About the Course
Data science courses contain math—no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus.
Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time. Learners who complete this course will master the vocabulary, notation, concepts, and algebra rules that all data scientists must know before moving on to more advanced material.
- Set theory, including Venn diagrams
- Properties of the real number line
- Interval notation and algebra with inequalities
- Uses for summation and Sigma notation
- Math on the Cartesian (x,y) plane, slope and distance formulas
- Graphing and describing functions and their inverses on the x-y plane,
- The concept of instantaneous rate of change and tangent lines to a curve
- Exponents, logarithms, and the natural log function.
- Probability theory, including Bayes’ theorem.
Skills you will gain
- Bayes’ Theorem
- Bayesian Probability
- Probability Theory
Welcome to Data Science Math Skills: This short module includes an overview of the course’s structure, working process, and information about course certificates, quizzes, video lectures, and other important course details. Make sure to read it right away and refer back to it whenever needed.
Building Blocks for Problem Solving: This module contains three lessons that are build to basic math vocabulary. The first lesson, “Sets and What They’re Good For,” walks you through the basic notions of set theory, including unions, intersections, and cardinality.
Functions and Graphs: This module builds vocabulary for graphing functions in the plane. In the first lesson, “Descartes Was Really Smart,” you will get to know the Cartesian Plane, measure distance in it, and find the equations of lines.
Measuring Rates of Change: This module begins a very gentle introduction to the calculus concept of the derivative. The first lesson, “This is About the Derivative Stuff,” will give basic definitions, work a few examples, and show you how to apply these concepts to the real-world problem of optimization.
Introduction to Probability Theory: This module introduces the vocabulary and notation of probability theory – mathematics for the study of outcomes that are uncertain but have predictable rates of occurrence.
To enroll for this course, click the link below.
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