21:219:105
Everyday Data (3)
Every day we share data about ourselves online using social media platforms, and in real life through shopping interactions and surveys. The data that we share creates a narrative of our past, present, and future. Through this course, students understand how our data is being collected, analyzed, and visualized. Students learn the basic principles of data visualization in Python and will be immersed in standard data science practices to learn exploratory data analysis and to effectively communicate findings and solutions.
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21:219:220
Fundamentals of Data Visualization (3)
This course introduces undergraduate students to data visualization. The course is intended to teach students how to create meaningful charts and figures that can simultaneously convey useful information and be pleasing to the eye. Students will learn to use the programming language R to develop graphics. The course is divided into three general themes: 1. Research Methods and Statistics; 2. Programming in R; 3. Generating Meaningful and Insightful Graphics. The course aims to offer an interactive environment where students feel comfortable to generate and share ideas.
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21:219:329
Statistics and Machine Learning (3)
Basic concepts in statistical learning and implementation in Python or R are introduced. Course covers linear regression, logistic regression, ensemble methods, optimization methods for model learning, and various advanced topics such as deep neural networks, kernel learning, and Gaussian processes.
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21:219:330
Ethical Issues in Data Science (3)
This course will prepare students to think critically so they may confront normative questions that will arise in their future work as practicing computer or data scientists. Students will gain a detailed understanding of some of the most important ethical issues relevant to the field of data science. The course will explore questions about the possibility of bias in automated decision-making systems; about what constitutes appropriate collection, aggregation, and use of personal information about the users of technology services; and about collective and individual responsibility for the social impacts of newly developed technologies. The course will use real-life examples to explore questions involving (1) bias in machine learning; (2) conflicts between preserving customer privacy and corporations collecting consumer data; and (3) corporate and individual responsibility for the harms caused by new technologies.
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21:219:400
Deconstructing Machine Learning Bias (3)
This course is designed to use contemporary case studies in algorithmic bias to teach students to identify and deconstruct machine learning (ML) bias. Students will learn how to combine critical reasoning and their understanding of both the modeling process and ML techniques to identify different types of bias, to assess the impact of technical bias on the model (outcome), and discuss the social and economic impact of deploying a biased model. In the second half of the semester, students will apply and critique a statistical-ML solution to mitigate algorithmic bias in a case study dataset.
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21:219:420
Agile IOS Design and Development (3)
This unique hands-on industry-partnership course has been designed to expose students to the end-to-end product (application) design and Agile Scrum development process used by tech companies and entrepreneurs. Students will draft technical documents outlining their design and development process and write code to deliver a functioning application (product) with a data feedback loop for a final grade. All developed products will be presented and used (tested) to top tech recruiters during the Spring 2021 Rutgers-Newark DS Product-Application Show.
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School of Arts and Sciences-Newark 
