DATA SCI 7600: Introduction to Data Analytics
DATA SCI 8610: Statistical and Mathematical Foundations for Data Analytics
DATA_SCI 8614 Data Analytics from Applied Machine Learning
Synopsis: This course leverages the foundations in statistics and modeling to teach applied concepts in machine learning. Participants will learn various classes of machine learning and modeling techniques, and gain an in-depth understanding how to select appropriate techniques for various data science tasks. Topics cover a spectrum from simple Bayesian modeling to more advanced algorithms such as support vector machines, decision trees/forests, and neural networks. Students learn to incorporate machine learning workflows into data-intensive analytical processes.
Data SCI 8620: Database and Analytics
DATA SCI 8640: Big Data Security
Data SCI 8650: Big Data Visualization
DATA SCI 8660: Data and Information Ethics
DATA SCI 8680: Big Data Analysis Case Study
DATA SCI 8690: Big Data Capstone
Concentration Area Courses
DATA_SCI 8001 Genomics
Data Journalism/ Strategic Communication
DATA_ SCI 7263 Digital Strategy II
DATA_SCI 7635 Communication Network Analytics
DATA_SCI 7636 Data Journalism
DATA_SCI 7637 Streaming Social Media Data Management and Analysis
DATA_SCI 8612 Spatial and Geostatistical Analysis
This course will provide a practical overview of key issues encountered when working with and analyzing spatial data as well as an overview of major spatial analysis approaches. Discussions and laboratory work will focus on implementation, analysis, and interpretive issues given constraining factors that commonly arise in practice.
DATA_SCI 7001 Geospatial Data Engineering
This course provides an overview of theoretical and practical issues encountered when working with geospatial data for both the vector and raster data models with a focus on incorporating geospatial data into the data science lifecycle. Data access, indexing, retrieval, and other technical concepts are investigated. Important data storage paradigms such as enterprise geospatial databases and desktop GIS systems are explored along with scalable computational tools beyond desktop computing for Geospatial Big Data. Core issues in geospatial data storage, management, exploitation, and multi-data set entity resolution / correlation are examined.
DATA_SCI 8634 Remote Sensing Data Analytics
This course provides an introduction to the principles of remote sensing of the environment leading to information extraction from remote sensing geospatial raster data sets. Examines theoretical and practical issues associated with digital imagery from spacecraft, conventional and high-altitude aerial photography, thermal imaging, and microwave remote sensing. Covers standard processing techniques, including preprocessing and normalization, pixel-level feature extraction, information extraction, and structural/object extraction.
Data SCI 8630: Data Mining and Information Retrieval
DATA_SCI 8635 Cloud Computing for Data Analytics
This course introduces students to cluster and cloud computing big data ecosystems. Topics include a survey of cloud computing platforms, architectures, and use-cases. Students will examine scaling data science techniques and algorithms using a variety of cluster and cloud paradigms, such as those built atop Spark (Map-Reduce) concepts, AWS, GCP, and others.
DATA_SCI 8750 Parallel Computing for Data Science
This course will provide in-depth treatment of the evolution of high performance, parallel computing architectures and how these architectures and computational ecosystems support data science. We will cover topics such as: parallel algorithms for numerical processing, parallel data search, and other parallel computing algorithms which facilitate advanced analytics. To reinforce lecture topics, learning activities will be completed using parallel computing techniques for modern multicore and multi-node systems. Parallel algorithms will be investigated, selected, and then developed for various scientific data analytics problems. Programming projects will be completed using Python and R, leveraging various parallel and distributed computing infrastructure such as AWS Elastic Map Reduce and Google Big Query and various other parallel computing architectures. Students will research emerging parallel and scalable architectures for data analytics.
Human-Centered Design for Data
DATA_SCI 8654 Advanced Visualization and Communication I
DATA_SCI 8656 Advanced Visualization and Communication II
Sample Course Path
Students move through 8 week modules completing core courses and then progressing through emphasis area concentration courses directly applicable with their area of study.