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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134 resultados para "discrete mathematics"

Imperial College London
Mathematics for Machine Learning: PCA
Habilidades que obtendrás: Mathematics, Algebra, Linear Algebra, Machine Learning, Computer Programming, General Statistics, Probability & Statistics, Python Programming, Statistical Programming, Dimensionality Reduction, Basic Descriptive Statistics, Data Analysis, Probability Distribution, Regression, Computer Graphic Techniques, Computer Graphics, Experiment, Mathematical Theory & Analysis
4.1
(3k reseñas)
Intermediate · Course · 1-4 Weeks
University of California San Diego
Mathematical Thinking in Computer Science
Habilidades que obtendrás: 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
4.4
(2.1k reseñas)
Beginner · Course · 1-3 Months
Gratis
DeepLearning.AI
Advanced Learning Algorithms
Habilidades que obtendrás: Machine Learning, Artificial Neural Networks, Deep Learning, Machine Learning Algorithms, Applied Machine Learning, Computer Programming, Python Programming, Statistical Programming, Theoretical Computer Science, Tensorflow, Algorithms, Data Management, Data Structures, Probability & Statistics, General Statistics, Statistical Machine Learning, Computer Vision, Mathematics, Probability Distribution, Computational Logic, Linear Algebra, Mathematical Theory & Analysis
4.9
(2k reseñas)
Beginner · Course · 1-4 Weeks
Gratis
University of Pennsylvania
Master of Computer and Information Technology
Habilidades que obtendrás: Theoretical Computer Science, Computer Programming, Databases, Data Management, Mathematics, Data Structures, Java Programming, Mobile Development, Statistical Programming, Algorithms, Operating Systems, Python Programming, Probability & Statistics, Graph Theory, Software Engineering, Mathematical Theory & Analysis, General Statistics, SQL, Design and Product, Product Design, Software Architecture, Combinatorics, System Programming, Probability Distribution, Software Testing, Agile Software Development, Computer Graphic Techniques, Computer Graphics, Software Framework, Operations Research, Research and Design, Strategy and Operations, Data Visualization, Database Design, Linear Algebra, Programming Principles, Statistical Machine Learning, Computer Networking, Network Architecture, Computer Architecture, Bayesian Statistics, Correlation And Dependence, Data Analysis, Database Theory, Dimensionality Reduction, Estimation, Machine Learning Algorithms, Regression, Software Engineering Tools, Advertising, Algebra, Application Development, BlockChain, Communication, Cryptography, Finance, Human Computer Interaction, Javascript, Journalism, Marketing, Security Engineering, Web Development
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Degree · 1-4 Years
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Imperial College London
Mathematics for Machine Learning
Habilidades que obtendrás: Mathematics, Linear Algebra, Algebra, Machine Learning, Python Programming, Probability & Statistics, General Statistics, Calculus, Computer Programming, Mathematical Theory & Analysis, Applied Mathematics, 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
4.6
(13.6k reseñas)
Beginner · Specialization · 3-6 Months
Gratis
The University of Melbourne
Discrete Optimization
Habilidades que obtendrás: Computer Science, Mathematics, Applied Mathematics, Computer Programming, Geometry, Linear Algebra, Python Programming, Algorithms, Entrepreneurship, Graph Theory, Market Research, Operations Research, Programming Principles, Research and Design, Software Engineering, Software Testing, Statistical Programming, Strategy and Operations, Theoretical Computer Science
4.8
(708 reseñas)
Intermediate · Course · 1-3 Months
Gratis

Búsquedas relacionadas con discrete mathematics

introduction to discrete mathematics for computer science

离散数学概论 discrete mathematics generality

En síntesis, estos son los 10 cursos más populares discrete mathematics

Mathematics for Machine Learning: PCA: Imperial College London
Mathematical Thinking in Computer Science: University of California San Diego
Single Variable Calculus: University of Pennsylvania
Advanced Learning Algorithms: DeepLearning.AI
离散数学: Shanghai Jiao Tong University
Master of Computer and Information Technology: University of Pennsylvania
Asymmetric Cryptography and Key Management: University of Colorado System
Delivery Problem: University of California San Diego
Fibonacci Numbers and the Golden Ratio: The Hong Kong University of Science and Technology
Mathematics for Machine Learning: Imperial College London

Habilidades que puedes aprender en Probability And Statistics

Programación R (19)

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Regresión Lineal (12)

Análisis Estadístico (12)

Inferencia Estadística (11)

Análisis De La Regresión (10)

Bioestadística (9)

Bayesiana (7)

Regresión Logística (7)

Distribución De Probabilidad (7)

Estadística Bayesiana (6)

Estadísticas Médicas (6)

Preguntas frecuentes sobre 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.‎
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.‎

Se puso a disposición este contenido de preguntas frecuentes solo con fines informativos. Se les pide a los estudiantes que realicen investigaciones adicionales para asegurarse de que los cursos y otras credenciales cumplan con sus objetivos personales, profesionales y financieros.