New courses for undergraduate students

March 18, 2020

The STOR Department is pleased to announce new courses coming soon (as early as Fall 2020) as well as some changes in some of the existing courses. Short descriptions of the new courses are provided below but here is a brief overview of the new additions and changes:

STOR 120 (available in Fall 2020) is a new introductory class, which the students will be able to take as an alternative to STOR 155. In other words, STAN majors and minors will be required to take either STOR 120 or STOR 155 and any STOR course that currently requires STOR 155 as a prerequisite will now accept either one of these courses as a prerequisite. Please note however that STOR 120 is a lecture/lab combination courses, with 3 hours lecture and 1 hour lab per week for a total of 4 credits.

Starting with Fall 2020, STOR 320 will be a lecture/lab combination course, with 3 hours lecture and 1 hour lab per week for a total of 4 credits.

STOR 475 (available in Fall 2020) is the permanently numbered and named version of the course that has been taught by Mr. Rick Kelly as STOR 390 for the last two years. The course is primarily geared towards students who are interested in learning more about actuarial science or are already interested in careers in actuarial science. The course will now automatically count as a Group A elective for STAN majors and minors.

STOR 520 (available in Spring 2021) is an advanced version of STOR 320. STOR 520 will be a direct substitute for STOR 320 and students CANNOT take credit for both STOR 320 and STOR 520. Similar to STOR 320, STOR 520 will also be a lecture/lab combination course, with 3 hours lecture and 1 hour lab per week for a total of 4 credits. This course will count as a group A elective for STAN majors and minors.

STOR 535 (available in Fall 2020) is an advanced and more computational version of STOR 435. STOR 535 will be a direct substitute for STOR 435 and students CANNOT take credit for both STOR 435 and STOR 535.

STOR 538 (available in Fall 2020) is the permanently named and numbered version of the Sports Analytics course Prof. Mario Giacomazzo taught as STOR 390 in Fall 2019. The course will now automatically count as a Group A elective for STAN majors and minors.

Finally, STOR115/BIOL115/MATH115/PSYC115 is a new crosslisted introductory course focused on developing reasoning skills with data. The course will not count towards the STAN major or minor.

Below are short descriptions of the courses:

STOR 120 Foundations of Statistics and Data Science

This course combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze those data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis

of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design.

STOR 475 Health Care Analytics

Using real world situations, the course will focus on how mathematics and the principles of risk management are used in managing the cost of health care and help insurance companies and employers make better decisions regarding employee benefit insurance products and programs. An emphasis on group project work will enable students to develop their skills presenting analytical work, better preparing them for their initial position after graduation.

Prerequisite: STOR 435 or MATH 535

STOR 535 Probability for Data Science

This course is an advanced undergraduate course in probability with the aim to give students the technical and computational tools for advanced courses in data analysis and machine learning. It covers random variables, moments, binomial, Poisson, normal and related distributions, generating functions, sums and sequences of random variables, statistical applications, Markov chains, multivariate normal and prediction analytics. Students develop deeper understanding of the standard mathematical theory via simulation and exploration in Python, and thus develop a solid grasp of both the mathematical aspects of probability and its use in applications.

Prerequisite: Math 233

STOR 538 Sports Analytics

This course will survey the history of sports analytics across multiple areas and challenge students in team-based projects to practice sports analytics. Students will learn how applied statistics and mathematics help decision makers gain competitive advantages for on-field performance and off-field business decisions.

The course will begin with an overview of the sports industry, so students understand the value of pursuing a career in sports analytics and the path to attain that career. Following these preliminary lectures, we survey the methodology that lead to key advancements in some of the highest grossing sports in the world. Since domain knowledge is a requirement for analytics, the course will be organized by sport. Per each topic, we will first learn the rules and flow of the sport, then study the history of analytics in the sport, and examine current research to provoke creative thought for future development. Furthermore, students will be challenged in team-based projects to practice sports analytics. This will give students an opportunity to innovate in sports where current research is lacking. High level work on these open-ended projects could immediately lead to internships and jobs across many areas pertaining to data science.

Prerequisite: STOR 320 or STOR 455

STOR 520 Statistical Computing for Data Science

The goal of this course is to advance your understanding of data science through statistical computation in the R programming language. Competency in the visualization and statistical methods discussed will make you immediately marketable across most industries.

This course provides you with hands-on experience working with datasets provided in class and downloaded from certain public websites. Lectures cover basic topics, such as R programming, visualization, data wrangling and cleaning, exploratory data analysis, web scraping, data merging, predictive modeling, and elements of machine learning. Programming analyses will challenge you in more advanced areas of data science and cover visualization of spatio-temporal data (i.e. GIS and remote sensing) and multivariate data, data acquisition using APIs, Monte Carlo simulations, parallel computation, the bootstrap and resampling, machine learning approaches to prediction and classification, cluster analysis, natural language processing, and text mining

Prerequisites: STOR 435 or STOR 535, and STOR 455

STOR 115 Reasoning with Data

Students will use mathematical and statistical methods to address societal problems, make personal decisions, and reason critically about the world. Authentic contexts may include voting, health and risk, digital humanities, finance, and human behavior. This course does not count as credit towards the psychology or neuroscience majors.