What courses earn college credit?
The Institute for Statistics Education offers 29 courses that are eligible for college credit through ACE CREDIT^{®} (the American Council on Education’s College Credit Recommendation Service).
The ACE CREDIT College and University Network is a group of more than 2,200 higher education institutions that consider ACE credit recommendations for transfer to degree programs.
The following courses are eligible for college credit recommendation:
Course 
Methods of Assessment 
Credit Assessment 
The methods of assessment are quizzes, weekly homework assignments and a final examination, with a minimum passing score of 70 percent. 
In the lowerdivision baccalaureate/ associate degree category, 3 semester hours in statistics. 

Biostatistics for Credit 
The methods of assessment are quizzes, weekly homework assignments and a final examination with a minimum passing score of 70 percent. 
In the upperdivision baccalaureate degree category, 3 semester hours in biostatistics. 
The methods of assessment include quizzes, case studies, and a final project, with a minimum passing score of 70 percent.  In the upperdivision baccalaureate degree category, 3 semester hours in computer science, machine learning or artificial intelligence.  
The methods of assessment are quizzes, case studies and weekly homework assignments with a minimum passing score of 70 percent. 
In the upper division baccalaureate degree category, 3 semester hours in statistics. 

The methods of assessment are weekly assignments and a final project with a minimum passing score of 70 percent.  In the upperdivision baccalaureate degree category, 3 semester hours in statistics or data mining.  
Customer Analytics in R  The methods of assessment are quizzes and case studies with a minimum passing score of 70 percent.  In the upperdivision baccalaureate degree category, 3 semester hours in statistics, data mining, or programming. 
Deep Learning 
The methods of assessment include quizzes and case studies, with a minimum passing score of 70 percent. 
In the upperdivision baccalaureate degree category, 3 semester hours in statistics, data mining or computer science. 
The methods of assessment include quizzes, case studies and a final project, with a minimum passing score of 70 percent.  In the upperdivision baccalaureate degree category, 3 semester hours in financial risk management, financial econometrics or applied statistics.  
The methods of assessment are homework assignments and a final project with a minimum passing score of 70 percent.  In the lowerdivision baccalaureate/associate degree category, 3 semester hours in forecasting analytics, data mining, or data science.  
The methods of assessment are quizzes, weekly homework assignments and a final project with a minimum passing score of 70 percent.  In the upperdivision baccalaureate degree category, 2 semester hours in computer science, computer information systems, or information technology.  
Integer & Nonlinear Programming and Network Flow  The methods of assessment are weekly homework assignments with a minimum passing score of 70 percent.  In the upper division baccalaureate degree category, 3 semester hours in operation research, computer programming, statistics or computer information systems. 
Matrix Algebra Review  The methods of assessment are quizzes and case studies with a minimum passing score of 70 percent.  In the lowerdivision baccalaureate/associate degree category, 3 semester hours in mathematics. 
The methods of assessment are weekly homework assignments, class posts, and short answer questions with a minimum passing score of 70 percent.  In the upperdivision baccalaureate degree category, 2 semester hours in computer science, computer information systems, or cyber security.  
The methods of assessment are weekly homework assignments and a final project with a minimum passing score of 70 percent.  In the upperdivision baccalaureate degree category, 3 semester hours in computer science or network analysis.  
Optimization – Linear Programming  The method of assessment is weekly homework assignments with a minimum passing score of70 percent.  In the upperdivision baccalaureate degree category, 3 semester hours in mathematics or business management. 
The methods of assessment include quizzes, case studies, and a final project, with a minimum passing score of 70 percent.  In the upperdivision baccalaureate/associate degree category, 3 semester hours in computer science or data mining.  
The methods of assessment are weekly homework assignments, a final project, and class posts with a minimum passing score of 70 percent.  In the upperdivision baccalaureate degree category, 3 semester hours in predictive analytics, data mining, or business analytics.  
The methods of assessment are weekly homework assignments, a final project, and multiplechoice exams with a minimum passing score of 70 percent.  In the upperdivision baccalaureate degree category, 3 semester hours in business analytics, predictive analytics, business analytics or data mining.  
Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules 
The methods of assessment are a final project, multiplechoice examinations, and class posts with a minimum passing score of 70 percent.  In the upperdivision baccalaureate degree category, 3 semester hours in business analytics, predictive analytics, or data mining. 
The methods of assessment are weekly homework assignments and a final project with a minimum passing score of 70 percent.  In the lowerdivision baccalaureate/ associate degree category, 3 semester hours in computer information systems, statistics, or programming.  
The methods of assessment are weekly homework assignments with a minimum passing score of 70 percent.  In the lowerdivision baccalaureate/ associate degree category, 3 semester hours in computer programming, computer science, or information systems.  
The methods of assessment are weekly homework assignments with a minimum passing score of 70 percent.  In the lowerdivision baccalaureate/ associate degree category, 3 semester hours in computer programming, computer science, or information systems.  
The methods of assessment are weekly homework assignments and a final exam with a minimum passing score of 70 percent.  In the upperdivision baccalaureate degree category, 3 semester hours in computer science, computer programming, information systems or statistics.  
The methods of assessment are quizzes, a final project, and weekly homework assignments with a minimum passing score of 80 percent. 
In the graduate degree category, 3 semester hours in statistics or applied regression analysis. 

The methods of assessment are quizzes and weekly homework assignments with a minimum passing score of 80 percent. 
In the graduate degree category, 3 semester hours in statistics or decision science. 

The methods of assessment are weekly homework assignments and a final project with a minimum passing score of 70 percent.  In the upperdivision baccalaureate degree category, 3 semester hours in spatial statistics, geospatial analysis or statistics.  
The methods of assessment are weekly homework assignments with a minimum passing score of 70 percent.  In the upperdivision baccalaureate degree category, 3 semester hours in SQL queries, computer science or information systems.  
The methods of assessment are weekly homework assignments and a final project with a minimum passing score of 80 percent.  In the graduate degree category, 3 semester hours in statistics or advanced biostatistics.  
The methods of assessment are weekly homework assignments with a minimum passing score of 70 percent.  In the lowerdivision baccalaureate/associate degree category, 3 semester hours in computer science, information systems or cyber security. 
 Biostatistics 1 – For Medical Science and Public Health
 Biostatistics 2 – For Medical Science and Public Health
 Biostatistics for College Credit
 Categorical Data Analysis
 Cluster Analysis
 Financial Risk Modeling
 Forecasting Analytics
 Interactive Data Visualization
 Introduction to Network Analysis
 Logistic Regression
 Introduction to Statistics for College Credit
 Natural Language Processing
 Optimization – Linear Programming
 Predictive Analytics 1 – Machine Learning Tools
 Predictive Analytics 1 – Machine Learning Tools – with Python
 Predictive Analytics 1 – Machine Learning Tools – with R
 Predictive Analytics 2 – Neural Nets and Regression
 Predictive Analytics 2 – Neural Nets and Regression – with R
 Predictive Analytics 2 – Neural Nets and Regression with Python
 Predictive Analytics 3 – Dimension Reduction, Clustering and Association Rules
 Predictive Analytics 3 – Dimension Reduction, Clustering and Association Rules – with R
 Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules with Python
 Python for Analytics
 R Programming – Intermediate
 R Programming – Introduction 1
 R Programming – Introduction 2
 Regression Analysis
 Risk Simulation and Queuing
 Sentiment Analysis
 Spatial Statistics for GIS – Using R
 Statistics 1 – Probability and Study Design
 Statistics 2 – Inference and Association
 SQL – Introduction to Database Queries
 Survival Analysis
 Text Mining Using Python