Applied Data-Driven Methods Graduate Certificate

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The Applied Data-Driven Methods (ADDM) Graduate Certificate is a 12-credit graduate certificate designed to develop a computationally- and data-oriented foundation that dovetails into expertise developed during the student’s undergraduate studies, on-the-job training, or subsequent/concurrent coursework.  In particular, this certificate engages with the following five concept areas:

• Computational foundations:  This concept area includes topics such as basic abstractions, algorithmic thinking, programming concepts, data structures, and simulations. 
• Data Management and Curation: This concept area includes topics such as data provenance, data preparation/cleansing/transformation, data management (of a variety of data types), record retention policies, data subject privacy, missing and conflicting data, and modern databases.
• Data Description and Visualization: This concept area includes topics such as data consistency checking, exploratory data analysis, grammar of graphics, attractive and sound static and dynamic visualizations, and dashboards
• Data Modeling and Assessment: This concept area includes topics such as machine learning, multivariate modeling and supervised learning, dimension reduction techniques and unsupervised learning, deep learning, model assessment and sensitivity analysis, and model interpretation.
• Workflow and Reproducibility: This concept area includes topics such as workflows and workflow systems, documentation and code standards, source code (version) control systems, reproducible analysis, and collaboration. 

The ADDM program consists of four 3-credit courses.  These courses consist of an introductory course and 3 core courses.  The core courses are structured such that the introductory course provides all necessary background for the other core courses.  An introductory course in statistics is recommended but not required for students pursuing this graduate certificate.  No prior programming experience is assumed.

It is expected that students will complete this certificate over the course of one calendar year, typically in three semesters. The recommended sequence starts in the Fall Semester and completes the following Summer Semester.

This graduate certificate is designed to be stackable towards graduate programs with an emphasis on topics related to data science.  In some situations, this certificate may serve as a gateway that opens students to the possibility of completing further study in a related MS or PhD program, while in others such as our MLIS program, this coursework could even count towards such a degree.

Admissions Requirements

To be considered for admissions to this graduate certificate program, students are expected to meet the following requirement:

• Have obtained a Bachelor’s degree with a B average (a grade point average of 3.00 on a 4.00 scale) or better in the total undergraduate program.

It is strongly recommended that applicants have completed an introductory statistics course with a grade of C or higher.  This course should cover topics including measures of central tendency and variability, regression, correlation, non-parametric analysis, probability and sampling, Bayesian analysis, significance tests, and hypothesis testing. Example courses at the University of Pittsburgh include STAT 0200: Basic Applied Statistics, and STAT 1000: Applied Statistical Methods

No prior programming experience is required for admission to this graduate certificate program.

This program is now offered as an online option.