"Leaders in business, education and government must take action to foster a new generation of talent with the technical expertise and unique ideas to make the most of this tsunami of Big Data."

-Richard Rodts, Manager of Global Academic Programs, IBM

Level 2 Course Offerings

INFERENTIAL STATISTICS

 

OPRE 207. Statistics for Business and Management Science I. 3 Units.

Organizing and summarizing data. Mean, variance, moments. Elementary probability, conditional probability. Commonly encountered distributions including binomial. Poisson, uniform, exponential, normal distributions. Central limit theorem. Sample quantities, empirical distributions. Reference distributions (chi-square, z-, t-, F-distributions). Point and interval estimation: hypothesis tests. Prereq: MATH 122 or MATH 126. 

 

EPBI 431: Statistical Methods in Biological and Medical Sciences I

 

STAT 312: Basic Statistics for Engineering and Science (3)

For advanced undergraduate students in engineering, physical sciences, life sciences. A comprehensive introduction to probability models and statistical methods of analyzing data with the object of formulating statistical models and choosing appropriate methods for inference from experimental and observational data and for testing the model’s validity. A balanced approach with equal emphasis on probability, fundamental concepts of statistics, point and interval estimation, hypothesis testing, analysis of variance, design of experiments, and regression modeling. Note: Credit given for only one (1) of STAT 312, 313, 333, 433. Prereq: MATH 122 or equivalent.

SYBB 310: Healthcare Data Analytics in R

As part of the Data Science Minor, SYBB 310 is designed to introduce students to the basic tools used in data science, focusing on elementary statistics and building up to regression models. In this course, we will provide hands-on training in statistical programming through the use of the open-source statistical computing language, R. Over the semester, students will gain a practical understanding of the essential statistics needed for data science, and students will apply these principles using R to analyze a large dataset of 10,000 patients’ de-identified electronic medical records. No background in statistics or programming is expected for this course. Undergraduate Prerequisites: EECS 131; or EECS 132; or equivalent proficiency

STAT 201R (taught using R statistics software):

Basic Statistics for Social and Life Sciences (3)

Designed for undergraduates in the social sciences and life sciences who need to use statistical techniques in their fields. Descriptive statistics, probability models, sampling distributions. Point and confidence interval estimation, hypothesis testing. Elementary regression and analysis of variance. Not for credit toward major or minor in Statistics. Counts for CAS Quantitative Reasoning Requirement.