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 data-based 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 real-world contexts and will communicate these solutions effectively
| Emerging | Developing | Proficient | Advanced | |
| Programming | Given simple algorithms, students can code them in a high-level programming language. | Students themselves can formulate simple algorithms to solve problems, and can code them in a high-level 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 real-world 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 on-line 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 menu-driven 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鈥檛 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 | Lots | Lots | Lots | Lots | Lots | Some |
| DATA 327 | Lots | Lots | Lots | Some | Lots | |
| COMP 150 | Lots | |||||
| COMP 290 | Lots | Lots | ||||
| calculus | some | |||||
| DATA 460 | Lots | Lots | Lots | Lots | 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 data-based 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 | Lots | Lots | Lots | Lots | Some |
| 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 |