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Loyola University Chicago Data Science
College of Arts and Sciences
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Learning Outcomes

  1. The ability to manage large data sets in preparation for data science analysis: Meeting this learning outcome will be assessed based upon course performance in Introduction to Data Science (DSCI401). Thesis track students will have further training in data set management in Introduction to Data Science Research (DSCI494); course performance for thesis track students also will be assessed for this class.
  2. A working knowledge of traditional statistical techniques and the ability to apply these methods to a wide array of real world problems: Meeting this learning outcome will be assessed based upon course performance in Categorical Data Analysis (STAT410).
  3. The ability to perform data science analysis from beginning to end while adhering to the principles of reproducible research: Meeting this learning outcome will be assessed based upon course performance in Data Science Consulting (DSCI470; or approved alternative) for non-thesis track students or Thesis (DSCI595) for thesis track students. In addition to assessment of this learning outcome from the student grade, faculty advisors (either DSCI470 instructor, internship supervisor, or thesis research mentor) will provide feedback on student progress towards this learning objective through a short survey.
  4. The ability to program in both the R and Python programming languages: R and Python will be used throughout courses. Student course performance in programming intensive courses will be evaluated. These courses include Introduction to Data Science (DSCI401), Categorical Data Analysis (STAT410), Big Data Analytics (COMP458), Predictive Analytics (STAT488), and Machine Learning (COMP479). In addition to assessment of this learning outcome from the student grade, students will be provided with an anonymous survey at the conclusion of their 2nd semester of study; this survey will engage students to reect upon their familiarity of both the R and Python programming languages.
  1. The ability to manage large data sets in preparation for data science analysis: Meeting this learning outcome will be assessed based upon course performance in Introduction to Data Science (DSCI401). Thesis track students will have further training in data set management in Introduction to Data Science Research (DSCI494); course performance for thesis track students also will be assessed for this class.
  2. A working knowledge of traditional statistical techniques and the ability to apply these methods to a wide array of real world problems: Meeting this learning outcome will be assessed based upon course performance in Categorical Data Analysis (STAT410).
  3. The ability to perform data science analysis from beginning to end while adhering to the principles of reproducible research: Meeting this learning outcome will be assessed based upon course performance in Data Science Consulting (DSCI470; or approved alternative) for non-thesis track students or Thesis (DSCI595) for thesis track students. In addition to assessment of this learning outcome from the student grade, faculty advisors (either DSCI470 instructor, internship supervisor, or thesis research mentor) will provide feedback on student progress towards this learning objective through a short survey.
  4. The ability to program in both the R and Python programming languages: R and Python will be used throughout courses. Student course performance in programming intensive courses will be evaluated. These courses include Introduction to Data Science (DSCI401), Categorical Data Analysis (STAT410), Big Data Analytics (COMP458), Predictive Analytics (STAT488), and Machine Learning (COMP479). In addition to assessment of this learning outcome from the student grade, students will be provided with an anonymous survey at the conclusion of their 2nd semester of study; this survey will engage students to reect upon their familiarity of both the R and Python programming languages.