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Computational Science Courses Online

Understand computational science for solving complex scientific problems. Learn to use computational tools and simulations in various scientific disciplines.

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Explore the Computational Science Course Catalog

  • Status: New
    New
    Status: Free Trial
    Free Trial
    U

    University of Colorado Boulder

    High-Performance and Parallel Computing

    Skills you'll gain: Bash (Scripting Language), Distributed Computing, Scalability, Software Architecture, Big Data, Operating Systems, Performance Tuning, File Systems, Cloud Development, Scripting, Command-Line Interface, C and C++, Linux, Data Sharing, OS Process Management, Communication Systems, Artificial Intelligence

    3.7
    Rating, 3.7 out of 5 stars
    ·
    148 reviews

    Advanced · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    U

    University of California, Davis

    Computational Social Science

    Skills you'll gain: Social Network Analysis, Network Analysis, Data Wrangling, Natural Language Processing, Web Scraping, Social Sciences, Data Ethics, Databases, Artificial Intelligence, Research, Simulations, Big Data, Systems Thinking, Trend Analysis, Data Science, Artificial Intelligence and Machine Learning (AI/ML), Research Methodologies, Machine Learning, Data Collection, Agentic systems

    4.6
    Rating, 4.6 out of 5 stars
    ·
    1.2K reviews

    Beginner · Specialization · 3 - 6 Months

  • U

    University of London

    Bachelor of Science in Computer Science

    Skills you'll gain: Virtual Reality, Human Computer Interaction, Game Design, Mobile Development, Agile Software Development, Animations, Database Design, Event-Driven Programming, Responsive Web Design, Web Applications, Version Control, Incident Response, Natural Language Processing, Unsupervised Learning, Combinatorics, Node.JS, Scikit Learn (Machine Learning Library), Secure Coding, Generative AI, Full-Stack Web Development

    Earn a degree

    Degree · 1 - 4 Years

  • Status: Free Trial
    Free Trial
    S

    Stanford University

    Algorithms

    Skills you'll gain: Data Structures, Graph Theory, Algorithms, Bioinformatics, Theoretical Computer Science, Social Network Analysis, Network Analysis, Computational Thinking, Analysis, Probability & Statistics, Network Routing, Probability, Pseudocode, Computational Logic, Operations Research

    4.8
    Rating, 4.8 out of 5 stars
    ·
    6K reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    U

    University of California San Diego

    Data Structures and Algorithms

    Skills you'll gain: Data Structures, Graph Theory, Algorithms, Network Routing, Program Development, Network Model, Bioinformatics, Operations Research, Data Storage, Development Testing, Theoretical Computer Science, Computational Thinking, Network Analysis, Programming Principles, File Systems, Computer Programming, Epidemiology, Social Network Analysis, Software Testing, Debugging

    4.6
    Rating, 4.6 out of 5 stars
    ·
    17K reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    U

    University of California San Diego

    Introduction to Discrete Mathematics for Computer Science

    Skills you'll gain: Graph Theory, Logical Reasoning, Cryptographic Protocols, Combinatorics, Computational Logic, Deductive Reasoning, Cryptography, Probability, Key Management, Computational Thinking, Encryption, Probability Distribution, Network Analysis, Public Key Cryptography Standards (PKCS), Theoretical Computer Science, Bayesian Statistics, Python Programming, Data Structures, Algorithms, Arithmetic

    4.5
    Rating, 4.5 out of 5 stars
    ·
    3.7K reviews

    Beginner · Specialization · 3 - 6 Months

What brings you to Coursera today?

  • Status: Free Trial
    Free Trial
    U

    University of Colorado Boulder

    Foundations of Data Structures and Algorithms

    Skills you'll gain: Theoretical Computer Science, Algorithms, Data Structures, Graph Theory, Operations Research, Public Key Cryptography Standards (PKCS), Computational Thinking, Computer Programming, Programming Principles, Cryptography, Computer Science, Pseudocode, Applied Mathematics, Advanced Mathematics, Mathematical Theory & Analysis, Tree Maps, Encryption, Combinatorics, Python Programming, Analysis

    Build toward a degree

    4.6
    Rating, 4.6 out of 5 stars
    ·
    857 reviews

    Advanced · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    U

    University of California, Davis

    Computational Social Science Methods

    Skills you'll gain: Social Network Analysis, Social Sciences, Network Analysis, Data Science, Machine Learning, Computational Thinking, Artificial Intelligence, Data Analysis, Simulations, Big Data, Scientific Methods

    4.7
    Rating, 4.7 out of 5 stars
    ·
    348 reviews

    Beginner · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    U

    University of California, Davis

    Computational Social Science Capstone Project

    Skills you'll gain: Social Network Analysis, Data Wrangling, Natural Language Processing, Web Scraping, Databases, Social Sciences, Trend Analysis, Data Collection, Simulations, Big Data, Machine Learning, Computational Thinking, Data Visualization Software, Artificial Intelligence and Machine Learning (AI/ML)

    4.7
    Rating, 4.7 out of 5 stars
    ·
    40 reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    U

    University of London

    Introduction to Computer Science and Programming

    Skills you'll gain: Computer Science, Debugging, Development Environment, Computer Systems, Web Applications, Arithmetic, Computer Literacy, Javascript, Network Security, Computer Networking, Program Development, Computer Graphics, Web Design and Development, Cybersecurity, Computer Programming, Problem Management, Computational Thinking, General Mathematics, Systems Of Measurement, Problem Solving

    Build toward a degree

    4.2
    Rating, 4.2 out of 5 stars
    ·
    1.5K reviews

    Beginner · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    U

    University of Illinois Urbana-Champaign

    Accelerated Computer Science Fundamentals

    Skills you'll gain: C++ (Programming Language), Data Structures, Object Oriented Programming (OOP), Object Oriented Design, Graph Theory, Development Environment, Data Storage, Engineering Software, Unstructured Data, Computer Programming, Algorithms, Data Storage Technologies, Debugging, Program Development, Database Systems, Database Theory, Network Routing, Theoretical Computer Science

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

    Intermediate · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    U

    University of Illinois Urbana-Champaign

    Cloud Computing

    Skills you'll gain: Distributed Computing, Cloud Infrastructure, Cloud Services, Big Data, Apache Spark, Cloud Computing, Cloud Storage, Cloud Platforms, Network Architecture, Data Storage Technologies, Computer Networking, File Systems, Apache Hadoop, Network Infrastructure, Cloud Applications, Infrastructure As A Service (IaaS), Middleware, Data Storage, Software-Defined Networking, NoSQL

    4.3
    Rating, 4.3 out of 5 stars
    ·
    2.1K reviews

    Intermediate · Specialization · 3 - 6 Months

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In summary, here are 10 of our most popular computational science courses

  • High-Performance and Parallel Computing: University of Colorado Boulder
  • Computational Social Science: University of California, Davis
  • Bachelor of Science in Computer Science: University of London
  • Algorithms: Stanford University
  • Data Structures and Algorithms: University of California San Diego
  • Introduction to Discrete Mathematics for Computer Science: University of California San Diego
  • Foundations of Data Structures and Algorithms: University of Colorado Boulder
  • Computational Social Science Methods: University of California, Davis
  • Computational Social Science Capstone Project: University of California, Davis
  • Introduction to Computer Science and Programming: University of London

Frequently Asked Questions about Computational Science

Computational science is an interdisciplinary field that utilizes computer modeling, simulation, and data analysis to solve complex problems across various scientific disciplines. It involves the application of computational techniques, algorithms, and mathematical models to study natural phenomena, analyze large datasets, and simulate complex systems. Computational science plays a crucial role in advancing scientific research, improving predictions, and facilitating scientific discovery across disciplines such as physics, chemistry, biology, engineering, and more.‎

To excel in computational science, you need to develop the following skills:

  • Programming: Proficiency in programming languages such as Python, C++, or MATLAB to implement algorithms, perform data analysis, and develop computational models.
  • Mathematical Modeling: Understanding of mathematical concepts and techniques used in computational science, such as calculus, linear algebra, differential equations, and numerical methods.
  • Scientific Computing: Knowledge of computational techniques, including numerical integration, optimization, finite element methods, and numerical simulation.
  • Data Analysis: Skills in analyzing and interpreting complex datasets using statistical methods, data visualization, and machine learning techniques.
  • High-Performance Computing: Familiarity with parallel computing, distributed computing, and utilizing computational resources efficiently for large-scale simulations.
  • Computational Modeling: Ability to develop and implement computational models to simulate and study complex systems, phenomena, or processes.
  • Problem-Solving: Aptitude for formulating scientific problems in computational terms, designing algorithms, and solving them using computational tools.
  • Data Management: Proficiency in handling and processing large datasets, data storage, and data organization for efficient analysis and retrieval.
  • Scientific Visualization: Ability to effectively present and visualize scientific data, simulation results, and complex models for analysis and communication.
  • Collaboration and Interdisciplinary Skills: Capacity to work in multidisciplinary teams, communicate with domain experts, and integrate computational methods into scientific research.‎

With computational science skills, you can pursue various job opportunities, including:

  • Computational Scientist
  • Research Scientist
  • Data Scientist
  • Simulation Engineer
  • Software Developer (specializing in scientific applications)
  • Computational Physicist
  • Computational Chemist
  • Bioinformatics Specialist
  • Computational Biologist
  • Research Analyst

These roles involve utilizing computational techniques, developing and implementing models and simulations, analyzing scientific data, and contributing to scientific research in academia, government labs, research institutions, or industries.‎

Computational science is well-suited for individuals who possess the following qualities:

  • Analytical and Mathematical Aptitude: Ability to analyze complex scientific problems, apply mathematical techniques, and derive meaningful insights using computational methods.
  • Programming Proficiency: Experience or willingness to learn programming languages and tools used in scientific computing, simulation, and data analysis.
  • Curiosity and Critical Thinking: A passion for scientific exploration, asking research questions, and devising innovative approaches to solving complex problems.
  • Interdisciplinary Interest: Eagerness to work across scientific disciplines, collaborate with domain experts, and apply computational methods to various scientific domains.
  • Problem-Solving Orientation: Aptitude for formulating and solving scientific problems, designing algorithms, and interpreting computational results in a scientific context.
  • Detail-Oriented: Meticulousness in handling and analyzing scientific data, ensuring accuracy in computational models, and interpreting simulation results.
  • Communication Skills: Ability to effectively communicate scientific concepts, present research findings, and collaborate with researchers from diverse backgrounds.
  • Continuous Learners: Willingness to stay updated with the latest research in computational science, emerging computational methodologies, and scientific domains.‎

Several topics are related to computational science that you can study to enhance your skills and knowledge, including:

  • Numerical Methods and Algorithms
  • Mathematical Modeling and Simulation
  • Parallel and Distributed Computing
  • Scientific Data Analysis and Visualization
  • High-Performance Computing
  • Computational Physics
  • Computational Chemistry
  • Computational Biology
  • Computational Engineering
  • Machine Learning for Scientific Applications

Exploring these topics through online courses, academic programs, research papers, and practical projects will provide a comprehensive understanding of the concepts and techniques used in computational science, enabling you to apply them effectively in scientific research and problem-solving.‎

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

When looking to enhance your workforce's skills in Computational Science, 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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