Step 1. Fulfill your prerequisites
Before you begin, you need Multivariable Calculus, Linear Algebra, and Python. If your math background is up to multivariable calculus and linear algebra, you'll have enough background to understand almost all of the probability / statistics / machine learning for the job.
Python is the most important language for a data scientist to learn.
R is the second most important language for a data scientist to learn. I'm saying this as someone with a statistics background and who went through undergrad mainly only using R. While R is powerful for dedicated statistical tasks, Python is more versatile as it will connect you more to production-level work.
Step 2. Learn Probability and Statistics
Step 3. Complete IBM's Data Science professional certificate from coursera
Step 4. Do all of Kaggle's Getting Started and Playground Competitions
upto this your base is strong.
after that you can move to advance machine learning and data science courses in coursera and handle some real world problem in kaggle
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nischal nayak
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Original Message:
Sent: Mon February 03, 2020 02:05 PM
From: Raffel Lawrence
Subject: Need guidance
Hey Everyone! I hope you all are doing great.
I am absolutely new to this field and wish to have got a proper road map that is followed actually in real-time data science application. Your time and guidance will be appreciated.
Thanking you in anticipation.
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Raffel Lawrence
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