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Über dieses Buch

This book will help future scientists to become more intelligent users of computing technology in their practice of science. The content is suitable for introductory courses on the foundations of computing and the specific application of computers in different areas of science. The text presents a set of modules for use in existing science courses in order to integrate individual aspects of computational thinking, as well as a set of modules introducing the computer science concepts needed to understand the computing involved. These modules guide science students in their independent learning. The book covers computing applications in such diverse areas as bioinformatics, chemical kinetics, hydrogeological modeling, and mechanics of materials, geographic information systems, flow analysis, the solving of equations, curve fitting, optimization, and scientific data acquisition. The computing topics covered include simulations, errors, data representation, algorithms, XMS, compression, databases, performance, and complexity.

Inhaltsverzeichnis

Frontmatter

1. Introduction to Computational Science

After completing this module, a student should be able to:Describe an example of a computational science simulation model.Define computational science, model, simulation, visualization, validation, verification.Appreciate the need to determine the reliability of simulation model results.List three sources of error in simulation model results.Appreciate the value of computational science.

Kevin Brewer, Cathy Bareiss

2. Types of Visualization and Modeling

After completing this module, a student should be able to:Describe the conditions under which computer models may not be completely reliable.Compare and contrast two different types of modeling: agent-based and dynamic systems modeling.

Kevin Brewer, Cathy Bareiss

3. Data Types: Representation, Abstraction, Limitations

After completing this module, a student should be able to:Define the different basic data types (i.e. integer, floating point, and character) and the implications and possible errors of each.Define different sources of error (i.e. measurement, representation, round off, overflow, underflow, and interpretation).Appreciate the issues associated with data storage (i.e. including space requirements, magnitude of size, compression).Apply abstraction to data in the areas of trees, arrays, linked lists, and graphs.

Kevin Brewer, Cathy Bareiss

4. Scientific Data Acquisition

After completing this module, a student should be able to:Define the terms calibration, precision, accuracy, instrument drift, and resolution.Appreciate the difficulty of scientific measurements using instrumentation.Construct and execute an experimental procedure that uses instrumentation.

Kevin Brewer, Cathy Bareiss

5. Procedures: Algorithms and Abstraction

After completing this module, a student should be able to:Read and understand simple NetLogo models.Make changes to NetLogo procedures and predict the effect on the simulation.Name the control structures sufficient to express all components of programs.

Kevin Brewer, Cathy Bareiss

6. Solving Equations

After completing this module, a student should be able to:Describe an analytical solution to an equation.Describe a numerical solution to an equation.Give examples of when numerical solutions are needed or preferred.Describe limitations of iterative numerical solutions.Appreciate the need to validate numerical solutions.List sources of error in numerical solutions.

Kevin Brewer, Cathy Bareiss

7. Iterative Solutions

Create formulas to model data.Organize and format data and calculationsUse formatting tools to improve data readability and visualization.Install and use the Excel Goal Seek to identify a solution for a complex model or an optimal parameter for a design or analysis.Install and use the Excel Solver to identify a solution for a complex model or an optimal parameter for a design or analysis.

Kevin Brewer, Cathy Bareiss

8. Solving Sets of Equations

After completing this module, a student should be able to:Describe a boundary value problem.Define Finite Difference, boundary condition, domain, iteration, convergence.Describe the two most common boundary conditions.Appreciate how numerical modeling software works.Construct a simple numerical model using Microsoft Excel® to solve a Laplacian system.

Kevin Brewer, Cathy Bareiss

9. Procedures: Performance and Complexity

After completing this module, a student should be able to:List computing environment components affecting performance.List and rank the standard Big-Oh computational complexity functions.Determine the time complexity function of an algorithm.Classify an algorithm by its standard Big-Oh function.

Kevin Brewer, Cathy Bareiss

10. Self-Defining Data: Compression, XML and Databases

Appreciate the power of self-defining data.Know when to use different types of compression.Read simple XML documents.Make simple queries in a relational database.Appreciate the demands of efficiency on a DBMS.Understand the purpose of data warehousing and mining.

Kevin Brewer, Cathy Bareiss

11. Searching

After completing this module a student should be able to:Describe a heuristic searchDefine definitions listed in the definitions sectionImplement a Basic Local Alignment Search Tool (BLAST) algorithm.Appreciate the limitations of manual execution of algorithms for analysis of large amounts of dataAppreciate the need for appropriate software tools to conduct complex/large analysisAppreciate reasons for using a fast but less sensitive algorithm in some scenariosList two other uses of heuristic search techniques in scientific investigation

Kevin Brewer, Cathy Bareiss

12. Curve Fitting

After completing this module, a student should be able to:Find an equation to approximate a set of data pointsDetermine the error of such an equationDefine splinesDescribe limitations of curve fitting methods

Kevin Brewer, Cathy Bareiss

13. Optimization

After completing this module, a student should be able to:Describe the components of an optimization problem.Define Objective Function.Appreciate the Simulated Annealing and Genetic Algorithm techniques.Construct an Objective Function with parameters and constraints for a system that needs the best solution known.

Kevin Brewer, Cathy Bareiss

14. Data Organization and Analysis

After completing this module, a student should be able toDescribe an example of a spatial analysis problem.Define table, key field, record, attribute, query, join, cardinality, projection.Appreciate how GIS software can support spatial analysis.Construct correct attribute queries using SQL statements.

Kevin Brewer, Cathy Bareiss

Backmatter

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