Data AnalyticsData AnalyticsThe 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:
Requirements to earn the microcredential:
Time to complete:3 semesters Cost to attend:Standard tuition rates apply. For tuition and student consumer information, please click here. Contact InformationMATHEMATICSWhitman Hall, Room 180
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 |
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