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Data Analytics

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Data Analytics

The Data Analytics microcredential is designed to help students develop expertise in data processing, prediction-making, and proficient communication within the field of data analytics. The curriculum covers crucial areas such as Applied Probability and Statistics, Experimental Design, and Statistical Data Mining. This microcredential ensures that individuals gain a practical understanding of quantitative techniques, strategies, and tools essential for effective data analytics. Students will learn statistical skills and applications for real-world scenarios, enhancing their capabilities for data-driven decision-making in both educational and professional settings.

Admission requirements for application:

For Non-matriculated students:

  • Completion of the non-matriculated student application
  • MTH 130: Calculus I with Applications or MTH 150: Calculus I or Permission of the Department Chair
  • Completion of the Microcredential Application on Etrieve (Instructions will be emailed after completing the non-matriculated student application)

Requirements to earn the microcredential:

  • To achieve the Data Analytics microcredential, students will complete 3 required courses totaling 9 credits. This coursework will include MTH 360: Applied Probability and Statistics (3 credits), MTH 380: Experimental Design (3 credits), and MTH 420: Statistical Data Mining (3 credits). Students must meet the prerequisites for each of the courses and earn a grade of B or better in MTH 360, MTH 380, and MTH 420 (pre-requisites listed below).
  • Pre-requisite coursework for the microcredential includes: MTH 151 or MTH 236 as a pre-requisite for MTH 360, MTH 220 or BUS 240 or MTH 360 or permission from the department as a pre-requisite for MTH 380, and MTH 360 or permission from the department as a pre-requisite for MTH 420.
  • Students will be required to work on a project in MTH 420. Students are also required to write a report and present their results to the department at the end of the semester.

Time to complete:

3 semesters

Cost to attend:

Standard tuition rates apply. For tuition and student consumer information, please click here.

Contact Information


Whitman Hall, Room 180

Required Coursework (3 course, 9 credits)
MTH 360: Applied Probability and Statistics 3 Credits
MTH 380: Experimental Desig 3 Credits
MTH 420: Statistical Data Mining 3 Credits

MTH 360 Applied Probability and Statistics

In this course, we study applications of probability distributions and statistical inference. Topics are chosen from statistical parameters, continuous and discrete random variables, probability and sampling distributions, confidence intervals, hypothesis testing, regression analysis, and analysis of variance. Prerequisite(s): MTH 130 or MTH 150

MTH 380 Experimental Design

This course will provide an overview of the practical and theoretical foundations of experimental design as applied in real-life situations. Topics discussed include ANOVA, randomized block design, Latin square design, factorial and fractional factorial designs, and response surface methods. Prerequisite(s): MTH 110 or BUS 240 or MTH 360 or permission from the department

MTH 420 Statistical Data Mining

This course provides an introduction to statistical learning techniques designed for the analysis of high-dimensional data. Topics covered include techniques for exploring and visualizing data, general linear models and generalized linear models, classification, model assessment, decision trees, and principal components analysis. Prerequisite(s): MTH 360 or permission from the department

Last Modified 5/21/24