Data Science Certificate

Dr. Matt Jones
Department of Mathematics and Statistics
Professor
Program Coordinator: Computer Science and Quantitative Methods Predictive Analytics Concentration
Location: Maynard Mathematics and Computer Science Building, Room 236
Phone: 931-221-7814
Email: j onesmatt@apsu.edu
Website: www.apsu.edu/mathematics

Dr. Jackie Vogel
Chair, Department of Mathematics and Statistics
Professor
Location: Maynard Mathematics and Computer Science Building, Room 204
Phone: 931-221-7313
Email: v ogelj@apsu.edu
Website: www.apsu.edu/mathematics

Listed below are the required application materials and the associated scores (if necessary) for each.

Admissions Requirements

Application for admission
Application Fee: $45 domestic or $55 international
Official transcripts from all colleges attended
Verification of lawful presence in the United States (i.e. submit a license or other approved document)
Bachelor’s degree, conferred in any field, from accredited institutions indicating a cumulative undergraduate GPA of at least 2.5
Military documents (if applicable)
Admission decision by department

For more information about the application process, please visit apsu.edu/grad-studies.

Program CIP Code

06.11.0802.11

Program Modality

On-Ground
Online

Program Student Learning Outcomes

Students will develop skills to study real world problems and then design and implement appropriate solutions.
Students will develop the ability to create appropriate data management plans in scientific, business, government, or industrial contexts.
Students will develop the ability to design experiments for the purpose of assessing meaningful causal inferences.
Students will develop skills to apply and evaluate modern model selection methods for the purpose of describing relationships between variables and making predictions.
Students will develop the ability to identify appropriate data structures, and statistical techniques for problems involving large data sets including Big Data.
Students will develop programming skills to use data analysis functions and libraries in practical data science problems using modern software.
Students will develop skills to use data mining methods including data integration, transformation, visualization, classification, and clustering in real world projects.
Students will develop the ability to draw appropriate conclusions and to recommend decisions based on analysis of data while recognizing the effect of assumptions and the limitations of the analysis.
Students will develop the ability to draw distinctions between statistically significant differences and practical ones, and to make appropriate decisions based on these conclusions.
Students will develop the ability to anticipate and identify sources of response bias and variance, and to interpret these sources in practically meaningful ways.

Curriculum

Data Science