Engineering Analysis Tools and Techniques

Course: ECE102First Term: 2019 Fall
Final Term:
Current
Final Term:
2021 Summer |
Lec + Lab 2.0 Credit(s) 4.0 Period(s) 4.0 Load
Credit(s) Period(s)
Load
AcademicLoad Formula: T - Lab Load |

Arizona Shared Unique Number SUN#: EGR 1102 - In combination with: ECE103

MCCCD Official Course Competencies | |||
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1. Contrast cooperative and competitive learning environments. (I)
2. Use basic social and communication skills in a group setting. (I) 3. Demonstrate self-evaluation of progress through developmental assessment techniques, such as student learning journals, check-sheets, or portfolios. (I) 4. Define functions and expressions using engineering/mathematical modeling software. (II) 5. Plot two- and three-dimensional representations of data and functions using engineering/mathematical modeling software. (II) 6. Fit functions to discrete sets of data using engineering/mathematical modeling software. (II) 7. Solve linear and nonlinear equations using engineering/mathematical modeling software. (II) 8. Solve systems of linear and nonlinear equations using engineering/mathematical modeling software. (II) 9. Use programming structures to implement algorithms for computer models. (II) 10. Develop and refine computer models using engineering/mathematical modeling software. (II, III) 11. Describe the structure of a spreadsheet. (II) 12. Use cell references to evaluate expressions in a spreadsheet. (II) 13. Manipulate cells and ranges of cells to construct a spreadsheet. (II) 14. Use conditional structures in the development of a spreadsheet. (II) 15. Develop two- and three-dimensional graphs of data using a spreadsheet. (II) 16. Use graph types to represent different types of data generated with a spreadsheet. (II) 17. Import and export data to and from other computer applications using a spreadsheet. (II) 18. Develop and refine computer models using a spreadsheet. (II, III) 19. Explain what a computer model is and why engineers use computer models. (III) 20. Contrast deterministic and stochastic computer models. (III) 21. Define the term heuristic, and explain how heuristics are used in the modeling process. (III) 22. Describe a sensitivity analysis, and explain how it relates to the modeling process. (III) 23. Build and apply a deterministic computer model to the solution of a design-oriented problem. (III) 24. Build and apply a stochastic computer model to the solution of a design-oriented problem. (III) 25. Explain how probability is used in the development of stochastic computer models. (III) 26. Interpret and analyze the results of computer models. (III) 27. Explain how feasibility constraints are used in the modeling process. (III) 28. Present the results of computer models. (III) | |||

MCCCD Official Course Competencies must be coordinated with the content outline so that each major point in the outline serves
one or more competencies. MCCCD faculty retains authority in determining the pedagogical approach, methodology, content
sequencing, and assessment metrics for student work. Please see individual course syllabi for additional information, including
specific course requirements.
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MCCCD Official Course Outline | |||

I. Learning culture
A. Principles of cooperative learning B. Cooperative learning environments vs. competitive learning environments C. Social skills necessary to be successful in cooperative settings D. Self-assessment techniques II. Engineering tools A. Engineering/mathematical modeling software 1. General syntax and structure 2. Expression syntax 3. Function definition 4. Plotting of functions 5. Solution of a linear equation 6. Solution of a nonlinear equation 7. Solution of systems of linear and nonlinear equations 8. Plotting discrete data sets 9. Fitting linear and nonlinear functions to discrete data sets 10. Algorithmic structure (If, For, While, etc.) 11. Uses in computer modeling B. Spreadsheet 1. General spreadsheet structure 2. Expressions and cell references 3. Manipulation of cells and ranges of cells 4. Conditional structures 5. Graphing sets of data 6. Importing and exporting data 7. Uses in computer modeling III. Computer modeling A. Principles of the modeling process B. Heuristics and how they are used in the modeling process C. Interpretation of results and solutions from computer models D. Stochastic and deterministic computer models E. Organizing and representing data effectively F. Optimization G. Comparing algorithms and effective use of models H. Probability and stochastic modeling I. Knowledge models and their importance J. Modeling examples and case studies | |||

MCCCD Governing Board Approval Date: May 28, 2019 |

All information published is subject to change without notice. Every effort has been made to ensure the accuracy of information presented, but based on the dynamic nature of the
curricular process, course and program information is subject to change in order to reflect the most current information available.