CSC 2621 - Introduction to Data Science

• Read scientific literature and interpret results
• Communicate computational results and their interpretations in language appropriate for a scientific audience
• Generate hypotheses about the relationships between pairs of variables
• Use evidence from descriptive statistics, visualizations, and statistical testing to assess correctness of hypotheses
• Leverage appropriate libraries and declarative programming techniques to efficiently solve non-trivial data cleaning and transformation challenges such as joining data sets, unifying types and representations of values, and handling missing data
• Use exploratory data analysis (e.g., summary statistics and visualizations) to characterize data sets, their variables, and relationships between variables
• Convert a variety of variable types into numerical features for use in machine learning models
• Determine which variables are informative for use in machine learning models
• Create well-reasoned, logical arguments regarding the potential biases of data sets and their impact on the results of a computational model
• Identify potential negative implications of applications of machine learning models

• Proficiency in at least one programming language
• Familiarity with common data structures (e.g., lists, maps, and sets)
• Familiarity with Python, Pandas, and visualization libraries