Essential Math for Data Science | Complete Code

Improve your skills by learning math for data science and machine learning using code and visualizations.

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The great libraries in the data science and machine learning ecosystem allow you to dive into the field without knowing much about the theory. I think that this top-down approach is a great way to start: take real data and run some algorithms.

Then, a lot of questions arise like: "Why don't I get the expected prediction performance on this dataset?", "How can I adapt this algorithm to my specific use case?" At this point, the lack of theory can be a limit to your skill growth. The solution is to dive a bit more into the theory and sharpen your understanding.

In this book, I'll introduce you to math concepts specifically targeted at increasing your understanding in data science and machine learning. I can assure you that even a preliminary exposition to math thinking will clear your vision of the field.

Because the audience of this book is people without a deep math background (e.g. junior data scientists, developers in a career move to data science), the approach is: no-jargon and more insights. You'll just need high school math notions (even rusted).

A preliminary exposition to Python (ideally with the library Numpy) is something that will allow you to make the most of this book. The level of programming skill needed is a bit higher in the hands-on projects at the end of each chapter.