Learning Goals

  1. Data formats: 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.
  2. Datasets, database design, and querying: Students will acquire data management skills such as database design and database querying. They will be able to create and maintain databases with intentions of facilitating querying and the incorporation of new data.
  3. Analyzing datasets, data analysis tools: Students will be able to execute data analyses with professional statistical and machine learning software such as R and python.
  4. Visualization, Communication, and Adaptation: Students will be able to visualize (plot) data relationships in meaningful ways such as scatter plots, bar charts, box plots, histograms, to communicate salient features of datasets. They will be able to adapt visualizations to the intended audience ranging from novice to expert.
  5. Conceptual underpinnings of analysis methods (the why behind the what): Students will 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.
  6. Transfer to novel contexts: Students will be able to apply data science concepts and analysis methods to solve real-world problems by extending concepts and methods to novel contexts.
  7. Data-driven reasoning: Students will be able to support an argument or stance with data as well as critique findings and conclusions which are not justified by the data. They will be able to use data analysis methods to distinguish between and/or compare arguments and stances, discern between arguments strongly supported by the data versus those which 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.
  8. Societal context: Students will be able to analyze the ethical, legal, and social implications of data collection, data processing, and algorithm development.