

Course ID:  GEOG 4300/6300. 3 hours. 
Course Title:  Data Science in Geography 
Course Description:  Descriptive and inferential techniques used in quantitative geographic analysis. Probability distributions, sampling techniques, parametric and nonparametric inference, analysis of variance, spatial autocorrelation measures and regression procedures. Applications of statistical methods to spatial analysis and geographic research design. Exercises develop knowledge of statistical programming with computer software. 
Oasis Title:  Data Science in Geography 
Semester Course Offered:  Offered fall and spring semester every year. 
Grading System:  AF (Traditional) 

Course Objectives:  Learning Objectives:
1. Understand basic concepts and methods of data collection and description
2. Understand basic concepts of mathematical probability theory
3. Understand the logic and practice of simple inferential statistics
4. Understand correlation and regression analysis
5. Ability to use appropriate statistical techniques in tackling geographical
problems.
6. Ability to comprehend academic literature that uses statistical techniques.
This course meets the following General Education Abilities by accomplishing the
specific learning objectives listed below:
Communicate effectively through writing. This is met by a series of writing
assignments associated with supplemental reading and data analysis.
Computer Literacy is addressed through course administration, studentfaculty
electronic interaction, data analysis activities and assignments, and exposure to
GIS technologies.
Critical Thinking is central to the learning objectives of this class, and is
developed through homework assignments, lecture, classroom discussion, and inquiry
based learning efforts.
Moral Reasoning (Ethics) is addressed by consideration of ethical guidelines for
use of statistical data and for interpretation and application of spatial
statistical outcomes. Moral reasoning is developed through lectures, writing
assignments, classroom discussion, and inquirybased learning activities. 
Topical Outline:  Geographical Data and Measurement
Geography, Data, and Statistics (Chapter 1 & 2)
Descriptive Statistics
NonSpatial Descriptive Statistics (Chapter 3)
Spatial Descriptive Statistics (Chapter 4)
Statistical Relationships Between Variables
Correlation (Chapter 13)
Regression (Ch 14, pp. 210220)
Building Blocks of Inferential Statistics
Probability Theory & Distributions (Chapter 5)
Sampling (Chapter 6)
Estimation (Chapter 7)
Statistical Inference
Basic Parametric Hypothesis Testing (Chapter 8)
Multiple Sample Parametric Tests (Chapter 9)
ANOVA (Chapter 10)
GoodnessofFit, Categorical Tests 
Course ID:GEOG 4300/6300. 3 hours.
Course Title:Data Science in GeographyCourse
Description:Descriptive and inferential techniques used in quantitative geographic analysis. Probability distributions, sampling techniques, parametric and nonparametric inference, analysis of variance, spatial autocorrelation measures and regression procedures. Applications of statistical methods to spatial analysis and geographic research design. Exercises develop knowledge of statistical programming with computer software.
Oasis Title:Data Science in Geography
Semester Course
Offered:Offered fall and spring semester every year.
Grading System:AF (Traditional)
Course Objectives:Learning Objectives:
1. Understand basic concepts and methods of data collection and description
2. Understand basic concepts of mathematical probability theory
3. Understand the logic and practice of simple inferential statistics
4. Understand correlation and regression analysis
5. Ability to use appropriate statistical techniques in tackling geographical
problems.
6. Ability to comprehend academic literature that uses statistical techniques.
This course meets the following General Education Abilities by accomplishing the
specific learning objectives listed below:
Communicate effectively through writing. This is met by a series of writing
assignments associated with supplemental reading and data analysis.
Computer Literacy is addressed through course administration, studentfaculty
electronic interaction, data analysis activities and assignments, and exposure to
GIS technologies.
Critical Thinking is central to the learning objectives of this class, and is
developed through homework assignments, lecture, classroom discussion, and inquiry
based learning efforts.
Moral Reasoning (Ethics) is addressed by consideration of ethical guidelines for
use of statistical data and for interpretation and application of spatial
statistical outcomes. Moral reasoning is developed through lectures, writing
assignments, classroom discussion, and inquirybased learning activities.
Topical Outline:Geographical Data and Measurement
Geography, Data, and Statistics (Chapter 1 & 2)
Descriptive Statistics
NonSpatial Descriptive Statistics (Chapter 3)
Spatial Descriptive Statistics (Chapter 4)
Statistical Relationships Between Variables
Correlation (Chapter 13)
Regression (Ch 14, pp. 210220)
Building Blocks of Inferential Statistics
Probability Theory & Distributions (Chapter 5)
Sampling (Chapter 6)
Estimation (Chapter 7)
Statistical Inference
Basic Parametric Hypothesis Testing (Chapter 8)
Multiple Sample Parametric Tests (Chapter 9)
ANOVA (Chapter 10)
GoodnessofFit, Categorical Tests
Syllabus: