Eikosstatistics & data science

Mathematical Foundations · Multivariable Calculus

Vector Calculus · Space Geometry, Quadrics, and Parameterized Curves

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction Introduction Vector Calculus for Data Science and Statistics

Overview

Topics covered

  • Introduction
  • Geometry of Space, Quadrics and Parameterized Curves
  • Previous Fundamentals of Analytical Geometry and Vector Algebra
  • 0.1. Review of Analytical Geometry in and
  • Points, Metric Distance and Midpoint
  • Lines in Three-Dimensional Space

Related concepts

Main connections

  • Linear algebra & matrices
  • Calculus & differentiation
  • Variance & standard deviation
  • Collinearity & VIF
  • Supervised machine learning
  • Principal component analysis (PCA)

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

d(P,Q)=‖PQ→‖=(x2−x1)2+(y2−y1)2+(z2−z1)2.
View LaTeX sourced(P,Q) = \|\vec{PQ}\| = \sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2 + (z_2 - z_1)^2}.