The document outlines the relationship between science and computer science, emphasizing the scientific method as a systematic approach for formulating and testing hypotheses. It details the sub-areas of computer science, including theoretical and experimental aspects, and highlights methods such as computer simulation and modeling. Ultimately, it discusses the advantages and disadvantages of these methods in the context of understanding complex phenomena.

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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
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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
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4. What is Science Cont…
 Science consists simply of the formulation and testing of
hypotheses based on observational evidence.
 Science is useful and ongoing.
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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
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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.
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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
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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
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9. Scientific Method Cont…
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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.
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11. What is Computer Science?
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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
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13. Mixture of
 Engineering
 Mathematics
 Logic
 Management
Generally CS is,
Information theory concerned on transformation and
interpretation of information
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14.  Computer science encompasses abstract mathematical
thinking and includes an element of engineering.
 Finding solutions
 Designing skills
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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..
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16. List expands as computer science
develops..
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17.  CS Objectives change with time
 Development of theories
 Practical experience in usage
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18. Scientific methods of computer science
Computer Science
Theoretical Experimental Simulation
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19. Common Method
Modeling
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20. Modeling
 Occur in Science
 Simplify a phenomenon
 Identify what is relevant
 Theoretical background
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21. Simplified model of a phenomenon
Description in
symbolic language
Observable/measurable
consequence of a given
change in a system
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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?
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23. Examples
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24.  Modeling process scheme follows the general scheme of
scientific method presented before
 Theory, experiment and simulation are all about models
of phenomena.
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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
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26. Key recurring ideas of computing
 Conceptual and formal models
 Different levels of abstraction
 Efficiency
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27. Data models
 Use to formulate different mathematical concepts
 CS data model – two aspects
 Values they can assume
 Operations on data
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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
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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
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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
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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.
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32. Experimental Computer Science
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33. What is experimental computer
science?
 Three components define experimental science
 Observation
 Hypothesis testing
 Reproducibility
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34.  Experimental computer science
 Mathematical modeling of the behavior of computer
systems
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35. Fields of computer science use
experiments
 Search
 Automatic theorem proving
 Planning
 NP complete problems
 Natural language
 Vision
 Games
 Machine learning
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36. Computer Simulation
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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
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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
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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
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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
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41. Simulations
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42. Key Areas
 Chaos and Complex Systems
 Virtual Reality
 Artificial Life
 Physically Based Modeling and Computer
Animation
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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
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44. Wrap-Up
 Introduction
 Scientific Method
 How related to Computer Science?
 Modeling
 Theoretical Computer Science
 Experimental Computer Science
 Computer Simulation
 Pros & Cons
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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
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