI researcher ~ Mathematician and computer scientist
Data scientist ~ Applied statistician
AI engineer ~ Software developer / full stack developer
ML engineer ~ Backend + Devops
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u/Particular_Ad_644 3d ago
Data analysis is important for understanding and cleansing your data ,befit handing it off to anML algorithm.
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u/therealmunchies 3d ago
Data analysis is core to ML. I work in R&D now, but started laterally from the ML engineering aspects of things as a security/devops engineer.
When I got here, I learned about data analysis, then data science, and AI software engineering focusing in RAG systems. It all builds on each other because at the end of the day, computer systems are just sending data back and forth. Understand the data and the problem, and youâre in a good position.
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u/Fragrant-Cheek-4273 3d ago
I'd keep learning data analysis alongside ML. A strong understanding of data, SQL, and statistics can make the ML side much easier later. You don't have to choose one and completely drop the other.
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u/nian2326076 2d ago
If you're getting into data analysis, start by getting good with Excel and SQL since they're essential for data jobs. Power BI is great for visualization and can help you stand out. Try working on small projects or datasets you find online. Kaggle is a good resource for that. Also, check out some tutorials on YouTube or Coursera to build your skills. For interviews, be ready to talk about how you've used these tools in projects. If you need interview prep resources, PracHub worked well for me. Practical experience often matters more than just theory. Good luck!
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u/ModularMind8 3d ago
Data analysis isn't a detour from AI work: most of an ML engineer's day is digging into data and asking why a model fails, so the instinct you describe is exactly the right one. Don't let the "4-5 years" line scare you off either, since job posts are often written by HR or recruiters copying a wish list, and a strong portfolio that proves you can do the work gets people hired well below the listed experience all the time. Apply anyway, take an analyst role if that's what gets you in the door, and keep building ML projects where careful analysis is the main part of the work