11 courses with the subject DA, 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.
DA 101Introduction to Data Science3
In this course students receive an introduction to the concepts and procedures in data science. An overview of the data, questions, and techniques and tools that data analysts and data scientists work with are introduced and reviewed. This course provides a conceptual introduction to the ideas behind turning data into actionable knowledge and tools that will be used to analyze this data. The course will focus on the collection, organization, manipulation, assessment and analysis, and c communication of data.
In this course the student will manipulate, process, clean, analyze and visualize data in a programming language. Real world datasets will be utilized. Structured data will be emphasized.
In this course students will focus on how to apply the Structured Query Language (SQL) to data analysis tasks. Spreadsheets will be used for the visualization of data. Additionally, basic statistics will be covered. All data will extracted from relational tables.
This course covers emerging big data architectures that deal with large amounts of unstructured and partially organized data. Focus in on the creation of applications that analyze big data stored in distributed file systems. Topics include file architecture, data retrieval, performance and data analysis.
and Computer Applications in Business This course uses computer applications and critical thinking skills to solve real world business problems. Students integrate the use of word processing, spreadsheet, database, presentation, add-in software, and Internet resources to manage data to solve problems. Emphasis is placed on the use of software tools and analysis and modeling techniques to manage and manipulate data sources for decision- making. The course assumes prior successful experience with and knowledge of individual Microsoft Office computer applications programs.
Science (3) Statistical concepts and applications related to data science including advanced exploratory data analysis, nonparametric inference and simulation for larger datasets, logistic regression modeling, statistical programming, and basics of machine learning. Prerequisites DA101 and MA120 or equivalent
This course is an applied statistics course that introduces students to key topics in data science, including exploration, statistical data analysis an communicating the results of data analyses. Major topics include advanced R programming language concepts, working as a standalone data analyst be and within a team, organizing analysis projects, modeling with univariate, bivariate and multivariate data and basic clustering, classification and time serie analysis and forecasting.
Intelligence (3) This course introduces students to key design principles and techniques for interactively visualizing data. Students will be able to tell a story with data, communicating observations in a clear, compelling way that provides meaning and explanation. Students will study how visual representations are used in the analysis and understanding of complex data and acquire data visualization skills including designing effective visualizations, creating interactive visualizations, and drawing and presenting conclusions based on raw data from industry.
This is the second course in the data sualization sequence. Students will apply advanced design principles and techniques for interactively visualizing data. Students will be able to both - create complex data visualizations and critique data visualizations designed for multiple audiences from many sources of information. Students will make use of tools like Tableau, Plotly and Quadrigram as they graphically represent analytical patterns. Students will also create and critique different types of dashboards.
Science (3) A comprehensive, project-based course where partners in industry, science, and government work with faculty and students providing expertise, guidance, and real data. Course includes topics in data mining, data ethics, and reproducible research. 83
