Applied Statistics – Master’s

• Convenience: Get to next at your pace. The program can be completed in under a year and a half. But you have many options to make the program fit your needs. You can begin taking classes in fall, spring, or summer. Also, you can move through the program by taking one course at a time. If needed, you can even take a semester off to address other obligations.
• Flexibility: Balance your studies with your career and personal life while advancing your skills.
• Experiential Learning Credits: If you are working full time, you can fulfill the work experience requirement with your employer. Or you can complete an internship or other work experience opportunities through an Iowa State consulting group. Paid internships are often available, enhancing graduates’ value in the job market across various industries.

• Intentionally Designed Courses: Learn more from professors who have worked closely with a dedicated group of instructional designers to create the best possible online learning experience for students.
• Integrated Practical Experience: Master the essentials. The curriculum focuses on the use of statistics in modern statistical computing techniques.
• Wide Variety of Training: Round out your skills. You will receive a broad education in the foundations of statistics, a comprehensive set of statistical methods, and statistical computing to prepare for problem-solving across multiple industries.
• Comprehensive Curriculum: Over the course of this program, you will:
  • correctly use and interpret the most commonly used classical and modern methods in statistics
  • explain the theoretical foundations of classical and modern statistical methods
  • identify relevant methods for a particular data-driven problem and describe advantages and disadvantages of each method
  • effectively communicate statistical results to all stakeholders, including non-statisticians
  • successfully complete work experience in statistics

This is a sample program for a full-time student. However, there are many flexible options to fit your needs.

• Semester 1 (Fall): 11 credits

Statistical Computing for Data Analysis – 3 credits
Statistical Methods for Data Analysis – 4 credits
Statistical Theory for Data Analysis – 4 credits

• Semester 2 (Spring): 9 credits

Time Series Data Analysis – 3 credits
Bayesian Data Analysis – 3 credits
Multivariate Data Analysis – 3 credits

• Semester 3 (Summer): 4 credits

Principles of Statistical Consulting – 2 credits
Experiential Learning in Applied Statistics – 2 credits

• Semester 4 (Fall): 6 credits

Experimental Design and Data Analysis – 3 credits
Statistical Machine Learning and Artificial Intelligence – 3 credits