CSC/GIS 711: Geospatial Data Management

Graduate Course for Geospatial Analytics PhD Students, NCSU, Computer Science Department, 2018

Course Description: Spatial databases which are designed to collect, manage, retrieve, and analysis of geospatial data are core of modern geographic information systems. The understanding of data management principles and technologies is critical for efficient implementation of geospatial applications. This course introduces students to: (i) spatial and temporal data types, (ii) data models, (iii) geometry models (iv) spatial predicates, (v) spatial access methods, and (vi) spatial query processing. In addition, students will be exposed to modern data management systems for geospatial application development, and data integration principles.

CSC 591/791: Spatial and Temporal Data Mining

Graduate Course, NCSU, Computer Science Department, 2015

Course Description: Spatial (spatiotemporal) data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from the spatial (spatiotemporal) data. However, explosive growth in the spatial and spatiotemporal data (~70% of all digital data), and the emergence of geosocial media and location sensing technologies has transformed the field in recent years. This course reviews the current state of the art in spatial, temporal, and spatiotemporal data mining and looks at real-world applications ranging from geosocial networks to climate change impacts.

CSC 591: Foundations of Data Science

Graduate Course for Data Science Track, NCSU, Computer Science Department, 2015

Course Description: Students will learn core data science principles related to statistical data analysis. This course introduces ideas in statistical learning and will help students prepare for advanced courses in data mining and machine learning. Focus will also be given on applying these principles for variety of data analysis tasks using R. Topics: Random variables and probability distributions, exploratory data analysis, variable selection, sampling methods, histograms and probability distributions, density estimation, missing data and imputation, mixture models, latent variables, and expectation maximization, regression analysis, discriminant analysis, bagging and boosting, principle component analysis, information theory – entropy, mutual information, Bayesian information criteria, conditional independence, rescaling and low-dimensional summaries, factor analysis, graphical causal models and causal inference, and evaluating predictive models.

CSC 4/522: Automated Learning and Data Analysis

U/Graduate Course in Data Mining, NCSU, Computer Science Department, 2014

Course Description: This course introduces students to algorithms and applications of data mining. Data mining is the art of extracting useful patterns from large collections of data. The rapid growth of (digital) data in recent decades and access to cheap and large scale computing resources, gives us the opportunity to apply data mining in science (e.g., earth and climate change sciences, astronomy, food, energy, and water), medicine (e.g. bioinformatics, brain imaging), business (e.g., web and text, financial, healthcare), and government (e.g., national security, cyber and physical critical infrastructures). Though emphasis will be on learning algorithms spanning classification and prediction, clustering, association rules, and anomaly detection, the course will also expose students to various applications.