Statistics Courses
COS102 Introduction to Computer Programming (3 credits)
This course delves into the fundamentals of computer programming, focusing on programming methodology, procedural abstraction, and an introduction to object-oriented programming using Python. Through a hands-on approach, students will engage in integrated lab sessions during lectures, ensuring practical application of concepts throughout the course. Prerequisite: None
COS321 Database Systems (4 credits)
This course focuses on data management issues in standard relational database systems and on the web. In particular, we will focus on the design and manipulation of data in relational database systems, discussing schema design and refinements, as well as query languages. Then, we will turn towards data management issues in a web context: web-centric data models, XML, Information Retrieval and Web Search. Prerequisite: COS205
COS331 Data Mining (4 credits)
Throughout this course, students will delve into fundamental principles and algorithms essential for extracting actionable insights from raw data. Core topics encompass data preprocessing, exploratory analysis, dimensionality reduction, classification, clustering, association rule mining, and anomaly detection. Engaging with real-world datasets and case studies spanning various domains including business, science, security, and healthcare, students will gain practical experience and insights into the application of these techniques in diverse contexts. Prerequisite: MAT201, COS211, DAS241
COS346 Big Data Engineering (3 credits)
The course provides an in-depth understanding of Big Data Engineering principles, technologies, and tools. Students will learn the fundamentals of handling and processing massive datasets, including storage, retrieval, and analysis techniques. Emphasis will be placed on various frameworks, such as Hadoop, Spark, and associated technologies, to engineer scalable and efficient solutions for real-world data problems. Prerequisite: COS205
COS431 Ethics in Computer and Data Science (3 credits)
This course seeks to orient the student and future technologist with the ethical issues arising from the rapidly increasing role of technology in our lives. In designing systems, developing requirements, and deploying systems technologists need to be thoroughly aware of historical precedent regarding the ethical use of information, but also aware of ethical issues arising daily around us. For example how can a user limit the amount of personal information Google or Facebook collects, shares with others, or sells for a fee? How ethical is it to collect personal information from users/customers and who actually own the rights to that personal information? How do technology companies responsibly pursue the advancement of AI technology and its incorporation into daily life? All these issues and more involve computing, ethics, and the internet. Prerequisite: None
DAS241 Data Visualization (3 credits)
This course introduces students to the principles and techniques of data visualization using the R programming language. Through hands-on projects and theoretical concepts, students will explore various visualization libraries and tools available in R to effectively communicate and analyze data. Prerequisite: COS102, STA101 or COS211
DAS341 Business Data Analysis (3 credits)
The objective of the course is to introduce to the students basic quantitative, mathematical and statistical methods for solving financial, marketing and business problems. Using Excel and Tableau, this course provides an introduction to data analytics for business professionals, including those with no prior analytics experience. Students will learn how data analysts describe, predict, and inform business decisions in the specific areas of marketing, human resources, finance, and operations, and develop the basic data literacy and analytics mindset needed to make appropriate business strategy recommendations based on insights from real-world data. Prerequisite: STA101 or COS211
DAS435 Machine Learning and Artificial Intelligence (4 credits)
This course provides introduction to concepts and theoretical basis of key machine learning algorithms, as well as hands-on experience on machine learning pipelines and working with real-world problems. Some of the machine learning algorithms covered in the course include k-means, support vector machines, naïve Bayes, decision trees, random forests, gradient boosting, ensemble methods, hierarchical clustering, and latent Dirichlet allocation, etc. An introduction to the deep learning algorithms with appropriate use case scenarios will also be covered in the second half of the course. In addition, the course also covers topics on practical machine learning techniques such as model validation and performance evaluation, data augmentation, hyper-parameter tuning, and feature engineering, etc. Prerequisite: COS331
MAT105 Calculus I (4 credits)
This course covers calculus of single-variable functions. Topics include brief review of precalculus, limits, derivatives, integration, and some application of these tools to mathematical (graphing, min/max etc.) or real-world problems. This course is intended for majors in science, engineering, economics, and computer science, among other disciplines. Prerequisite: None
MAT106 Calculus II (4 credits)
This course is a follow-up course of Calculus I. It covers important concepts and techniques that are essential to understand advanced courses on probability or statistics. The topics covered include techniques of integration (Chapter 7), application of integration (Chapter 6 & 8), polar coordinates (Chapter 10), infinite sequences and series (Chapter 11), and Multiple Integrals (Chapter 15). Students are also encouraged to use Python coding to visualize some results in Calculus as bonus projects. This course is intended for majors in data science. Prerequisite: MAT105
MAT201 Linear Algebra (4 credits)
This course is an introductory one designed to equip students with the essential mathematical foundations necessary to understand and implement modern data science techniques. The course explores the core concepts of linear algebra, including vectors, matrices, and their applications in algorithms and models crucial to the big data era. By understanding the geometric and algebraic properties of vectors and matrices, students will gain insights into how linear algebra powers machine learning, image processing, neural networks, and more. Practical examples and applications will be woven throughout the course to demonstrate the real-world utility of these mathematical tools. Prerequisite: None
MAT207 Calculus III (3 credits)
This course is an advanced course in calculus. It includes vectors and vector functions, partial derivatives and differentiability of functions of several variables, multiple integrals. Prerequisite: MAT106
STA101 Introduction to Statistics (3 credits)
This course is an introductory course in statistics intended for students in a wide variety of areas of study. Topics covered include basic descriptive measures (histograms, average, and standard deviation etc.), probability theory, statistical inference, confidence intervals, hypothesis tests and regression with applications in the real world. In addition, students will learn and use statistical programming language R to help understand and perform select statistical analyses. Prerequisite: None
STA202 Introduction to Probability (3 credits)
This course is intended for majors in data science. It provides a systematic introduction to the principles and theories of probability. Prerequisite: MAT106
STA211 Statistical Theory and Methods (3 credits)
This course is intended for majors in data science. It provides a systematic introduction to the principles and techniques of statistics. Prerequisite: STA202
STA331 Applied Regression Analysis (3 credits)
This course is intended for majors in data science. It provides the methods and applications of fitting and interpreting multiple regression models, with emphasis on the analysis of data. Prerequisite: MAT201
STA411 Introduction to Categorical Data Analysis (3 credits)
This is a undergraduate level statistics course. Topics include description and interference using proportions and odd ratios, multi-way contingency tables, logistic regression and other generalized linear models, and log linear models. Prerequisite: STA211
STA421 Design and Analysis of Experiments (3 credits)
This applied course is for students with majors in Data Sciences, Statistics, and Biomedical Sciences. It introduces basic concepts and methods for design and analysis of experiments that commonly arise in clinical trial, public health, industry quality control, agriculture, life sciences, and insurance. Prerequisite: STA211
STA431 Introduction to Multivariate Analysis (3 credits)
The course will cover important statistical methods, relevant theory and applications for analyzing continuous multivariate data. Prerequisite: STA211
STA435 Introduction to Bayesian Analysis (3 credits)
Bayesian statistical methods combine information from similar experiments, account for complex spatial, temporal, and other correlations, and also incorporate prior information or expert knowledge (when available) into a statistical analysis. Prerequisite: STA211
STA441 Survival Analysis (3 credits)
This applied course is for students with majors in Data Sciences, Statistics, and Biomedical Sciences. It introduces basic concepts and methods for analyzing survival time data (time-to-event data) that commonly arise in clinical trial, public health, industry quality control, and insurance. Prerequisite: STA211
STA445 Nonparametric Statistics (3 credits)
This course is an elective course for majors in mathematics/statistics. It introduces basic theory and computing tools for nonparametric statistical methods. Prerequisite: STA211
STA471 Introduction to Linear Models (3 credits)
This course is intended for Bachelor of Science in Statistics program. It provides the fundamental concepts, theories, and applications of linear models in statistics. The primary emphasis is on simple and multiple linear regression analyses. Prerequisite: STA211 & MAT201
STA481 Introduction to Stochastic Processes (3 credits)
This course introduces students to the theory and applications of stochastic processes, with wide-ranging applications in various fields. Topics covered include probability spaces, discrete and continuous-time Markov chains, Poisson processes, Brownian motion, and their practical implementations in fields such as finance, engineering, and biology. Emphasis is placed on developing a practical skills for analyzing and modeling random phenomena. Prerequisite: STA202 & STA211
STA491 Senior Project (4 credits)
This required course serves as the capstone project of all statistics major students seeking a bachelor’s degree. Students will work with a qualified mentor who is either a faculty member or an off-campus supervisor for an individual project to apply their knowledge and gain practical experience in statistical methods in a real-world setting. Prerequisite: Dept Approval