Bayesian Statistics: This module introduces the Bayesian paradigm to Statistics from probability to inference. We will compare the Bayesian methods with commonly taught Frequentist methods in parameter estimation and hypothesis testing.
Calculus Review: This module covers limits, differentiation, integration, and multivariate functions.
Game Theory: This module introduces the basics of non-cooperative Game theory, decision science of interactions among multiple agents, and applications with model and analysis of an interactive situation.
Graph Theory: This module covers how to define and graph data structures and implement graphs for data analysis.
Hypothesis Theory: Development 2021
Information Theory: This module covers an introduction into Information Theory with emphasis on fundamental concepts. The ideas of this theory will cover data compression and reliable communication over noisy channels.
Linear Programming: This module introduces a foundation of Linear programming including how to formulate, basic idea of solution algorithm, how to solve a problem using Excel, and how to interpret the result such as shadow price.
Matrix Algebra (Previous Linear): This module introduces matrix algebra for data science, including matrix addition, subtractions and multiplication, finding determinant, transpose, inverse and row echelon form of a matrix, and calculating eigenvalues and eigenvectors of a matrix.
Multivariate Calculus for Data Science: Development 2021
Optimization (Linear and quadratic programming): Development 2021
Probability Theory: This module introduces the concepts of probability, calculations of probability using addition rule and multiplication rule, and conditional probability. This module also includes counting and Baye’s rules.
Set Theory: This module covers set theory. It embodies categorical classes and sets in terms of relations and functions.
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