This course hub website contains OER/ZTC (Open Educational Resources/Zero Textbook Cost) resources for faculty teaching Multimedia Project Lab (MMP 460)at the Borough of Manhattan Community College (BMCC). These resources are freely available for use by BMCC faculty and beyond.
This course introduces students to the use of computers and other information …
This course introduces students to the use of computers and other information systems and technologies to solve problems in organizations. Topics include management information systems (MIS), hardware and software concepts, and organization of information using systems analysis and design, electronic commerce, and contemporary applications of technology in organizational environments. Students will explore ethical perspectives and globalization issues and will cultivate an awareness of emerging processes. Working individually and in groups, students will apply their knowledge through writing assignments, conducting information and organizational analyses and developing, where appropriate, applications using widely used spreadsheets, data presentation, and database management software.
In-depth study of tools and techniques for designing dynamic and interactive multimedia …
In-depth study of tools and techniques for designing dynamic and interactive multimedia systems for use in live performance situations. Emphasis will be on student creation of custom computer software to realize interactive projects. Video, audio, three-dimensional computer images, and alternative computer-human interfaces will be addressed. Extensive instruction in graphical computer programming; no experience required.
Introduction to unit on Social Networking, Blogging and Artificial Intelligence in CUNY …
Introduction to unit on Social Networking, Blogging and Artificial Intelligence in CUNY SPS COM 110: Digital Literacy, partially adapted from a video by Wendy Williams
This is a self-contained course in data science and machine learning using …
This is a self-contained course in data science and machine learning using R. It covers philosophy of modeling with data, prediction via linear models, machine learning including support vector machines and random forests, probability estimation and asymmetric costs using logistic regression and probit regression, underfitting vs. overfitting, model validation, handling missingness and much more. There is formal instruction of data manipulation using dplyr and data.table, visualization using ggplot2 and statistical computing.
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