"Best way to learn Data Science is by getting into a project within your current company".
In your career, You must have already played with large and small dataset, identified tasks which needs to be tested, checked the internal process or work flow, wrote manual scripts to do a dry test, then automated those scripts either in Selenium or Python or some other tools, and then did unit test and UAT, and then worked with deployment team to move that codes into production. Guess what, in data science, you will do exactly similar thing. Only difference could be, you will spend more time with the large and small data and identifying potential business insights based on the data. Rest part of the code to production will more or less remain the same.
Here is a simple guide to begin your journey in Data Science career. This list was prepared after lot of research, we studied how other engineer, economists, BI developers, QA, and even Physics moved to data science, we picked courses that they picked and so on. We hope this article will provide some guidance.
You might be wondering why do we need to learn all of it.
Linear Algebra is used to solve simultaneous equation, like in our school days. Exactly same, no change.
Mathematics for Machine Learning is to represent data in the form of Vectors/Matrices, because computers only understand data that way, use Probability and Statistics to build a machine learning model, Calculus is used to optimize the solution. If you need the underlying Mathematics for Machine learning details, you can view it here https://www.youtube.com/watch?v=vLJcduC4lBM&list=PLcQCwsZDEzFmlSc6levE3UV9rZ8yY-D_7
If you do not wish to study separate course, and instead follow an end to end guide.
You can take this course from DataCamp: Data Science with Python.
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We wish you all the very best.
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