CS 6330 - Data Science

• Understand the basic process of data science and exploratory data analysis including modes of inquiry (hypothesis driven, data driven, and methods driven)
• Identify, access, load, and prepare (clean) a data set for a given problem
• Select, apply, and interpret appropriate visual and statistical methods to analyze distributions of individual variables and relationships between pairs of variables
• Communicate findings through generated data visualizations and reports
• Apply and interpret unsupervised learning models for exploratory data analysis
• Generate appropriate supervised learning problem descriptions
• Determine and apply appropriate experimental setup, evaluation metrics, and models for supervised learning problems
• Engineer features for machine learning tasks
• Perform and interpret feature selection to identify relationships between features and predicted variables
• Apply methods to real-world data sets

• Application of the scientific method to data analysis and inference
• Cleaning and managing data using functional programming libraries such as pandas
• Using matrices and software libraries to structure data for analysis and manipulation
• Manipulation of models of real-world problems based on data
• Communicationg of data interpretations including generating meaningful data visualizations