Onyejekwe, Osita Eluemuno

courses taught

APPM 1360 - Calculus 2 for Engineers
Primary Instructor - Spring 2020 / Fall 2020 / Summer 2021 / Fall 2021 / Summer 2022 / Summer 2023
Continuation of APPM 1350. Focuses on applications of the definite integral, methods of integration, improper integrals, Taylor's theorem, and infinite series. Degree credit not granted for this course and MATH 2300.
APPM 2360 - Introduction to Differential Equations with Linear Algebra
Primary Instructor - Summer 2020 / Summer 2021 / Summer 2022
Introduces ordinary differential equations, systems of linear equations, matrices, determinants, vector spaces, linear transformations, and systems of linear differential equations. Credit not granted for this course and both MATH 2130 and MATH 3430.
CSCI 2820 - Linear Algebra with Computer Science Applications
Primary Instructor - Summer 2023 / Spring 2024 / Summer 2024
Introduces the fundamentals of linear algebra in the context of computer science applications. Includes vector spaces, matrices, linear systems, and eigenvalues. Includes the basics of floating point computation and numerical linear algebra. Same as CSPB 2820.
CSCI 3022 - Introduction to Data Science with Probability and Statistics
Primary Instructor - Summer 2023 / Summer 2024
Introduces students to the tools methods and theory behind extracting insights from data. Covers algorithms of cleaning and munging data, probability theory and common distributions, statistical simulation, drawing inferences from data, and basic statistical modeling. Same as CSPB 3022.
CSCI 3656 - Numerical Computation
Primary Instructor - Spring 2024
Covers development, computer implementation, and analysis of numerical methods for applied mathematical problems. Explores topics such as floating point arithmetic, numerical solution of linear systems of equations, root finding, numerical interpolation, differentiation, and integration.

CSCI 5502 - Data Mining
Primary Instructor - Fall 2023
Introduces basic data mining concepts and techniques for discovering interesting patterns hidden in large-scale data sets, focusing on issues relating to effectiveness and efficiency. Topics covered include data preprocessing, data warehouse, association, classification, clustering, and mining specific data types such as time-series, social networks, multimedia, and Web data. Same as CSCI 4502.

CSCI 5622 - Machine Learning
Primary Instructor - Spring 2023 / Spring 2024
Trains students to build computer systems that learn from experience. Includes the three main subfields: supervised learning, reinforcement learning and unsupervised learning. Emphasizes practical and theoretical understanding of the most widely used algorithms (neural networks, decision trees, support vector machines, Q-learning). Covers connections to data mining and statistical modeling. A strong foundation in probability, statistics, multivariate calculus, and linear algebra is highly recommended.

DTSA 5020 - Statistical Learning for Data Science: Regression and Classification
Primary Instructor - Spring 2023 / Summer 2023 / Fall 2023 / Summer 2024 / Fall 2024
Consists of the foundational framework & application of simple and multiple linear regression and classification methods.

DTSA 5021 - Statistical Learning for Data Science: Resampling, Selection and Splines
Primary Instructor - Summer 2023 / Fall 2023 / Spring 2024 / Summer 2024 / Fall 2024
Consists of the foundational framework & application of cross-validation, bootstrapping, dimensionality reduction, ridge regression, lasso, GAMs and splines.

DTSA 5022 - Statistical Learning for Data Science: Trees, SVM and Unsupervised Learning
Primary Instructor - Summer 2023 / Fall 2023 / Spring 2024 / Summer 2024 / Fall 2024
Consists of the foundational framework & application of tree-based methods, support vector machines, and unsupervised learning.

DTSA 5707 - Deep Learning Applications for Computer Vision
Primary Instructor - Spring 2023 / Summer 2023 / Fall 2023 / Spring 2024 / Summer 2024
Students will learn about Computer Vision as a field of study and research. They explore several Computer Vision tasks and suggested approaches, from the classic Computer Vision perspective. They'll be introduced to Deep Learning methods and apply them to some of the same problems. They will analyze the results and discuss advantages and drawbacks of both types of methods. Examples of Computer Vision tasks where Deep Learning can be applied include: image classification, image classification with localization, object detection, object segmentation, facial recognition, and activity or pose estimation. Same as CSCA 5812.

DTSA 5740 - Global Climate Change Policies and Analysis
Primary Instructor - Summer 2024 / Fall 2024
This course explores and critically analyzes historical and contemporary climate policies (e.g. Kyoto Protocol and the Paris Agreement). Political issues pertaining to energy sources, such as nuclear energy, will be reviewed. The course will focus on understanding key climate principles and terms surrounding policy development, specifically for low-income or developing countries/communities. Further, this course explores up-to-date technologies that are used in climate analysis. This course also introduces the Python programming language.

DTSA 5741 - Modeling Climate Anomalies with Statistical Analysis
Primary Instructor - Summer 2024 / Fall 2024
This course introduces the use of statistical analysis in Python programming to study and model climate data, specifically with the SciPy and NumPy package. Topics include data visualization, predictive model development, simple linear regression, multivariate linear regression, multivariate linear regression with interaction, and logistic regression. Strong emphasis will be placed on gathering and analyzing climate data with the Python programming language. Recommended prerequisite: DTSA 5740 - Global Climate Change Policies and Analysis.

DTSA 5742 - Predicting Extreme Climate Behavior with Machine Learning
Primary Instructor - Fall 2024
This course reviews current global climate policies with the goal of gathering data and applying machine learning algorithms to predict extreme climate behaviors, specifically in developing countries. Topics include multivariate linear regression, time-series analysis, and numerical weather prediction. The use of monte carlo simulations to forecast extreme weather events will be analyzed. Strong emphasis will be placed on application in the Python programming language. Recommended prerequisites: DTSA 5740 and DTSA 5741.

INFO 4652 - Statistical Programming in R
Primary Instructor - Fall 2022
This intensive course covers foundational data science tools and techniques in the R programming language, including acquiring, cleaning, exploring, and analyzing data, programming, and conducting reproducible research. The course will emphasize the use of data management best practices such as the tidyverse toolkit in R. Same as INFO 5652.

INFO 5652 - Statistical Programming in R
Primary Instructor - Fall 2022
This intensive course covers foundational data science tools and techniques in the R programming language, including acquiring, cleaning, exploring, and analyzing data, programming, and conducting reproducible research. The course will emphasize the use of data management best practices such as the tidyverse toolkit in R. Same as INFO 4652.

MATH 1012 - Quantitative Reasoning and Mathematical Skills
Primary Instructor - Fall 2018
Promotes mathematical literacy among liberal arts students. Teaches basic mathematics, logic, and problem-solving skills in the context of higher level mathematics, science, technology, and/or society. This is not a traditional math class, but is designed to stimulate interest in and appreciation of mathematics and quantitative reasoning as valuable tools for comprehending the world in which we live. Degree credit not granted for this course and MATH 1112.

MATH 2300 - Calculus 2
Primary Instructor - Fall 2019
Continuation of MATH 1300. Topics include transcendental functions, methods of integration, polar coordinates, differential equations, improper integrals, infinite sequences and series, Taylor polynomials and Taylor series. Department enforced prerequisite: MATH 1300 or MATH 1310 or APPM 1345 or APPM 1350 (minimum grade C-). Degree credit not granted for this course and APPM 1360.

MATH 2510 - Introduction to Statistics
Primary Instructor - Fall 2018 / Spring 2019 / Summer 2019 / Fall 2019
Elementary statistical measures. Introduces statistical distributions, statistical inference, hypothesis testing and linear regression. Department enforced prerequisite: two years of high school algebra.

MATH 3430 - Ordinary Differential Equations
Primary Instructor - Spring 2019
Involves an elementary systematic introduction to first-order scalar differential equations, nth order linear differential equations, and n-dimensional linear systems of first-order differential equations. Additional topics are chosen from equations with regular singular points, Laplace transforms, phase plane techniques, basic existence and uniqueness and numerical solutions. Formerly MATH 4430.

STAT 2600 - Introduction to Data Science
Primary Instructor - Spring 2021 / Fall 2021 / Spring 2022
Introduces students to importing, tidying, exploring, visualizing, summarizing, and modeling data and then communicating the results of these analyses to answer relevant questions and make decisions. Students will learn how to program in R using reproducible workflows. During weekly lab sessions students will collaborate with their teammates to pose and answer questions using real-world datasets.

STAT 3400 - Applied Regression
Primary Instructor - Spring 2020 / Fall 2020 / Spring 2021 / Fall 2021 / Spring 2022
Introduces methods, theory, and applications of linear statistical models, covering topics such as estimation, residual diagnostics, goodness of fit, transformations, and various strategies for variable selection and model comparison. Examples will be demonstrated using statistical programming language R.

STAT 4010 - Statistical Methods and Applications II
Primary Instructor - Fall 2024
Expands upon statistical techniques introduced in STAT 4000. Topics include modern regression analysis, analysis of variance (ANOVA), experimental design, nonparametric methods, and an introduction to Bayesian data analysis. Considerable emphasis on application in the R programming language. Same as STAT 5010.

STAT 5010 - Statistical Methods and Applications II
Primary Instructor - Fall 2022 / Spring 2023 / Fall 2023 / Fall 2024
Expands upon statistical techniques introduced in STAT 4000. Topics include modern regression analysis, analysis of variance (ANOVA), experimental design, nonparametric methods, and an introduction to Bayesian data analysis. Considerable emphasis on application in the R programming language. Same as STAT 4010.

STAT 5600 - Methods in Statistical Learning
Primary Instructor - Fall 2022 / Fall 2024
Provides an introduction to methods in the field of statistical learning. Topics include a review of multiple regression, assessing model accuracy, classification, resampling methods, model selection and regularization, nonlinear regression, tree-based methods, support vector machines and unsupervised learning. Involves hands-on data analysis using the R programming language.

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