What is discrete mathematics?

Discrete mathematics refers to a group of mathematics branches that deal with discrete objects instead of continuous objects. In discrete mathematics, objects have distinct, separate values represented by integers, unlike real numbers used in continuous mathematics. Discrete mathematics includes combinatorics, set theory, graph theory, number theory, and probability. It is integral to computer science and plays a role in the field of data science.

Why learn discrete mathematics?

You may consider learning discrete mathematics if you want to study computer science or work in a data science field. Discrete mathematics is a foundation of computer science, and programmers often use principles of set theory, probability, and combinations to analyze algorithms when writing programs and applications. Learning discrete mathematics can also help boost other useful skills like logic, reasoning, and problem-solving, making you a more marketable job candidate.

What are typical careers that use discrete mathematics?

Typical careers that use discrete mathematics are in the computer science field, such as software development, programming, and cryptography. Data scientists and data analysts may use their knowledge of discrete mathematics in their work. Other careers that can incorporate discrete mathematics include electrical and mechanical engineers in addition to data analysts, business analysts, and market researchers. Discrete mathematics also influences the work of urban planners, epidemiologists, and social scientists who collect and analyze large amounts of data.

How can online courses help me learn discrete mathematics?

Online courses can introduce you to core concepts of discrete mathematics, such as sets, relations, and functions. Lessons include topics like partial orders, enumerative combinatorics, and the binomial coefficient, and you have opportunities to apply the concepts to real-world applications. You may choose courses that cover specific subjects like graph theory or probability to increase your knowledge of these disciplines. Alternatively, you may decide to take courses that explore the application of discrete mathematics in specific fields like data science, computer science, or machine learning.

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University of California San Diego
Introduction to Discrete Mathematics for Computer Science
習得できるスキル： Mathematics, Graph Theory, Computer Science, Computer Programming, Python Programming, Statistical Programming, Data Science, Algebra, Theoretical Computer Science, Probability & Statistics, Algorithms, Calculus, Combinatorics, Data Analysis, Statistical Analysis, General Statistics, Mathematical Theory & Analysis, Programming Principles, Bayesian Statistics, Computational Thinking, Geometry, Applied Mathematics, Correlation And Dependence, Estimation, Probability Distribution, Computational Logic, Business Analysis, Communication, Computer Architecture, Critical Thinking, Cryptography, Data Visualization, Entrepreneurship, Leadership and Management, Machine Learning, Markov Model, Microarchitecture, Problem Solving, Research and Design, Scientific Visualization, Security Engineering, Statistical Visualization, Strategy and Operations
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Beginner · Specialization · 3-6 Months
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University of California San Diego
Mathematical Thinking in Computer Science
習得できるスキル： Mathematics, Algebra, Geometry, Algorithms, Applied Mathematics, Computational Thinking, Theoretical Computer Science, Computational Logic, Business Analysis, Computer Programming, Critical Thinking, Leadership and Management, Mathematical Theory & Analysis, Problem Solving, Programming Principles, Python Programming, Research and Design, Statistical Programming, Strategy and Operations
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Stanford University
Introduction to Statistics
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Imperial College London
Mathematics for Machine Learning
習得できるスキル： Mathematics, Algebra, Linear Algebra, Machine Learning, Python Programming, Probability & Statistics, General Statistics, Calculus, Applied Mathematics, Mathematical Theory & Analysis, Computer Programming, Statistical Programming, Algorithms, Dimensionality Reduction, Regression, Theoretical Computer Science, Basic Descriptive Statistics, Data Analysis, Probability Distribution, Artificial Neural Networks, Computer Graphic Techniques, Computer Graphics, Computer Networking, Deep Learning, Differential Equations, Experiment, Machine Learning Algorithms, Network Model
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University of California San Diego
Combinatorics and Probability
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discrete mathematicsに関連する検索

introduction to discrete mathematics for computer science

离散数学概论 discrete mathematics generality

要約して、discrete mathematics の人気コース10選をご紹介します。

Introduction to Discrete Mathematics for Computer Science: University of California San Diego
Discrete Mathematics: Shanghai Jiao Tong University
Mathematical Thinking in Computer Science: University of California San Diego
离散数学概论 Discrete Mathematics Generality: Peking University
Data Science Math Skills: Duke University
Introduction to Statistics: Stanford University
Mathematics for Machine Learning: Imperial College London
Mathematics for Computer Science: University of London
Combinatorics and Probability: University of California San Diego
Cyber-Physical Systems: Modeling and Simulation: University of California, Santa Cruz

Probability And Statisticsで学べるスキル

Discrete Mathematicsに関するよくある質問

Discrete mathematics refers to a group of mathematics branches that deal with discrete objects instead of continuous objects. In discrete mathematics, objects have distinct, separate values represented by integers, unlike real numbers used in continuous mathematics. Discrete mathematics includes combinatorics, set theory, graph theory, number theory, and probability. It is integral to computer science and plays a role in the field of data science.‎
You may consider learning discrete mathematics if you want to study computer science or work in a data science field. Discrete mathematics is a foundation of computer science, and programmers often use principles of set theory, probability, and combinations to analyze algorithms when writing programs and applications. Learning discrete mathematics can also help boost other useful skills like logic, reasoning, and problem-solving, making you a more marketable job candidate. ‎
Typical careers that use discrete mathematics are in the computer science field, such as software development, programming, and cryptography. Data scientists and data analysts may use their knowledge of discrete mathematics in their work. Other careers that can incorporate discrete mathematics include electrical and mechanical engineers in addition to data analysts, business analysts, and market researchers. Discrete mathematics also influences the work of urban planners, epidemiologists, and social scientists who collect and analyze large amounts of data.‎
Online courses can introduce you to core concepts of discrete mathematics, such as sets, relations, and functions. Lessons include topics like partial orders, enumerative combinatorics, and the binomial coefficient, and you have opportunities to apply the concepts to real-world applications. You may choose courses that cover specific subjects like graph theory or probability to increase your knowledge of these disciplines. Alternatively, you may decide to take courses that explore the application of discrete mathematics in specific fields like data science, computer science, or machine learning.‎