10 courses with the subject DS, 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.
DS 501Statistical Methods of Data Analysis3
This course provides a comprehensive overview of the statistical techniques essential for data analysis in a data-driven world. Students will explore both foundational and advanced statistical methods used to extract meaningful insights from complex datasets. Key topics include probability distributions, hypothesis testing, regression analysis, ANOVA, and multivariate analysis. Emphasis will be placed on practical applications using statistical software to analyze real-world data, interpret results, and communicate findings effectively. Through a combination of lectures, hands-on exercises, and projects, students will develop the critical thinking and analytical skills necessary to address diverse challenges in data analytics. Usage of R Studio is required.
This course is designed to introduce students to the basics of programming for data analytics. This course is to be taken by all students in the Master’s in Data Analytics program. Topics covered will include a brief review of standard Python coding, use of the NumPy library and arrays, use of the Pandas library and dataframes, an introduction to plotting and graphing data, and an introduction to the R statistical programming language. Usage of the Python programming language, Jupyter Notebooks, and R Studio is required.
This course in Python programming is designed for students entering the Master’s in Data Science Program, particularly those who do not possess a bachelor’s degree in computer science (or similar field) or do not have a background in computer coding. Topics covered in the course include: variables, data types, conditional statements, loops, dictionaries, lists, functions, and an overview of object-oriented programming. Additionally, discussion of extendable program libraries is presented. Usage of the Python programming language and Anaconda Distribution is required.
This course introduces students to data analytics techniques and their applications in business using the R programming language. Students will learn to clean, analyze, and visualize data to derive business insights. The course covers statistical concepts, data manipulation, and exploratory data analysis. Furthermore, advanced modelling techniques such as clustering and time series analysis will be presented. Usage of the R programming language is required.
This course is designed to equip students with advanced data manipulation skills using Python and the Pandas library. Students will learn data cleaning techniques, including handling missing data, duplicates, and outliers. Furthermore, filtering, indexing, joining, and merging datasets will be explored. This class will focus on using larger datasets to emulate real-world environments. Use of Anaconda is required.
This course is designed to provide students a thorough understanding of visualizing and disseminating data. Students will learn to create clear, concise, and visually appeasing graphs, charts, and dashboards to display data, especially for non-technical audiences. Focus will be given to ensuring that visualizations effectively and accurately display data. The course includes a hands-on element for students to gain experience with using the Tableau software program. Usage of Tableau is required.
This course focuses on applying advanced data analytics techniques using the R programming language. The course builds upon foundational knowledge of statistics and basic R programming, Students will gain experience in data manipulation, visualization, statistical modeling, and machine learning methods essential for tackling real-world data. The course emphasizes hands-on experience with the R programming language to implement analytics workflows, and the development of reproducible research and programming practices. Use of the R language is required. This course is required for all students in the Master’s in Data Analytics program.
This course is designed to provide students with the ability to create, manage, and access databases. Students will learn SQL to create and update databases and tables, as well as create complex queries to return information. Particular attention will be given to entity relationships and utilizing a relational database model. Joins and the usage of mathematic functions in SQL will also be taught. Additionally, students will learn to access and query a database through Python. This course will use PostgresSQL as the database software program.
This course explores the ethical implications of data analytics in today’s data-driven world. Students will examine key ethical principles, privacy concerns, bias in algorithms, and responsible data practices. Through case studies and discussions, learners will develop critical thinking skills to navigate complex ethical dilemmas in data analysis and decision-making. This course is required for all students in the Master’s in Data Analytics program.
This course is the culmination of the Master’s in Data Analytics program. Students will work with the course instructor to choose an individualized project in data analytics. This project should be focused on real-world data analysis and will focus and an end-to-end approach. Students will import, clean, manipulate, and conduct advanced analytics on the chosen dataset. The project will utilize at least one of the coding languages learned (Python, R, SQL) in the program. Students will also give a presentation of their project to communicate their findings. This course is required for all students in the Master’s in Data Analytics program. Prerequisites DS 615 and DS 623
