ENVENG 6220
Transcript Abbreviation:
Data Analy EnvEng
Course Description:
Application of programming and statistical methods for engineering data analysis. Will explore distribution, variance, and multivariate methods. Will provide a deeper understanding of analysis theories in the space, time, and spectral domain. Students will develop computer programming toolboxes and theoretical skills for analyzing and modeling data in their own research.
Course Levels:
Graduate (5000-8000 level)
Designation:
Elective
General Education Course:
(N/A)
Cross-Listings:
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
3.00
Select if Repeatable:
Off
Maximum Repeatable Credits:
(N/A)
Total Completions Allowed:
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
12 weeks (summer only)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: Stat 3450, 3460, 3470, or CivilEn 2050, or equiv; and Grad standing in the Civil Engineering or Environmental Science Graduate programs.
Electronically Enforced:
No
Exclusions:
(N/A)
Course Goals / Objectives:
Understand the theoretical background underlying statistical and data analysis techniques
Understand the assumptions and validity conditions for statistical tests and data analysis techniques
Employ software for statistics and data analysis
Understand methods and techniques to work with spatial data and time series
Understand methods and techniques for dealing with large data sets
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC out-of-class | REC in-class | Weekly LAB out-of-class | Weekly LAB in-class |
|---|---|---|---|---|---|
| Introductory Data Analysis (Linear Algebra; Statistical Measures; Multivariable Probability Densities; Correlation & Covariance; Random Variables; Data Exploration & Distributions; Normality and Outliers; Transformations; Regression) | 8.0 | 0.0 | 0 | 8.0 | 0 |
| Univariate/Multivariate Analysis (ANOVA/MANOVA; Non-Parametric Statistics; Factor Analysis and PCA; Discriminant Analysis; Point Estimation & Uncertainty) | 8.0 | 0.0 | 0 | 8.0 | 0 |
| Time Series Analysis (Time & Frequency Domain Models; Stationarity; Auto-Regression Models; Spectral Analysis and Coherence; Trend Analysis and Significance; Estimating errors in time series reconstruction) | 6.0 | 0.0 | 0 | 6.0 | 0 |
| Forecasting and Extrapolation (Statistically Optimal Linear Estimators; Regression models; Space and time models; Multivariate regression models; Covariance models) | 6.0 | 0.0 | 0 | 6.0 | 0 |
| Total | 28 | 0 | 0 | 28 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Lab
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework/Labs | 30% |
| Project and Presentation | 40% |
| Midterm/Final | 30% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Geostatistics for Natural Resources | Pierre Goovaerts | |
| Computer-Aided Multivariate Analysis | A.A. Afifi and V. Clark | |
| Environmental Data Analysis with MatLab | W. Menke and J. Menke | |
| Matlab Software | ||
| JMP Software |
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
(N/A)
Embedded Literacies Info: