Data Science Math Skills
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assignment Уровень : Начальный
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timer 12 час(ы) курса


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!

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  • Week 1 - 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
  • Week 1 - 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. It also gives a real-world appl...
  • Week 2 - 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. The second lesson introduces the idea of a ...
  • Week 3 - 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 probl...
  • Week 4 - 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. We start with the basic definitions and rules of probability, including the probabil...


Daniel Egger
Executive in Residence and Director, Center for Quantitative Modeling
Pratt School of Engineering, Duke University

Paul Bendich
Assistant research professor of Mathematics; Associate Director for Curricular Engagement at the Information Initiative at Duke



Duke University
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.



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