Data Science and Big Data Analytics

Catalog description

This course serves as an introduction to the interdisciplinary and emerging fields of data science and big data analytics. Students learn to combine tools and techniques from computer science, statistics, data visualization and the social sciences to solve problems using data. Central themes include: the data science and data analytics processes; advanced analytical theory and methods; tools for working with both big and small datasets, statistical modeling, and machine learning. Specific topics and tools include: data wrangling and munging, machine learning algorithms, statistical models, data visualization, NoSQL, Weka, RapidMiner, R, Python, Hadoop, and MapReduce.