Data Science Math Skills
Master essential math for data science: Set theory, real numbers, functions, calculus basics, and probability theory for aspiring data scientists.
Master essential math for data science: Set theory, real numbers, functions, calculus basics, and probability theory for aspiring data scientists.
This comprehensive course introduces the fundamental math skills required for data science. Designed for learners with basic math knowledge, it covers essential topics like set theory, real number properties, interval notation, algebra with inequalities, Cartesian plane graphing, functions, derivatives, exponents, logarithms, and probability theory including Bayes' theorem. The course aims to build a strong foundation in mathematical concepts and notation used in data science, preparing learners for more advanced material. With a focus on clear explanations and practical applications, it's an ideal starting point for those looking to enter the field of data science or strengthen their mathematical background.
4.5
(12,066 ratings)
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English
پښتو, বাংলা, اردو, 3 more
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What you'll learn
Master set theory concepts including Venn diagrams and their applications
Understand real number properties, interval notation, and algebra with inequalities
Learn to use summation and Sigma notation for statistical calculations
Graph and describe functions on the Cartesian plane, including slope and distance formulas
Grasp basic calculus concepts like instantaneous rate of change and tangent lines
Explore exponents, logarithms, and the natural log function
Skills you'll gain
This course includes:
5 Hours PreRecorded video
14 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This course provides a comprehensive introduction to the essential mathematical skills needed for data science. It covers fundamental topics such as set theory, properties of real numbers, interval notation, algebra with inequalities, graphing on the Cartesian plane, functions and their inverses, basic calculus concepts like derivatives, exponents and logarithms, and probability theory including Bayes' theorem. The course is designed to build a strong foundation in mathematical concepts and notation used in data science, preparing learners for more advanced material. With a focus on clear explanations and practical applications, it's an ideal starting point for those looking to enter the field of data science or strengthen their mathematical background.
Welcome to Data Science Math Skills
Module 1 · 17 Minutes to complete
Building Blocks for Problem Solving
Module 2 · 3 Hours to complete
Functions and Graphs
Module 3 · 2 Hours to complete
Measuring Rates of Change
Module 4 · 3 Hours to complete
Introduction to Probability Theory
Module 5 · 3 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructors
Executive in Residence and Director, Center for Quantitative Modeling
Daniel Egger brings over seventeen years of experience in developing new software products and services as the founder and CEO of several venture-backed information technology companies, as well as serving as Managing Partner in a venture capital fund. He is currently an Executive in Residence in Duke University’s Master of Engineering Management Program and has been teaching courses in entrepreneurship and venture capital at Duke since 2003. Previously, he held the position of Entrepreneur-in-Residence at Duke's Markets and Management Program for undergraduates through the Howard Johnson Foundation.
Assistant research professor of Mathematics; Associate Director for Curricular Engagement at the Information Initiative at Duke
Dr. Paul Bendich is an Assistant Research Professor of Mathematics at Duke University, where he also serves as the Associate Director for Curricular Engagement at the Information Initiative at Duke (iiD). His research primarily focuses on adapting theories from topology, geometry, and abstract algebra to develop tools applicable in various data-centered fields. With a strong foundation in topological data analysis (TDA), Dr. Bendich has contributed significantly to the methodology of TDA, particularly for stratified spaces, and has explored its applications in neuroscience, multi-target tracking, and deep learning. He teaches courses that connect mathematical principles to machine learning, including upper-level courses in topological data analysis and high-dimensional data analysis. Additionally, he directs programs like Data+ and Data Expeditions, which promote interdisciplinary undergraduate research and student engagement across the university.
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4.5 course rating
12,066 ratings
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