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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: Free Trial
      Free Trial
      G

      Google

      Crash Course on Python

      Skills you'll gain: Programming Principles, Python Programming, Computer Programming, Computational Thinking, Algorithms, Problem Management, Data Structures, Integrated Development Environments, Debugging, Development Environment

      4.8
      Rating, 4.8 out of 5 stars
      ·
      39K reviews

      Beginner · Course · 1 - 3 Months

    • U

      University of Michigan

      Problem Solving Using Computational Thinking

      Skills you'll gain: Computational Thinking, Programming Principles, Computer Science, Disaster Recovery, Algorithms, Design Thinking, Simulations

      4.6
      Rating, 4.6 out of 5 stars
      ·
      1.3K reviews

      Beginner · Course · 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, Engineering Software, Computer Programming, Software Engineering, Algorithms, Debugging, Program Development, Database Systems, Database Theory, Network Routing, Theoretical Computer Science, Data Storage

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

      Intermediate · Specialization · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      I

      IBM

      Introduction to Data Science

      Skills you'll gain: SQL, Jupyter, Data Literacy, Data Mining, Peer Review, Data Modeling, Relational Databases, Stored Procedure, Databases, Data Science, Big Data, Computer Programming Tools, Query Languages, Data Cleansing, GitHub, Business Analysis, Cloud Computing, Data Analysis, Digital Transformation, R Programming

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      99K reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      I

      IBM

      Python Project for Data Science

      Skills you'll gain: Dashboard, Pandas (Python Package), Data Visualization Software, Web Scraping, Jupyter, Matplotlib, Data Analysis, Data Science, Data Processing, Data Manipulation, Python Programming, Data Collection

      4.5
      Rating, 4.5 out of 5 stars
      ·
      4.6K reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      G

      Google

      Data Analysis with R Programming

      Skills you'll gain: Rmarkdown, Ggplot2, R Programming, Data Analysis, Tidyverse (R Package), Statistical Programming, Data Visualization Software, Data Cleansing, Data Manipulation, Exploratory Data Analysis, Data Import/Export, Package and Software Management, Data Structures

      4.8
      Rating, 4.8 out of 5 stars
      ·
      11K reviews

      Beginner · Course · 1 - 3 Months

    • N

      Nanyang Technological University, Singapore

      Introduction to Forensic Science

      Skills you'll gain: Criminal Investigation and Forensics, Scientific Methods, Chemistry, Investigation, Biochemistry, Pharmacology, Laboratory Testing, Pathology, Microbiology

      4.9
      Rating, 4.9 out of 5 stars
      ·
      2.6K reviews

      Mixed · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      I

      IBM

      Data Science Fundamentals with Python and SQL

      Skills you'll gain: Dashboard, SQL, Descriptive Statistics, Jupyter, Statistical Analysis, Data Analysis, Probability Distribution, Pandas (Python Package), Data Visualization Software, Statistics, Data Visualization, Web Scraping, Relational Databases, Stored Procedure, Databases, Computer Programming Tools, Automation, Data Science, GitHub, Python Programming

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      71K reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: Free
      Free
      P

      Princeton University

      Computer Science: Algorithms, Theory, and Machines

      Skills you'll gain: Theoretical Computer Science, Data Structures, Computer Science, Computer Architecture, Algorithms, Programming Principles, Computational Logic, Computational Thinking, Java Programming, Computer Hardware

      4.7
      Rating, 4.7 out of 5 stars
      ·
      700 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      G

      Google

      Google Advanced Data Analytics

      Skills you'll gain: Exploratory Data Analysis, Data Storytelling, Statistical Hypothesis Testing, Data Ethics, Data Visualization Software, Sampling (Statistics), Data Presentation, Regression Analysis, Feature Engineering, Data Transformation, Descriptive Statistics, Professional Networking, Data Visualization, Tableau Software, Data Manipulation, Statistical Analysis, Statistical Machine Learning, Object Oriented Programming (OOP), Data Analysis, Interviewing Skills

      Build toward a degree

      4.7
      Rating, 4.7 out of 5 stars
      ·
      6.3K reviews

      Advanced · Professional Certificate · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      I
      U
      I

      Multiple educators

      Data Science Foundations

      Skills you'll gain: Dashboard, Pseudocode, Jupyter, Algorithms, Data Literacy, Data Mining, Pandas (Python Package), Data Visualization Software, Correlation Analysis, Web Scraping, NumPy, Probability & Statistics, Predictive Modeling, Big Data, Computer Programming Tools, Automation, Data Collection, Data Science, Machine Learning Algorithms, Unsupervised Learning

      4.6
      Rating, 4.6 out of 5 stars
      ·
      113K reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Data Science

      Skills you'll gain: Shiny (R Package), Rmarkdown, Exploratory Data Analysis, Regression Analysis, Leaflet (Software), Version Control, Statistical Analysis, R Programming, Data Manipulation, Data Cleansing, Data Science, Statistical Inference, Statistical Hypothesis Testing, Predictive Modeling, Data Wrangling, Data Visualization, Plotly, Machine Learning Algorithms, Plot (Graphics), Knitr

      4.5
      Rating, 4.5 out of 5 stars
      ·
      51K reviews

      Beginner · Specialization · 3 - 6 Months

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    1234…614

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

    • Crash Course on Python: Google
    • Problem Solving Using Computational Thinking: University of Michigan
    • Accelerated Computer Science Fundamentals: University of Illinois Urbana-Champaign
    • Introduction to Data Science: IBM
    • Python Project for Data Science: IBM
    • Data Analysis with R Programming: Google
    • Introduction to Forensic Science: Nanyang Technological University, Singapore
    • Data Science Fundamentals with Python and SQL: IBM
    • Computer Science: Algorithms, Theory, and Machines: Princeton University
    • Google Advanced Data Analytics: Google

    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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