The certificate program in cybersecurity and defense prepares computer science professionals to identify, analyze and mitigate technical cybersecurity related vulnerabilities, exploits and attacks against network and critical cyber infrastructure. The coursework emphasizes practical technical skills, analysis and research focused on current cybersecurity issues.
This course provides theoretical and computational foundations in machine learning to design and develop intelligent applications to perform tasks like object recognition, personalized recommendations, improve cybersecurity, fact-checking, forecasting and finding communities.
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This course provides the students with a comprehensive overview on the data life cycle as well as hands-on experience with manipulating the data with code to transform data to knowledge.
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This course introduces the concepts, algorithms, techniques, and systems of data warehousing and data mining
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This course provides a foundation on deep learning - currently the most sought-after skills in machine learning. Students will gain both theoretical and practical understanding of the concept and will work on few real-world problems like location identiication from photos (without GPS meta), speech reading / lip reading from silent videos, sign language recognition for your smartphones, stock market forecasting, image/object recognition from surveillance footage.
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An in-depth and advanced study of data manipulation modules in data life cycle with a focus on addressing Big Data challenges of volume, velocity and variety.
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This course introduces advanced concepts, algorithms, techniques, and systems of data warehousing and data mining at scale with Big Data.
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In this course, we start by briefly reviewing database application development concepts (e.g., data modeling, database languages, and normalization). Thereafter, we focus on how internal components of DBMS engines (e.g., data indexing, query evaluation, query optimization, concurrency control, and data recovery components) are designed and developed.
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Presents an in depth review of a series of modern data processing systems (e.g., Hadoop, Spark, Tensorflow), designed to address the Big Data challenges of volume, velocity, and variety. In combination, these systems enable the (Big) data lifecycle from data to knowledge. Students will obtain practical skills to use these systems while understanding the design principles in developing such systems.
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This course covers and relates three main components essential in parallel computation namely, parallel algorithms, parallel architectures, and parallel languages. The three areas will be described and their design influences on each other will be demonstrated from the perspective of high performance and scientific computing. Students will use our Parallel Distributed Systems Laboratory http://PDS.ucdenver.edu
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This course studies fundamental designs and key technologies in Cloud Computing by reading technical articles, and conducting a semester group project
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This course covers programming concepts related to the security of operating systems, applications, networks, and mobile devices. This course will explore:
This course covers analysis and defense techniques for operational networks and critical infrastructure. This course will explore: