Cathy Bareiss

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.

Concise Guide to Computing Foundations

ion 1: Electrical Signals to 0’s and 1’s Currently, all computers are based on electrical signals (with some optical signals used between computers). While there are ideas about using other forms such as DNA (Sample and correspondent 2007) and organic molecules (“Scientists Create Organic ‘Molecular Computer’” 2016), these are primarily only in the research stage. Thus we will look only at electrical computers. Further, when dealing with electricity and the computers, the standard way is to think of it in one of two stages: on or off. But, just like a ceiling fan can have four different speeds (off, slow, medium, fast), we are not limited to on/off when working with computers. Some computers have been made/proposed that have more than two values (Neeley et al. 2009), but all modern computers are based solely on the on/off principle (called binary) – which will be the focus of this module. Because of the inconsistency of electricity at low powers and the rapid changes used in computing, we can’t use just on and off. Instead, one range of voltage will be used for off and a second range will be used for on. One common standard is for off (0) to be between 0 and 0.4 volts and on (1) to be between 2.4 and 5 volts. Other standards exist for different uses, but each will use a range with varying voltage gaps to ensure reliable differentiation between 0’s and 1’s. 22 3 Data Types: Representation, Abstraction, Limitations

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.

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.

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.

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.

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.

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.

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.

Data Organization and Analysis

After completing this module, a student should be able to Describe 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.