A Crash Course in Applied Linear Algebra. Many people think of Linear Algebra as intimidating, difficult, and great for ending conversations at parties. The truth is that Linear Algebra is extraordinarily useful, often unreasonably so. By studying one equation, y = Ax, you will add an arsenal of tools and intuition to your skillset that can be applied in any technical situation (even nonlinear ones).
Many people think of Linear Algebra as intimidating, difficult, and great for ending conversations at parties. The truth is that Linear Algebra is extraordinarily useful, often unreasonably so. By studying one equation, y = Ax, you will add an arsenal of tools and intuition to your skillset that can be applied in any technical situation (even nonlinear ones).
Chances are good that during your education you were required to take a Mathematics course on Linear Algebra, during which you probably covered topics including null spaces, reduced row echelon forms, independence, and a host of similarly abstract concepts. How much of that material do you remember, much less use on a regular basis? Are your eyes glazing over already?
It may amaze you to discover the number of things in your life, from your movie recommendations, to your GPS, to your 401k portfolio, that depend on concepts from Linear Algebra. Linear Algebra provides the theory for many core techniques in Data Science and Statistics, notably linear regression and PCA. You can't even talk about a normal distribution in more than one dimension without introducing matrices!
This talk will cover the highlights of Applied Linear Algebra. We'll discuss the impacts of familiar topics like eigenvalues and rank and introduce some likely unfamiliar topics such as low-rank approximations, quadratic forms, and definiteness. Throughout the talk, I'll bring in geometric interpretations of the math to help create a visual sense for what is happening, as well as application examples from different science and engineering disciplines. Each concept will be demonstrated using Python and Numpy, often in shockingly few lines of code.
My goal is to leave you with an intuition for matrices and linear systems that will unlock your ability to dive into deeper subjects as you continue in your own growth and exploration.
Prerequisites: a basic knowledge of vectors, matrices, and matrix multiplication
This tutorial on Python vs R vs SAS will help you understand the difference between Python, R and SAS so you know should learn that in 2020. First, you will learn the history of Python, R, and SAS. You will look at how they differ in terms of cost, speed, and ease of learning. You will get an idea about the different data handling and data visualization packages available in Python, R, and SAS. You will understand their usage in the industries, customer, and community support. Finally, you will see the job trends, their popularity, and their preference in the industry. Now, let's get started with learning Python vs R vs SAS.
R Power BI Integration Tutorial - How to run R Scripts in Power BI | Power BI Training will help you to understand the value brought by the integration of the R language into Power BI Desktop and how it provides a powerful tool for transforming and presenting business intelligence data.
Image Top-datascience-blog-2020 hosted in ImgBB
In this article, we understand why data is important, and talk about the importance of statistics in data analysis and data science.
I shall go over the fundamental concepts required multivariate-calculus which we will need to to understand many of the mechanisms of Machine Learning and Data Science