A step-by-step guide to becoming a data scientist without spending any money
In this article, we’ll go through all of the errors you shouldn’t make along the road so you don’t do them again. Let’s go right onto the road map without wasting any time.
1. Learn Python
Some individuals recommend that you master the mathematics needed for Data Science. However, one needs to first master a programming language (such as Python or R).
However, simply taking this will not help you learn Python very quickly; you must practise it. HackerRank is a website that ranks hackers. It solves the issues in it. That’s it, you’ve completed your Python training.
2. Learn Mathematics
There are a few topics that you must master. Understand that “mathematics hurts” for certain individuals. But don’t worry; try to absorb as much information as possible. But it’s not as difficult as we imagine. Statistics, Probability, and Linear Algebra and Calculus are the most common subjects.
Here are the courses we prefer:
Probability and Statistics: To p or not to p? is a course on coursera on probability and statistics. This course is fee-based, but only if you require a certificate. You couldn’t afford the price, so we audited the course (you didn’t have to pay anything).
Linear Algebra: Full College Course on Linear Algebra — YouTube
Calculus: Coursera’s Introduction to Calculus. This is an incredible course. It almost teaches you all you need to know. Then you should enroll in this course.
Data Science Fundamentals
3. Python libraries for data science
NumPy, Pandas, and other Python packages for data research are available…. We must also become acquainted with them.
4. Tools for Data Science
SQL: SQL Tutorial — Beginner’s Database Course
MongoDB: A Beginner’s Guide to MongoDB | Complete Course
5. Machine learning
ML is a concept that does not require any explanation. Each year, significant advances are made in machine learning, which is one of the most important components of data science and the trendiest study topic among scholars.
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