Minor in Applied Statistics & Data Science
From political briefings to genomic sequences to historical maps, data is present in just about every discipline. The minor in Applied Statistics and Data Science (ASDS) shows students how to identify and apply data to deepen their understanding of the analytic aspects of their major. At the same time, students develop some of the 21st century’s most in-demand job skills. The ability to find and evaluate data from diverse sources — and assess the integrity of those sources — drives strategic decision making and problem solving at a high organizational level in every profession.
Applied Statistics and Data Science Minor Requirements
Course Prerequisite
MA 170 Applied Statistics none
EC 272 Advanced Applied Statistics* MA 170
EC 365 Econometrics – take Sophomore year MA 170, EC 272
MA 380 Data Mining MA 170, EC 272, EC 365
MA 385 Machine Learning MA 170, EC 272, EC 365, MA 380
MA 480 ASDS Capstone MA 170, EC 272, EC 365, MA 380, MA 385
* Or MA 206, MA 275, MA 285, PS 206, EC 271
If you entered QU prior to the MA 170 or EC 272 requirement, (for example you took MA 206 or EC 271) see Prof. Jill Shahverdian in Math Department to discuss alternatives to the first two requirements.
For more information about the ASDS Minor, contact Jesse Kalinowski at jesse.kalinowski@quinnipiac.edu
Why minor in Applied Statistics and Data Science (ASDS)?
The minor in Applied Statistics and Data Science (ASDS) is designed to develop students into critical consumers of data, who can work with data and present data in a way that tells a story, enabling effective decision making and problem solving. Students who complete this minor will have a toolkit that they can use in their daily lives, and can be applied to any field of study or career path.
From natural language processing to textual analysis, from genomic sequences to historical maps, data flow through every major. The ASDS minor will allow students to take advantage of these data to deepen their understanding of the analytic aspects of their area of study, or perhaps see it from a completely different perspective, all while developing some of the 21st century’s most in-demand job skills.
Curriculum
Students will complete six 3-credit courses for a total of 18 credits.
- MA 170 Probability and Data Analysis
- EC 271 Applied Statistics (or MA 206 or MA 275 or MA 285 or PS 206)
- EC 365 Econometrics
- MA 380 Data Mining
- MA 385 Machine Learning
- MA 480 Applied Statistics and Data Science Capstone
