INFO-H 515 Data Analytics
Prerequisites: ECON E570 or HPER T591 or PBHL B561 or PSY 60000 or STAT 51100
This course applies statistical learning methods for data mining and inferential and predictive analytics to informatics-related fields. The course also introduces techniques for exploring and visualizing data, assessing model accuracy, and weighing the merits of different methods for a given real-world application. This course provides an essential toolset for transforming large, complex informatics datasets into actionable knowledge.
- Analyze datasets with supervised learning methods for functional approximation, classification, and forecasting and unsupervised learning methods for dimensionality reduction and clustering.
- Explore, transform, and visualize large, complex datasets with graphs in R.
- Solve real-world problems by adapting and applying statistical learning methods to large, complex datasets.
- Identify, assess, and select among statistical learning methods and models for solving a particular real-world problem, weighing their advantages and disadvantages.
- Write programs to perform data analytics on large, complex datasets in R.
- Analyze datasets from case studies in informatics-related fields (e.g., digital media, human-computer interaction, health informatics, bioinformatics, and business intelligence).