15 courses with the subject CS, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
2 class hours, 2 credits. General computer literacy, with emphasis on computers in the business environment. Major concepts and recent developments in hardware, operating systems, applications software, database management, and the internet are presented. Societal and ethical concerns, including issues such as cyber security and software piracy, are also considered. [Fall and Spring]
2 laboratory hours, 1 credit. A laboratory course covering word processing, spreadsheets, presentations and graphics using Microsoft Office applications. [Fall and Spring]
3 class hours, 3 credits. An introduction to computer programming in an object-oriented language (such as Python). Topics include: primitive data types and operations; branching and looping; functions; object-oriented programming; functional programming. Other possible topics include data exploration with R. Credit will not be given for both this course and ENGR 120.
3 class hours, 3 credits. An introductory course in obtaining, manipulating, analyzing, and visualizing data. Topics include: Python language basics; importing and exporting various data formats such as JSON, Excel and CSV; Numpy computing library; data processing with Pandas library; webscraping; transforming data; linear regression with sci-kit learn; data visualization.
3 class hours, 3 credits. Techniques and algorithms for organizing and processing data. Data structures considered may include: text and binary files; contiguous and linked lists; stacks and queues; linked lists; trees; graphs. For each data structure, relevant processing algorithms (e.g., for traversing, searching, and sorting) will be considered, including recursive methods. [Spring]
3 class hours, 3 credits. Introduction to Logistic Regression, KNN, Naive Bayes and Decision Trees/Random Forest with Statistics. Project-based. This course cannot be taken with ENGR 396 for credit [Fall] 2024-25 Undergraduate Course 8
3 class hours, 3 credits. Second course on Machine Learning. Includes statistical learning methods such as Hidden Markov Model and MCMC. Unsupervised learning methods and clustering algorithms. Natural language processing using word to vector. With Word2Vec, Sci-Kit Learn. Project-based. [Spring]
3 class hours, 3 credits. Introductory database concepts; the relational model and normalization; use of a relational database management system; SQL; big data and cloud; use of NoSQL system such as MongoDB. [Fall]
3 class hours, 3 credits. Introduction to Neural Network structures and learning algorithms. Construction of various Neural Network models, including CNN, RNN, and LSTM. Methods in dimensionality reduction and feature extraction via unsupervised learning algorithms. [Fall]
3 class hours, 3 credits. Capstone course for applying data science skills to various types of data in respective concentrations. Includes library information literacy session, guest lectures from various departments, review of various data science methods. Project/presentation based. Students must demonstrate skills in data cleaning, data presentation and data analysis, with analytical methods of statistics or machine learning. [Spring]
2 class hours, 2 laboratory hours, 3 credits. An introduction to the design, construction, programming and operation of a micro- computer system; topics include: overall computer organization, CPU group, memory interfacing, assembly language programming, testing and debugging techniques; the initial phases of the design and construction of a microcomputer are included. Prerequisite: ENGR 388. Deleted 4/5/17.
2 class hours, 2 laboratory hours, 3 credits. Continuation of Computer Science 480. The input/output sections of a computer; types of I/0 devices; interfacing and programming; design projects including the design and construction of the I/0 portion of the computer, the interfacing of the display, keyboard and robot arm. Deleted 4/5/17.
1, 2, 3, or 4 credits. Theoretical or project-based independent investigation of special topics in Data Science or Machine Learning. Student work will be under the direct supervision of a mentor approved by the Science Department.
1, 2, 3, or 4 credits. Theoretical or project-based independent investigation of special topics in Data Science or Machine Learning. Student work will be under the direct supervision of a mentor approved by the Science Department.
1, 2, 3 or 4 credits. Significant topics in programming languages, software engineering, cybersecurity and machine learning that reflect the interest of both the student and the instructor. 2024-25 Undergraduate Course 9
Source: SUNY Maritime College's catalog, linked per course · table learning_unit · CourseShelf publish 61
