## Data Science Math Skills

Start Date: 08/16/2020

 Course Type: Common Course

#### About 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. Topics include: ~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. While this course is intended as a general introduction to the math skills needed for data science, it can be considered a prerequisite for learners interested in the course, "Mastering Data Analysis in Excel," which is part of the Excel to MySQL Data Science Specialization. Learners who master Data Science Math Skills will be fully prepared for success with the more advanced math concepts introduced in "Mastering Data Analysis in Excel." Good luck and we hope you enjoy the course!

#### Course Syllabus

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. It also gives a real-world application to medical testing. The second lesson, "The Infinite World of Real Numbers," explains notation we use to discuss intervals on the real number line. The module concludes with the third lesson, "That Jagged S Symbol," where you will learn how to compactly express a long series of additions and use this skill to define statistical quantities like mean and variance.

#### Course Introduction

Data Science Math Skills Data Science Math Skills is the first course in the Data Science Specialization. This course will introduce data scientists and data analysts to basic math concepts and gives you the tools to apply them in practice. Data scientists will learn how to access, manipulate, and manipulate data using basic math formulas. These tools will prepare them to work with more advanced math problems. Data Science Math Skills will build on the research presented in the first Data Science Math Capstone Course, which is the second course in the Specialization. This course will focus on building more advanced applications in Excel by adding new elements, new formulas, and new datasets. These topics will require more advanced math knowledge and will be introduced through peer review. Data Science Math Skills introduces the knowledge of accessing, manipulating, and manipulating data using basic math formulas. These tools will prepare them to work with more advanced math problems. Data Science Math Skills introduces the concepts of arithmetic and algebra. These topics will require more advanced math knowledge. The course will have four parts. First, we will introduce the topics of data analysis and data visualization. We will then introduce the basic concepts of data visualization and explore graphics. We will then introduce the basics of data manipulation and how to introduce new data into Excel. We will then apply the basics of Excel to introduce new data. And finally, we will introduce a number series and its exponential form. Each part of the course is designed to give you a good understanding of the

#### Course Tag

Bayes' Theorem Bayesian Probability Probability Probability Theory

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