Course Description
Through a series of case studies, students learn to leverage data analysis to drive strategic decisions via the telling of a compelling, evidence-based story.
Additional Requirements for Graduate Students:
Graduate students will design, conduct, and deliver an
additional solo project for which the student identifies the
question, locates and prepares the data, conducts the analysis,
and delivers a written report of the methods, results, and
implications.
Athena Title
Cons An Evidence Based Strateg
Equivalent Courses
Not open to students with credit in FHCE 4000S or FHCE 6000S
Undergraduate Pre or Corequisite
STAT 2000 or STAT 2000E or STAT 2010 or STAT 2100H or MSIT 3000 or MSIT 3000E or MSIT 3000H or BUSN 3000 or BUSN 3000E or BUSN 3000H
Semester Course Offered
Offered fall and spring
Grading System
A - F (Traditional)
Course Objectives
- To move from an organizational need to the right question(s) and analytical plan to support the need - To find, extract, organize, and describe the data required - To develop spreadsheet models to analyze data, evaluate risk, and optimize strategic decisions - To identify, quantify, and interpret relationships between variables - To craft effective, fact-based stories that inspire conversation and action - To present and justify a course of action to those who can take action
Topical Outline
Introduction to the Process Case 1: Learning the Process Getting to the Question Preparing the Data Commands and Methods Descriptive Statistics Bivariate Analysis (Crosstabs, Correlations, T-Tests) Multivariate Analysis (Regression) Storytelling Case 2: Mastering the Process Getting to the Question Commands and Methods Descriptive Statistics Bivariate Analysis (Crosstabs, Correlations, T-Tests) Multivariate Analysis (Regression) Storytelling Case 3: Flying Solo Getting to the Question Preparing the Data Commands and Methods Descriptive Statistics Bivariate Analysis (Crosstabs, Correlations, T-Tests) Multivariate Analysis (Regression) Storytelling Case 4: Expert Storytelling The Language of Data Science
Syllabus