Students who have completed the data science and analytics major should be able to:

• Demonstrate a well-developed understanding of the mathematics that are foundational to techniques in data science and analytics, such as set theory, symbolic logic, calculus, linear algebra, probability, and combinatorics.
• Collect, clean, validate and filter data sources.
• Demonstrate proficiency in an appropriate scripting language (e.g. Python), statistical software (e.g. R), and programming language for managing relational databases (e.g. SQL) and unstructured data repositories (e.g. No-SQL).
• Fit statistical models (e.g. linear regressions) and assess the efficacy of those models.
• Communicate statistical results in written and oral forms to technical and nontechnical audiences.
• Understand what can and cannot be inferred from a set of data and the limits of statistical techniques used in order to recognize errors that can be made in carrying out analyses and interpreting results.
• Apply data science and analytics in the investigation of real-world problems.
• Understand and appropriately respond to the many ethical considerations that arise in the field of data science and analytics.