The course is intended for those who want to start Data Science journey, but math knowledge does not allow you to feel confident enough; for those who want to refresh basic mathematics before delving into the data science; for those who do not want to make a reference to a math textbook every time when studying new algorithms.
In the course we will give a base that will allow you to easily learn new specialized sections of mathematics for Data Science. Recall the basic functions, their properties, operations with them; we will go through the basics of differentiation and integration and their application; we will get acquainted with sets and matrices, combinatorics and an introduction to probability theory.
Equations and Inequalities; Quadratic Equations and Inequalities; Absolute Value
Exponentiation; Basic Rules for Exponentiation; Arithmetic, Geometric and Harmonic Progressions
Sets Definition; Basic Operations; Common Number Sets; Binary Relations
Introduction to Function; Domain of Definition; Range of the Function (Codomain); Even and Odd Functions; Periodic Function
Power Function; Inverse Function
Logarithm; Exponential Function; Logarithmic Function
Trigonometry Basics; Trigonometric Functions
Vector; Line; Plane; Angle Between Geometry Objects
Introduction to Derivatives; Equation of a Tangent Line
Functions Monotony; Extrema of a Function; Concavity and Convexity of a Function
Antiderivative: Rules, Formula & Examples.
The Indefinite Integral and Basic Rules of Integration. Area Under a Curve
Matrix of a Numbers, Basic Operations, Determinant
Basics of Combinatorics
Basic Concepts of Probability; Sum Rule; Product Rule; Unconditional Probability; Conditional Probability