Big Data Analytics

3 Credits

Provides a comprehensive introduction to big data analytics, emphasizing statistical and machine learning techniques for analyzing large datasets. While rooted in geography and spatial sciences, the course is broadly applicable across socio-behavioral sciences. Students will gain hands-on experience with industry-standard tools and frameworks for data exploration, modeling, and visualization. Topics covered include foundational data analytics methods using R, clustering techniques, regression analysis, text analysis, and deep learning with Keras and TensorFlow. The course also explores geospatial machine learning in ArcGIS, big data processing with Hadoop and Spark, and working with distributed computing frameworks such as MapReduce, Pig, Hive, and NoSQL databases.