CSCI 2211 - Data Science and Visualization

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

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.

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