Essential Topics of Math & Stat by Randy Lao.txt

By Randy Lao
Here are the Essential Topics of Math to learn #DataScience and #MachineLearning:

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Breakdown of Importance
Linear Algebra (35%)
Statistics & Probability (25%)
Calculus (15%)
Optimization & Algorithms (15%)
Other (10%)

➤ Linear Algebra
Matrices, Vectors, Eigenvalues & Eigenvectors, Linear Transformations & Equations
https://lnkd.in/gW6_F_3

➤ Inferential Statistics
Sampling Distribution, Central Limit Theorem, Hypothesis Testing, Types of Errors, ANOVA, Chi-Square, T-Test
https://lnkd.in/gbh3aRj

➤ Probability Theory
Random Variables, Types of Distributions, Sampling, Confidence Intervals, Z Scores
https://lnkd.in/gJ442c8

➤ Basic Calculus
https://lnkd.in/gEUFDaS

➤ Linear Programming
Formulating a Real Problem to a Mathematical Model
https://lnkd.in/gGBpjaK

➤ Optimization
Gradient Algorithms & Objective Functions
https://lnkd.in/g_e9sJu

➤ Graph Theory
Trees, Nodes, Edges
https://lnkd.in/gYUgBhA

➤ Data Structures
https://lnkd.in/gT_8_Fc

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There are so many other great online resources for you to find and get a better understanding on how all of these topics tie into Data Science & Machine Learning

Going over these topics will definitely set you up towards building the necessary mathematical foundations for this field.