Machine Learning & Artificial Intelligence · Big Data & Distributed Computing

Big Data: Big data paradigm in statistics — Advanced technical reference

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction Introduction # Statistical Paradigms

Overview

Topics covered

  • Introduction
  • Statistics under the Prism of Big Data
  • The Dimensional and Computational Challenge
  • 1.1 The Breaking of Asymptotic Assumptions
  • 1.2 Computability Limits
  • 1.3 Spurious Correlations and Noise Accumulation

Related concepts

Main connections

  • Regularization (Lasso / Ridge / Elastic Net)
  • Linear algebra & matrices
  • Bootstrap resampling
  • Variance & standard deviation
  • Calculus & differentiation
  • Principal component analysis (PCA)

Compatible notation

Mathematical sample in LaTeX

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

𝛃^OLS=(𝐗⊤𝐗)−1𝐗⊤𝐲
View LaTeX source\hat{\boldsymbol{\beta}}_{OLS} = (\mathbf{X}^\top \mathbf{X})^{-1}\mathbf{X}^\top \mathbf{y}