Learning Goals
Students will be able to:
- Transform and map data (otherwise called data wrangling) from one "raw" data format into another format for further analysis, querying, learning, and prediction.
- Acquire data management skills such as database design and database querying. They will be able to create and maintain databases to facilitate querying and the incorporation of new data.
- Execute data analyses with professional statistical and machine learning software such as R and python.
- Visualize (plot) data relationships in meaningful ways, such as scatter plots, bar charts, box plots, and histograms, to communicate salient features of datasets. They will be able to adapt visualizations to the intended audience ranging from novice to expert.
- Understand the conceptual basis of those analyses used in data science and how those analysis methods aid in finding and articulating salient features of the data.
- Apply data science concepts and analysis methods to solve real-world problems by extending concepts and methods to novel contexts.
- Support an argument or stance with data, as well as critique findings and conclusions that are not justified by the data. They will be able to use data analysis methods to distinguish between and/or compare arguments and stances and discern between arguments strongly supported by the data versus those that misinterpret, distort, and/or misrepresent the data analysis to suit the argument, such as selectively emphasizing a subset of data while neglecting other conflicting data.
- Analyze the ethical, legal, and social implications of data collection, data processing, and algorithm development.
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Programs of Study and Courses for Liberal Arts and Sciences Students 
