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Computational Science Courses

Computational Science courses can help you learn numerical analysis, simulation techniques, and data visualization. You can build skills in algorithm development, statistical modeling, and high-performance computing. Many courses introduce tools like Python, MATLAB, and R, that support implementing simulations and analyzing large datasets. You'll also explore key topics such as parallel computing, machine learning applications, and optimization methods, allowing you to tackle complex scientific problems and enhance your analytical capabilities.


Popular Computational Science Courses and Certifications


  • 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, File I/O, Big Data, Operating Systems, File Systems, Cloud Development, Scripting, Command-Line Interface, C and C++, Performance Tuning, Linux, Programming Principles, Computer Architecture, Communication Systems

    3.8
    Rating, 3.8 out of 5 stars
    ·
    168 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, Social Studies, Data Science, Machine Learning, Computational Thinking, Artificial Intelligence, Data Analysis, Simulations, Big Data, Digital Transformation, Predictive Modeling

    4.7
    Rating, 4.7 out of 5 stars
    ·
    350 reviews

    Beginner · Course · 1 - 4 Weeks

  • 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, Data Processing, Big Data, Systems Thinking, Social Studies, Trend Analysis, Data Science, AI Enablement, Research Methodologies, Machine Learning

    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, Game Design, Full-Stack Web Development, Mobile Development, Human Computer Interaction, Agile Software Development, Animations, Data Ethics, Event-Driven Programming, Git (Version Control System), Web Applications, Incident Response, Data Science, Natural Language Processing, Unsupervised Learning, Combinatorics, Database Design, Web Development, Generative AI, Usability Testing

    Earn a degree

    Degree · 1 - 4 Years

  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    GPU Programming

    Skills you'll gain: Artificial Neural Networks, Image Analysis, Event-Driven Programming, Scalability, Deep Learning, C and C++, Performance Tuning, Convolutional Neural Networks, Data-oriented programming, C++ (Programming Language), Digital Signal Processing, Linear Algebra, Distributed Computing, Computer Graphics, System Programming, Numerical Analysis, Programming Principles, Computer Architecture, Program Development, Machine Learning

    Build toward a degree

    2.8
    Rating, 2.8 out of 5 stars
    ·
    187 reviews

    Intermediate · Specialization · 3 - 6 Months

  • 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, Computer Programming Tools, Applied Mathematics, Computer Graphics, Web Design and Development, Software Development Tools, Cybersecurity, Computational Thinking, Computer Programming, General Mathematics, Problem Solving

    Build toward a degree

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

    Beginner · Specialization · 1 - 3 Months

What brings you to Coursera today?

  • 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, Combinatorics, Computational Logic, Deductive Reasoning, Cryptography, Probability, Computational Thinking, Encryption, Probability Distribution, Network Analysis, Public Key Cryptography Standards (PKCS), Theoretical Computer Science, Bayesian Statistics, Python Programming, Data Structures, Cybersecurity, Algorithms, Arithmetic, Visualization (Computer Graphics)

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

    Beginner · Specialization · 3 - 6 Months

  • Next level skills. New Year savings.

    Save on Coursera Plus
  • Status: Free Trial
    Free Trial
    U

    University of California San Diego

    Data Structures and Algorithms

    Skills you'll gain: Data Structures, Graph Theory, Algorithms, Program Development, Bioinformatics, Data Storage, Development Testing, Theoretical Computer Science, Computational Thinking, Network Analysis, Test Case, Programming Principles, Computer Programming, Python Programming, C and C++, Java, Rust (Programming Language), Javascript, 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 Colorado Boulder

    Foundations of Data Structures and Algorithms

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

    Build toward a degree

    4.6
    Rating, 4.6 out of 5 stars
    ·
    911 reviews

    Advanced · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    S

    Stanford University

    Algorithms

    Skills you'll gain: Data Structures, Graph Theory, Algorithms, Bioinformatics, Theoretical Computer Science, Network Model, Programming Principles, Social Network Analysis, Network Analysis, Computational Thinking, Analysis, Computer Science, 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 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, Computer Networking, File Systems, Apache Hadoop, Cloud Applications, Apache Mahout, Cloud Computing Architecture, Infrastructure As A Service (IaaS), Data Storage, Middleware, Software-Defined Networking, NoSQL

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

    Intermediate · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    U

    University of Glasgow

    Computational Thinking with JavaScript

    Skills you'll gain: Computational Thinking, JSON, Application Deployment, Data Structures, Javascript, Application Development, HTML and CSS, Data Analysis, Code Review, Software Development, Data Visualization Software, Web Development, Web Applications, Scripting, Prototyping, Data Processing, Programming Principles, Unsupervised Learning, Front-End Web Development, Computer Programming

    Beginner · Specialization · 3 - 6 Months

What brings you to Coursera today?

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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 Methods: University of California, Davis
  • Computational Social Science: University of California, Davis
  • Bachelor of Science in Computer Science: University of London
  • GPU Programming: Johns Hopkins University
  • Introduction to Computer Science and Programming: University of London
  • Introduction to Discrete Mathematics for Computer Science: University of California San Diego
  • Data Structures and Algorithms: University of California San Diego
  • Foundations of Data Structures and Algorithms: University of Colorado Boulder
  • Algorithms: Stanford University

Frequently Asked Questions about Computational Science

Computational science is an interdisciplinary field that uses computational methods and simulations to solve complex scientific problems. It integrates principles from mathematics, computer science, and domain-specific knowledge to analyze and model systems across various disciplines, including physics, biology, and social sciences. The importance of computational science lies in its ability to handle large datasets and perform simulations that would be impractical or impossible to conduct in the real world. This capability enables researchers and professionals to make informed decisions, predict outcomes, and innovate solutions in a rapidly evolving technological landscape.‎

Careers in computational science are diverse and can be found in various sectors, including academia, industry, and government. Some potential job titles include computational scientist, data analyst, research scientist, software engineer, and bioinformatician. These roles often involve developing algorithms, creating simulations, and analyzing data to support research or business objectives. As organizations increasingly rely on data-driven decision-making, the demand for professionals skilled in computational science continues to grow, offering numerous opportunities for those looking to enter this field.‎

To succeed in computational science, you will need a blend of technical and analytical skills. Key competencies include programming languages such as Python, R, or C++, as well as a solid understanding of algorithms and data structures. Familiarity with statistical methods and data analysis techniques is also crucial. Additionally, knowledge of specific domain areas, such as biology or physics, can enhance your ability to apply computational methods effectively. Developing problem-solving skills and the ability to work collaboratively in interdisciplinary teams will further empower you in this field.‎

There are many online courses available to help you learn computational science. Some notable options include the Computational Social Science Specialization and the Introduction to Computational Statistics for Data Scientists Specialization. These courses cover essential concepts and practical applications, allowing you to build a strong foundation in computational science. Exploring various offerings can help you find the right fit for your learning style and career goals.‎

Yes. You can start learning computational science on Coursera for free in two ways:

  1. Preview the first module of many computational science courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

If you want to keep learning, earn a certificate in computational science, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

To learn computational science, start by identifying your areas of interest and the specific skills you want to develop. Enroll in online courses that align with your goals, such as those focusing on programming, data analysis, or specific applications in fields like biology or social science. Engage with practical projects to apply your knowledge and reinforce your learning. Additionally, consider joining online communities or forums where you can connect with others in the field, share insights, and seek guidance as you progress.‎

Computational science courses typically cover a range of topics, including programming fundamentals, algorithms, data structures, statistical methods, and simulation techniques. You may also encounter specialized subjects such as computational biology, computational physics, and data visualization. These courses often include hands-on projects that allow you to apply theoretical concepts to real-world problems, enhancing your understanding and skill set in computational science.‎

For training and upskilling employees in computational science, courses like the IBM Data Science Professional Certificate and the Fractal Data Science Professional Certificate can be particularly beneficial. These programs are designed to equip learners with practical skills and knowledge that can be directly applied in the workplace. By focusing on real-world applications and industry-relevant tools, these courses help enhance the capabilities of the workforce in computational science.‎

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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