Already know most of it? Give yourself a challenge with challenging courses. Very new to math? Start with the simpler resources. So, here I’m providing linear algebra resources for different levels of knowledge on mathematics one already has. This eigenvector is incredibly useful in advanced treatment of the subject, forming the cornerstone of many techniques used including Principle Component Analysis and Singular Value Decomposition. Another important property is the eigenvalue, which is the change in length of the same vector after that transformation. One of the most important properties is the eigenvector, which is the vector that does not change in direction after the transformation is applied. Imagine we want the boat to travel twice as fast in the same direction (represented by new vector y), we would scale the boat’s velocity vector by two using the matrix A, with the formula:Įach matrix has its own properties. Matrices can be interpreted as a transformation to vectors, like scaling, rotation or rotation. If we combine several vectors together, we get a matrix. To start learning linear algebra, we need to start understanding what are the properties of vectors (linear dependence or independence), what we can do with them (dot and cross products), and the properties of the spaces they exist in (subspaces). We can then imagine that the boat’s velocity vector as x =. In the past hour, we can imagine that the boat has moved 3 miles north and 4 miles east, such that it is moving 5 miles per hour in the north-east direction. In this post, I will highlight how I learnt about the linear algebra required for data science. Part 5- Machine Learning (read it here!).Part 4- Computer Science Fundamentals (coming soon). Part 2 - Mathematics: Linear Algebra (you’re here!).Part 1 - Data Processing with SQL, Python and R.Through that, I hope to help people who were in my shoes in planning their self-learning journey in data science. In this series of blog posts, I’ll highlight some of the classes that I have taken along the journey, along with their pros and cons. Illustration by author.ĭata science is the inter-disciplinary field that uses techniques and theories drawn from the fields of mathematics, computer science, domain knowledge. Data Science as the Intersection of Multiple Disciplines.
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