Mar 29, 2024  
2019-2020 Course Catalog 
    
2019-2020 Course Catalog [ARCHIVED CATALOG]

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CSCI 2211 - Data Science and Visualization

Credits: 4
Hours/Week:
Course Description: This course introduces students to the conceptual foundations and applications of data science. Programming, mathematical, and statistical techniques will be used to analyze and visualize a variety of large-scale data sets. Students will gain hands-on practice in data analysis and visualization. Topics include ethical issues with the use of data, statistical programming language, such as R, for data analysis, and visualizations for presenting the results of data analysis.
MnTC Goals
None

Prerequisite(s): MATH 1025  with a grade of C or higher.
Corequisite(s): None
Recommendation: None

Major Content
  1. Overview of data science concepts, application, and use
  2. Introduction to data analysis tools such as R and RStudio
  3. Overview of current packages and basic techniques for use
  4. Programming concepts such as variables, control statements, loops, etc.
  5. Techniques to import, clean, and prepare data
  6. Introduction to Data frames, including manipulation, merging, and missing data
  7. Creating and using basic vector and matrix operations
  8. Generating descriptive statistics and using basis functions
  9. Different visualization techniques including graphs, tables, charts, and plots such as dot, pie, bar, histogram, mosaic, scatter, correlation, sunflower, box, line, and bag charts
  10. Emerging ethical issues with the use of data

Learning Outcomes
At the end of this course, students will be able to:

  1. import, prepare, and analyze data from different sources.
  2. clean and prepare data for analysis using statistical programming language such as R.
  3. summarize and present data in simple and easy-to-understand visualizations.
  4. apply effective visualization concepts and principles.
  5. discuss emerging ethical challenges with the use of data.
  6. collaborate with others on team projects and written and oral reports.
  7. describe the general types of data science analytics and their use in different organizational structures.
  8. use visualization to observe patterns of data, identify outliers and influential points, and understand import features of the data.
  9. use exploratory data visualization to help see the details of the data.
  10. apply programming techniques such as conditionals, loops, functions and input and output to analyze data.

Competency 1 (1-6)
None
Competency 2 (7-10)
None


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