Scientific methods in computer science
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Scientific Method

Scientific Method
How related to Computer Science?
Modeling
Theoretical Computer Science
Experimental Computer Science
Computer Simulation

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Scientific methods in computer science Presentation Transcript

  • 1. 1 By : Parinda Rajapaksha Samudra Herath Isuri Udayangi Najini Harischandra
  • 2. Roadmap  Introduction  Scientific Method  How related to Computer Science?  Modeling  Theoretical Computer Science  Experimental Computer Science  Computer Simulation  Pros & Cons 2
  • 3. What is Science ?  A systematic and logical approach to discovering how things in the universe work.  It aims for measurable results through testing and analysis.  It is not meant to prove theories, but rule out alternative explanations until a likely conclusion is reached 3
  • 4. What is Science Cont…  Science consists simply of the formulation and testing of hypotheses based on observational evidence.  Science is useful and ongoing. 4
  • 5. How related to Computer Science?  Study of phenomena related to computers.  Computing encompasses, - Computer Science - Computer Engineering - Software Engineering - Information Systems  The purpose of Computing is the systematic study of algorithmic processes that describe and transform information their theory, analysis, design, efficiency and implementation 5
  • 6. Scientific Method  In 19th century.  scientific method is the logical scheme used by scientists searching for answers to the questions  It is used to produce scientific theories..  When conducting a research, scientists observe the scientific method to collect measurable, empirical evidence in an experiment related to a hypothesis. 6
  • 7. Scientific Method Cont… The steps of the scientific method : 1. Pose the question in the context of existing knowledge (theory & observations) 2. Formulate a hypothesis as a tentative answer 3. Deduce consequences and make predictions 4. Test the hypothesis in a specific experiment/theory field •In case the hypothesis leads to contradictions and demands a radical change in the existing theoretical background, it has to be tested carefully 7
  • 8. Scientific Method Cont… Rule: • loop 2-3-4 is repeated with modifications of the hypothesis until the agreement is obtained, which leads to 5. • If major discrepancies are found the process must start from the beginning, 1. 5. When consistency is obtained the hypothesis becomes a theory and provides a coherent set of propositions that define a new class of phenomena or a new theoretical concept 6. A theory is then becoming a framework within which observations/theoretical facts are explained and predictions are made 8
  • 9. Scientific Method Cont… 9
  • 10. Scientific Method Cont… Some key underpinnings to the scientific method:  The hypothesis must be testable and falsifiable  Deductive reasoning is the process of using true premises to reach a logical true conclusion  dependent variable and an independent variable  experimental group and a control group. 10
  • 11. What is Computer Science? 11
  • 12. Many definitions  Study of algorithmic processes that describe and transform information  Study of phenomena related to computers  Study of information structures  Study and management of complexity  Mechanization of abstraction 12
  • 13. Mixture of  Engineering  Mathematics  Logic  Management Generally CS is, Information theory concerned on transformation and interpretation of information 13
  • 14.  Computer science encompasses abstract mathematical thinking and includes an element of engineering.  Finding solutions  Designing skills 14
  • 15. Sub-areas of Computer Science 1. Discrete Structures 2. Programming Fundamentals 3. Algorithms and Complexity 4. Programming Languages 5. Architecture and Organization 6. Operating Systems & etc.. 15
  • 16. List expands as computer science develops.. 16
  • 17.  CS Objectives change with time  Development of theories  Practical experience in usage 17
  • 18. Scientific methods of computer science Computer Science Theoretical Experimental Simulation 18
  • 19. Common Method Modeling 19
  • 20. Modeling  Occur in Science  Simplify a phenomenon  Identify what is relevant  Theoretical background 20
  • 21. Simplified model of a phenomenon Description in symbolic language Observable/measurable consequence of a given change in a system 21
  • 22. Question that come in the process  How to model?  Is the model appropriate?  In what way model differs from “reality”?  Validation: are the results valid? 22
  • 23. Examples 23
  • 24.  Modeling process scheme follows the general scheme of scientific method presented before  Theory, experiment and simulation are all about models of phenomena. 24
  • 25. What is theoretical computer Science?  Subset of general computer science and mathematics  focus on more abstract or mathematical aspects of computing  Includes the theory of computation  Follows a very classical methodology of building theories with rigid definitions of  Objects  operations 25
  • 26. Key recurring ideas of computing  Conceptual and formal models  Different levels of abstraction  Efficiency 26
  • 27. Data models  Use to formulate different mathematical concepts  CS data model – two aspects  Values they can assume  Operations on data 27
  • 28. Typical data model examples  Tree data model  List data model  Set data model  Relational data model  Graph data model  Patterns, automata and regular expression 28
  • 29. Physical science and computer science  Do not compete with each other on which better explains the fundamental nature of information  No new theories develop to reconcile theory with experimental results reveal unexpected phenomena  No history of critical experiments that decide the validity of various theories 29
  • 30. Design and analysis  Methods are developed for algorithm design  Measures are defined for computational resources  Trade offs are explored  Upper and lower resource bounds are proved 30
  • 31. Main methodological themes  Iteration – performing sequence of operations repeatedly  Iterative constructs such as for /while statements  Recursion – call themselves directly or indirectly  Induction – definitions and proofs use basis and inductive step to encompass all possible cases. 31
  • 32. Experimental Computer Science 32
  • 33. What is experimental computer science?  Three components define experimental science  Observation  Hypothesis testing  Reproducibility 33
  • 34.  Experimental computer science  Mathematical modeling of the behavior of computer systems 34
  • 35. Fields of computer science use experiments  Search  Automatic theorem proving  Planning  NP complete problems  Natural language  Vision  Games  Machine learning 35
  • 36. Computer Simulation 36
  • 37.  computation which comprises computer - based modeling and simulation, has become the third research methodology within CS  Computational Science has emerged, at the intersection of Computer Science, applied mathematics, and science disciplines in both theoretical investigation and experimentation Computational Science 37
  • 38. Computational Science Cont… Tools  modeling with 3D visualization and computer simulation  efficient handling of large data sets  ability to access a variety of distributed resources  collaborate with other experts over the Internet 38
  • 39. Computational Science Cont…  Computational science involves the use of computers (''supercomputers'') for visualization and simulation of complex and large-scale phenomena.  If Computer Science has its basis in computability theory, then computational science has its basis in computer simulation 39
  • 40. Computer Simulation  Definition simulation: (computer science) the technique of representing the real world by a computer program; "a simulation should imitate the internal processes and not merely the results of the thing being simulated“  Computer simulation makes it possible to  investigate regimes that are beyond current experimental capabilities  study phenomena that cannot be replicated in laboratories, such as the evolution of the universe and Nano technology 40
  • 41. Simulations 41
  • 42. Key Areas  Chaos and Complex Systems  Virtual Reality  Artificial Life  Physically Based Modeling and Computer Animation 42
  • 43. Advantages and Disadvantages  Advantage  You can test in many different ways, and the more times you test, the more accurate your results will be  Disadvantage  You can come up with different results which can disprove your hypothesis, and this leads to inconsistent conclusions 43
  • 44. Wrap-Up  Introduction  Scientific Method  How related to Computer Science?  Modeling  Theoretical Computer Science  Experimental Computer Science  Computer Simulation  Pros & Cons 44
  • 45. References 1. Some definitions of Science : http://www.gly.uga.edu/railsback/1122sciencedefns.html 2. Computing as a Discipline, Denning, P.J. et al. Commun. ACM 32, 1 (January 1989), 9 3. What is computer science ? : http://www.cs.mtu.edu/~john/whatiscs.html 45