Learn Computational Science

Basic Principles Programming & Math Statistics & Data Science Machine Learning Computational Modeling

  1. What is Computational Science?
  2. The Scientific Method
  3. Theories, Hypotheses, & Models
  4. Hypothesis Testing & Falsification
  5. Bayesian Inference
  6. Which Model is Best?

  7. Observational Methods
  8. Experimental Methods
  9. Philosophy of Science

  10. Research Ethics
  11. Reproducibility
  12. Open Science
  13. Tools and Software

Basic Principles of Computational Science

  1. What is Computational Science?
  2. A basic overview of computational science, definitions, and an introduction to some critical concepts.

  3. The Scientific Method
  4. The scientific method is a body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge. To be termed scientific, a method of inquiry is commonly based on empirical or measurable evidence subject to specific principles of reasoning.1 In this section, we review the basic principles of the scientific method, and distinguish the scientific method of inquiry from nonscientific methods of inquiry, including discussing limitations of the scientific approach, such as difficulties with objectity and reasoning fallacies that make the scientific method difficult to execute in practice.

  5. Theories, Hypotheses, Models, and Levels of Analysis
  6. What, exactly, is a theory, a model, and a hypothesis, and what is the difference? One confusing issue is that we often are insterested in modeling or simulating a situation, and have many choices about the level of analysis and detail in which we wish to do so.

  7. Hypothesis Testing & Falsification
  8. Scientific reasoning is often taught in terms of the principle of hypothesis testing - verifying the probability that observed data is due to a particular hypothesis being true, compared to a "null" hypothesis that assumes that hypothesis is false. This is often taught in conjunction with the notion of falsificationism - that no theory can ever be proven true, but that some theories (such as null hypotheses) can be proven false. In this section, we review these and related principles of the scientific method.

  9. Bayesian Inference
  10. Bayesian inference is an inference technique where Bayes' theorem is used to update the probability of a hypothesis as one collects or obtains more data. Bayesian inference is becoming a valuable tool for modeling and comparing scientific hypotheses.

  11. Which Model is Best?
  12. How do you compare theories and models? How do you decide which model is best? This can involve practical decision such as evaluating which theories and models make the most precise predictions. But it can also involve higher level decisions about how to compare different theoretical frameworks, often at different levels of analysis and involving different degrees of complexity.

  13. Observational Methods
  14. An overview of methods, principles, and techniques for observational methods in science.

  15. Experimental Methods
  16. An overview of methods, principles, and techniques for experimental methods in science.

  17. Philosophy of Science
  18. A review of important principles and debates in the philosophy of science. Many scientists are making assumptions about the nature of teh world, science, or their theoretical framework, which have important consequences for what they conclude or how in interpret their data.

  19. Research Ethics
  20. Research ethics involves the application of fundamental ethical principles to a variety of topics involving research, including scientific research. These include the design and implementation of research involving human experimentation, animal experimentation, various aspects of academic scandal, including scientific misconduct (such as fraud, fabrication of data and plagiarism), whistleblowing; regulation of research, etc. Research ethics is most developed as a concept in medical research. The key agreement here is the 1964 Declaration of Helsinki. The Nuremberg Code is a former agreement, but with many still important notes. Research in the social sciences presents a different set of issues than those in medical research.2

  21. Reproducibility
  22. Reproducibility is the extent to which the results of a scientific study or experiment can be duplicated. There has recently been increased focus on replications of previous research, and the failure of many foundational studies in biology, neuroscience, and psychology (among other disciplines) has resulted in what some are calling a replication crisis for these fields.3

  23. Open Science
  24. Open science is a movement to make research, data, and dissemination accessible to everyone. It includes a push to make publication and communication of research more open, transparent and accessible, as well as encouraging scientists to practice what is sometimes called "open notebook science", where all data and every step of the data analysis process is publically accessible.4

  25. Tools and Software
  26. Links to helpful software, websites, and resources that are useful for doing computational science.

  1. This text uses material from the Wikipedia article "The Scientific Method", which is released under the Creative Commons Attribution-Share-Alike License 3.0.
  2. This text uses material from the Wikipedia article "Research Ethics", which is released under the Creative Commons Attribution-Share-Alike License 3.0.
  3. This text uses material from the Wikipedia article "Reproducibility", which is released under the Creative Commons Attribution-Share-Alike License 3.0.
  4. This text uses material from the Wikipedia article "Open Science", which is released under the Creative Commons Attribution-Share-Alike License 3.0.
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