Learning Goals for the Major Program in Data Science
Students with a major in Data Science.
 Students will develop relevant programming abilities.
 Students will demonstrate proficiency with statistical analysis of data.
 Students will develop the ability to build and assess databased models.
 Students will execute statistical analyses with professional statistical software.
 Students will demonstrate skill in data management.
 Students will apply data science concepts and methods to solve problems in realworld contexts and will communicate these solutions effectively
Emerging  Developing  Proficient  Advanced  
Programming  Given simple algorithms, students can code them in a highlevel programming language.  Students themselves can formulate simple algorithms to solve problems, and can code them in a highlevel language appropriate for data science work (e.g., Python, SQL, R, Java).  Students can create algorithms of moderate complexity, and can implement them in at least two languages appropriate for data science work.  Students can design more complex algorithms involving more complex data structures, and can implement their solutions in multiple languages. 
Data Anal.  Students can carry out standard data visualization and formal inference procedures and can comment on the results.  Students can choose appropriately from a wider range of exploratory and inferential methods for analyzing data, and can interpret the results contextually.  In addition to exploratory and inferential analysis, students can construct complex statistical models, assess the fit of such models to the data, and apply the models in realworld contexts.  Students can also compare the performance of multiple methods and models, recognize the connections between how the data were collected and the scope of conclusions from the resulting analysis, and articulate the limitations and abuses of formal inference and modeling. 
Modeling  Students understand what a model is and can use a given model.  Students can use more complex models and can begin to construct models of their own.  Students recognize that different models fit and perform better than others, and can measure fit and performance appropriately.  Students have multiple strategies for constructing models and can use different measures of model fit and performance to assess models. 
Stat Soft.  Students can generate simple statistical summaries using online tools or software not designed for statistical analyses (e.g., Excel).  Students can create a wider range of visual and numerical data summaries and carry out basic inferential procedures (confidence intervals and significance tests) using menudriven statistical software.  In addition to performing exploratory and inferential procedures, students can fit complex models using dedicated statistical software (e.g., R, Minitab, SAS).  Students can design their own statistical analyses and implement them with advanced statistical programming tools. 
Data Mgt.  Students can work with data after the data have been collected and cleaned, and can use data in the form in which the data are given.  Students can perform basic data cleaning, and can transform variables to facilitate analysis.  Students can acquire and clean their own data, and can move information in and out of relational databases.  Students can integrate data from disparate sources, can transform data from one format to another, and can program data management in relational databases. 
Solve/ Comm.  Students can manipulate data and carry out basic analyses, but the data management and analyses may be flawed or are inappropriate for the problem at hand, and there may be no sense of the purpose of the work.  Students can manage data sources and execute analyses appropriately, but can’t fully connect or apply the results to the original context of the data or meaningfully communicate the impact of the work.  Students can choose appropriate data management strategies, can carry out relevant analyses, can interpret and apply the results to inform understanding and solve specific problems in context, and can communicate the work to a technical audience. 

The coursework that a student undertakes with as a major in Data Science will support the learning goals in the following way:
Course  Programming  Data Anal.  Modeling  Stat Soft.  Data Mgt.  Solve/ Comm. 
intro stat  Lots  Some  
DATA 229  Some  Some  Some  Lots  
MATH 327  Lots  Lots  Lots  Some  Some  
COMP 150 
Lots 

COMP 290  Lots  Lots  
calculus  some  
DATA 460  Some  Some  Some  Some  Some  Lots 
BUSN 390  Some  Some  Some  Some  
COMP 250  Lots  Some  
COMP 265  Lots  
COMP 275  Some  Some  
COMP 350  Some  Some  
COMP 353  Some  Some  
MATH 228  Some  
MATH 261  Some  
MATH 328  Some  Some  Some  
MATH 337  Lots  Some  Some 
Learning Goals for the Minor Program in Data Science
Students with a minor in Data Science.
 Students will develop relevant programming abilities.
 Students will demonstrate proficiency with statistical analysis of data.
 Students will develop the ability to build and assess databased models.
 Students will execute statistical analyses with professional statistical software.
 Students will demonstrate skill in data management.
The coursework that a student undertakes with as a minor in Data Science will support the learning goals in the following way:
Course  Programming  Data Anal.  Modeling  Stat Soft.  Data Mgt. 
intro stat  Lots  Some  
DATA 229  Some  Some  Some  Lots  
MATH 327  Lots  Lots  Lots  Some  
COMP 150 
Lots 

COMP 290  Lots  Lots  
calculus  some  
DATA 460  Some  Some  Some  Some  Some 
BUSN 390  Some  Some  Some  
COMP 250  Lots  Some  
COMP 265  Lots  
COMP 275  Some  Some  
COMP 350  Some  Some  
COMP 353  Some  
MATH 228  Some  
MATH 261  Some  
MATH 328  Some  Some  Some  
MATH 337  Lots  Some 