Mathematics is a broad subject, but there are specific subfields that are heavily employed in data science: calculus and linear algebra – and this is what the 365 Data Science program covers. However, in order to thrive in data science, you must have all the numerical tools so you can eventually understand the most complicated of machine learning algorithms.

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Section 1

Introduction to Linear Algebra

In this section, we will discuss the basics of linear algebra – scalars, vectors, matrices, and tensors. We will dive into the terminology and the different operations that one can perform, like transposing, addition, subtraction, multiplication, etc. We then look into types of matrices, like the identity matrix and inverse matrix. We finish off this part with Eigenvalues and EigenVectors.

FREE What is a Matrix
FREE Scalars and Vectors
FREE Linear Algebra and Geometry
FREE Scalars, Vectors, and Matrices as Python Arrays
Premium course icon What is a Tensor?
Premium course icon Addition and Subtraction
Premium course icon Errors when Adding Matrices
Premium course icon Transpose of a Matrix
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Premium course icon Dot Product
Premium course icon Dot Product of Matrices
Premium course icon Why is Linear Algebra Useful
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Data Science Fundamentals

This course is part of Module 1 of the 365 Data Science Program. The complete training consists of four modules, each building upon your knowledge from the previous one. Whereas the other three modules are designed to improve upon your technical skill set, Module 1 is designed to help you create a strong foundation for your data science career. You will understand the core principles of probability, statistics, and mathematics; you will also learn how to visualize your data.

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