Official Course
Description: MCCCD Approval:
5262009 

ECE102 2011 Fall  9999 
L+L
2.0 Credit(s) 4.0 Period(s) 3.4 Load Acad 

Engineering
Analysis Tools and Techniques 

Learning
culture of engineering, engineering use of computer tools, and computer
modeling as applied to engineering analysis and design. Prerequisites: Two years of high school
algebra or MAT122 or departmental approval. Corequisites: MAT151 or MAT182 or MAT187. Course Attribute(s): 

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MCCCD
Official Course Competencies: 



ECE102 2011
Fall  9999 
Engineering Analysis Tools and Techniques 
1.

Contrast cooperative and competitive learning
environments. (I) 
2.

Use basic social and communication skills in a group
setting. (I) 
3.

Demonstrate
selfevaluation of progress through developmental assessment techniques, such
as student learning journals, checksheets, or portfolios. (I) 
4.

Define functions and expressions using
engineering/mathematical modeling software. (II) 
5.

Plot two and
threedimensional 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 threedimensional 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 designoriented problem. (III) 
24.

Build and apply a stochastic computer model to the
solution of a designoriented 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) 
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MCCCD
Official Course Outline: 



ECE102 2011
Fall  9999 
Engineering Analysis Tools and Techniques 



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. Selfassessment
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 

