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    • Dynamic Programming

    Dynamic Programming Courses Online

    Master dynamic programming techniques for solving complex problems. Learn to break down problems into simpler subproblems for efficient solutions.

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    Explore the Dynamic Programming Course Catalog

    • Status: Free Trial
      Free Trial
      U

      University of Colorado Boulder

      Dynamic Programming, Greedy Algorithms

      Skills you'll gain: Theoretical Computer Science, Algorithms, Computational Thinking, Data Structures, Design Strategies, Programming Principles, Computer Science, Advanced Mathematics, Linear Algebra, Analysis

      4.6
      Rating, 4.6 out of 5 stars
      ·
      221 reviews

      Advanced · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      S

      Stanford University

      Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming

      Skills you'll gain: Algorithms, Bioinformatics, Graph Theory, Computational Thinking, Data Structures, Theoretical Computer Science

      4.8
      Rating, 4.8 out of 5 stars
      ·
      1.3K reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: New
      New
      P

      Packt

      Matrix Calculus for Data Science & Machine Learning

      Skills you'll gain: Calculus, Applied Mathematics, NumPy, Linear Algebra, Applied Machine Learning, Tensorflow, Machine Learning, Derivatives, Algorithms, Development Environment

      Intermediate · Course · 1 - 3 Months

    • Status: New
      New
      M

      MathWorks

      Reinforcement Learning

      Skills you'll gain: Reinforcement Learning, Artificial Intelligence and Machine Learning (AI/ML), Artificial Neural Networks, Applied Machine Learning, Machine Learning, Simulations

      Beginner · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      U

      University of Alberta

      Reinforcement Learning

      Skills you'll gain: Reinforcement Learning, Machine Learning, Sampling (Statistics), Machine Learning Algorithms, Artificial Intelligence, Deep Learning, Simulations, Solution Architecture, Artificial Intelligence and Machine Learning (AI/ML), Markov Model, Supervised Learning, Algorithms, Performance Testing, Artificial Neural Networks, Pseudocode, Linear Algebra, Probability Distribution, Debugging

      4.7
      Rating, 4.7 out of 5 stars
      ·
      3.5K reviews

      Intermediate · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      D

      Duke University

      Rust Programming

      Skills you'll gain: Rust (Programming Language), Containerization, Data Pipelines, CI/CD, Docker (Software), DevOps, Jenkins, Unit Testing, Large Language Modeling, Test Case, Restful API, Serverless Computing, Generative AI, Command-Line Interface, Amazon Web Services, Software Testing, Maintainability, Prometheus (Software), System Monitoring, MLOps (Machine Learning Operations)

      4
      Rating, 4 out of 5 stars
      ·
      209 reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: Free
      Free
      P

      Princeton University

      Computer Science: Programming with a Purpose

      Skills you'll gain: Programming Principles, Computer Programming, Object Oriented Programming (OOP), Performance Tuning, Data Structures, Java, Program Development, Computational Thinking, Algorithms, Animations, Debugging

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.3K reviews

      Beginner · Course · 1 - 3 Months

    • U

      University of Washington

      Programming Languages, Part A

      Skills you'll gain: Software Installation, Programming Principles, Other Programming Languages, Functional Design, Ruby (Programming Language), Software Design Patterns, Computational Thinking

      4.9
      Rating, 4.9 out of 5 stars
      ·
      1.9K reviews

      Intermediate · Course · 1 - 3 Months

    • B

      Ball State University

      Introduction to Programming

      Skills you'll gain: Data Analysis, Statistical Analysis, Computer Programming, Python Programming, Object Oriented Programming (OOP), Natural Language Processing, Data Science, Scripting, NumPy, Debugging, Data Manipulation

      Build toward a degree

      4.7
      Rating, 4.7 out of 5 stars
      ·
      28 reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      I

      IBM

      Python for Data Science, AI & Development

      Skills you'll gain: Jupyter, Python Programming, Data Structures, Web Scraping, Data Manipulation, Programming Principles, Pandas (Python Package), Computer Programming, Object Oriented Programming (OOP), Restful API, NumPy, Application Programming Interface (API), Data Analysis, Data Import/Export, File Management

      4.6
      Rating, 4.6 out of 5 stars
      ·
      41K reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      Status: AI skills
      AI skills
      I

      IBM

      IBM Data Science

      Skills you'll gain: Dashboard, Data Visualization Software, Data Visualization, SQL, Predictive Modeling, Supervised Learning, Feature Engineering, Plotly, Interactive Data Visualization, Exploratory Data Analysis, Jupyter, Data Wrangling, Data Literacy, Data Analysis, Data Mining, Matplotlib, Unsupervised Learning, Generative AI, Pandas (Python Package), Professional Networking

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      143K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      M

      Meta

      Programming with JavaScript

      Skills you'll gain: Jest (JavaScript Testing Framework), Javascript, Unit Testing, Web Applications, Programming Principles, Object Oriented Programming (OOP), Event-Driven Programming, Node.JS, JSON, Computer Programming, Data Structures, Debugging, Package and Software Management, Program Development, Functional Design

      4.7
      Rating, 4.7 out of 5 stars
      ·
      4.7K reviews

      Beginner · Course · 1 - 3 Months

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    In summary, here are 10 of our most popular dynamic programming courses

    • Dynamic Programming, Greedy Algorithms: University of Colorado Boulder
    • Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming: Stanford University
    • Matrix Calculus for Data Science & Machine Learning: Packt
    • Reinforcement Learning: MathWorks
    • Reinforcement Learning: University of Alberta
    • Rust Programming: Duke University
    • Computer Science: Programming with a Purpose: Princeton University
    • Programming Languages, Part A: University of Washington
    • Introduction to Programming: Ball State University
    • Python for Data Science, AI & Development: IBM

    Skills you can learn in Business Strategy

    Modeling (25)
    Market (economics) (21)
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    Planning (16)
    Strategic Management (16)
    Business Model (13)
    Operations Management (13)
    Analytics (12)
    Evaluation (12)
    Project Management (11)
    Supply Chain (10)
    Decision-making (9)

    Frequently Asked Questions about Dynamic Programming

    Dynamic programming is an algorithmic technique that solves optimization problems by breaking them down into simpler sub-problems. The solutions to these sub-problems are stored along the way, which ensures that each problem is only solved once. Dynamic programming has become an important technique for efficiently solving complex optimization problems in applications such as reinforcement learning for artificial intelligence (AI) and genome sequencing in bioinformatics.

    The advantages of dynamic programming can be understood in relation to other algorithms used to solve optimization problems. Like divide and conquer algorithms, dynamic programming breaks down a larger problem into smaller pieces; however, unlike divide and conquer, it saves solutions along the way so each problem is only solved once, improving the speed of this approach. By contrast, greedy algorithms also solve each problem only once, but unlike dynamic programming, it does not look back to consider all possible solutions, running the risk that the greedy algorithm will settle on a locally optimal solution that is not globally optimal.

    Ultimately, there is no single “silver bullet” algorithm that is best for every application, and different types of problems will require different techniques. However, dynamic programming’s ability to deliver globally optimal solutions with relative efficiency makes it an important part of any programmer’s skill set.‎

    Dynamic programming is a valuable career skill for programmers working on complex optimization problems in high-tech fields such as data science, artificial intelligence and machine learning, robotics, and bioinformatics. Computer scientists with the ability to find the right approaches to these high-value problems are highly sought after and compensated accordingly by leading companies in these industries. According to the Bureau of Labor Statistics, computer and information research scientists earned a median annual salary of $122,840 per year in 2019, and these jobs are expected to grow much faster than the average across the rest of the economy.‎

    Yes! There are an incredibly wide range of learning opportunities in computer science on Coursera, including courses and Specializations in algorithms and dynamic programming. Coursera lets you learn about dynamic programming remotely from top-ranked universities from around the world such as Stanford University, National Research University Higher School of Economics, and University of Alberta. And, because learners on Coursera pay a significantly lower tuition than on-campus students, you won’t need to use dynamic programming or other algorithmic techniques to determine whether it’s an optimal investment in your career.‎

    The people best suited for roles in dynamic programming are computer programmers and people with experience working with algorithms. Dynamic programming is an algorithmic technique for breaking down a problem into simpler subproblems, so it’s important that people who pursue roles in dynamic programming have experience working in fields that utilize this technique. People who work as aerospace engineers or in economics are best suited for these roles.‎

    A common career path for someone in dynamic programming is a job as a computer and information research scientist. These scientists design software systems and invent new computing languages, which they later test and present to colleagues in academic journals and conferences. Another common career path for someone studying dynamic programming is working with robotics or in computer programming.‎

    It’s important for learners to have a strong handle on algorithms, so topics related to advanced algorithms and complexity, discrete optimization, data structures and algorithms, algorithms on strings, and algorithms on graphs are extremely useful. Reinforcement learning may explore topics related to AI tools and how it is useful in game development, customer interaction, and supply chain. For learners looking to improve their computer programming skills, the fundamentals of computing may help you program and think like a computer scientist. Competitive programming is the next step up and is a great option if you want to explore topics related to dynamic programming and number and graph theories.‎

    Most people with a background in dynamic programming are hired to work in the federal government, according to the U.S. Bureau of Labor Statistics. Some people also work for computer systems and design-related services companies. Research and development companies hire people with a background in dynamic programming as well as software publishers.‎

    Online Dynamic Programming courses offer a convenient and flexible way to enhance your knowledge or learn new Dynamic Programming skills. Choose from a wide range of Dynamic Programming courses offered by top universities and industry leaders tailored to various skill levels.‎

    When looking to enhance your workforce's skills in Dynamic Programming, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.‎

    This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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