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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
      G

      Google

      Foundations: Data, Data, Everywhere

      Skills you'll gain: Data Ethics, Data Analysis, Data-Driven Decision-Making, Google Sheets, Spreadsheet Software, Analytical Skills, Data Sharing, Data Cleansing, Data Processing, Data Visualization Software, SQL, Data Management

      4.8
      Rating, 4.8 out of 5 stars
      ·
      115K reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      I

      IBM

      IBM Business Intelligence (BI) Analyst

      Skills you'll gain: Dashboard, Data Storytelling, Data Warehousing, SQL, Database Design, MySQL, Presentations, Descriptive Statistics, Extract, Transform, Load, Business Intelligence, IBM DB2, Tableau Software, Relational Databases, Star Schema, Data Visualization Software, Interactive Data Visualization, Regression Analysis, Data-Driven Decision-Making, Excel Formulas, Microsoft Excel

      4.7
      Rating, 4.7 out of 5 stars
      ·
      14K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      I

      IBM

      IBM DevOps and Software Engineering

      Skills you'll gain: CI/CD, Istio, Open Web Application Security Project (OWASP), Continuous Integration, Software Development Life Cycle, Software Architecture, Linux Commands, Agile Software Development, Cloud Computing Architecture, Application Deployment, Test Driven Development (TDD), Gherkin (Scripting Language), Jenkins, Kubernetes, Bash (Scripting Language), DevOps, Git (Version Control System), Flask (Web Framework), Jupyter, Grafana

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      60K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      D

      DeepLearning.AI

      Deep Learning

      Skills you'll gain: Computer Vision, Deep Learning, Image Analysis, Natural Language Processing, Artificial Neural Networks, Tensorflow, Supervised Learning, Artificial Intelligence and Machine Learning (AI/ML), Large Language Modeling, Artificial Intelligence, Applied Machine Learning, PyTorch (Machine Learning Library), Machine Learning, Debugging, Performance Tuning, Keras (Neural Network Library), Python Programming, Machine Learning Algorithms, Linear Algebra, Analysis

      Build toward a degree

      4.8
      Rating, 4.8 out of 5 stars
      ·
      146K reviews

      Intermediate · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of Michigan

      Python 3 Programming

      Skills you'll gain: Unified Modeling Language, JSON, Object Oriented Programming (OOP), Software Design, Debugging, Object Oriented Design, Data Processing, Web Scraping, Unit Testing, Programming Principles, Data Import/Export, Restful API, Python Programming, Image Analysis, Data Manipulation, Jupyter, Maintainability, Data Structures, Software Engineering, File Management

      4.8
      Rating, 4.8 out of 5 stars
      ·
      23K reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of Colorado Boulder

      Approximation Algorithms and Linear Programming

      Skills you'll gain: Operations Research, Algorithms, Applied Mathematics, Graph Theory, Network Model, Linear Algebra, Combinatorics, Computational Thinking, Mathematical Modeling, Theoretical Computer Science, Python Programming, Network Analysis

      Build toward a degree

      4.9
      Rating, 4.9 out of 5 stars
      ·
      44 reviews

      Advanced · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      G

      Google

      Google IT Automation with Python

      Skills you'll gain: Git (Version Control System), GitHub, Version Control, Cloud Services, Debugging, Puppet (Configuration Management Tool), Infrastructure as Code (IaC), Bash (Scripting Language), Configuration Management, Professional Networking, CI/CD, Cloud Computing, Technical Communication, Docker (Software), Unit Testing, Containerization, Web Services, Interviewing Skills, Python Programming, Programming Principles

      4.7
      Rating, 4.7 out of 5 stars
      ·
      48K reviews

      Advanced · Professional Certificate · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of Michigan

      Programming for Everybody (Getting Started with Python)

      Skills you'll gain: Programming Principles, Computer Programming, Python Programming, Scripting Languages, Scripting, Computational Thinking, Development Environment

      4.8
      Rating, 4.8 out of 5 stars
      ·
      231K reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      D

      Duke University

      Java Programming and Software Engineering Fundamentals

      Skills you'll gain: Object Oriented Programming (OOP), Java, Hypertext Markup Language (HTML), Software Design, Software Design Patterns, Data Structures, HTML and CSS, Debugging, Software Engineering, Cascading Style Sheets (CSS), Web Development, Computer Programming, Programming Principles, Javascript, Encryption, Algorithms, AI Personalization, Web Design, Data Processing, Statistical Analysis

      4.6
      Rating, 4.6 out of 5 stars
      ·
      22K reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of California, Santa Cruz

      Coding for Everyone: C and C++

      Skills you'll gain: C++ (Programming Language), Debugging, C (Programming Language), Object Oriented Programming (OOP), Software Design Patterns, Code Review, Data Structures, Computer Programming, Algorithms, Command-Line Interface, Programming Principles, Program Development, Computer Science, Computational Thinking, Integrated Development Environments, Graph Theory, Artificial Intelligence, Software Technical Review, File Systems, Game Design

      4.5
      Rating, 4.5 out of 5 stars
      ·
      9.4K reviews

      Beginner · Specialization · 3 - 6 Months

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

      IBM

      IBM Data Engineering

      Skills you'll gain: NoSQL, Data Warehousing, SQL, Apache Hadoop, Extract, Transform, Load, Apache Airflow, Web Scraping, Linux Commands, Database Design, IBM Cognos Analytics, MySQL, Apache Spark, Data Pipelines, Apache Kafka, Database Management, Bash (Scripting Language), Data Store, Jupyter, Generative AI, Professional Networking

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      59K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      I

      IBM

      IBM AI Developer

      Skills you'll gain: Prompt Engineering, HTML and CSS, Software Development Life Cycle, Software Architecture, Large Language Modeling, Flask (Web Framework), Generative AI, Software Design, Unit Testing, ChatGPT, Jupyter, Artificial Intelligence, Scrum (Software Development), Bootstrap (Front-End Framework), Software Engineering, OpenAI, Software Design Patterns, Python Programming, Interviewing Skills, Engineering Software

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      70K reviews

      Beginner · Professional Certificate · 3 - 6 Months

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

    • Foundations: Data, Data, Everywhere: Google
    • IBM Business Intelligence (BI) Analyst: IBM
    • IBM DevOps and Software Engineering: IBM
    • Deep Learning: DeepLearning.AI
    • Python 3 Programming: University of Michigan
    • Approximation Algorithms and Linear Programming : University of Colorado Boulder
    • Google IT Automation with Python: Google
    • Programming for Everybody (Getting Started with Python): University of Michigan
    • Java Programming and Software Engineering Fundamentals: Duke University
    • Coding for Everyone: C and C++: University of California, Santa Cruz

    Skills you can learn in Business Strategy

    Modeling (25)
    Market (economics) (21)
    Plan (19)
    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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