Intelligent Computing Graduate Certificate
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Wonder how Netflix predicts what its customers will enjoy? Wonder how health applications in your mobile devices are predicting your medical conditions? Wonder how your virtual assistants like Google, Siri and Alexa can do your daily work for you? Soon, your car will be able to drive itself faster and safer while you relax in the seat, but how?
That’s where SHU’s intelligent computing graduate certificate program will come in to play to teach you to understand the above situations, learn how they work and generate the best results while building some fascinating models. With massive computational power, big data, artificial intelligence and machine learning systems will manage, analyze and use the data far more successfully than ever before.
The program requires the student to complete a minimum of twelve (12) semester credit hours of course work with a minimum cumulative GPA of 3.0.
Prerequisite Course
If required
This hands-on course will introduce programming using Python on Windows and Linux platforms. Topics covered include basic programming concepts, regular expressions, basic data structures and algorithms, Boolean operations, and basic programming constructs including variables and types, string, arrays, sequential and parallel execution, assignments, decision and branching, loops, functions, procedures and calls, and basic debugging techniques.
Required Courses | 9 credits
This course provides an understanding of machine learning techniques. It offers the concepts and the tools the students need to implement programs capable of learning from data.
Addresses foundational principles making computers learn, plan, and solve problems autonomously; and driving modern intelligent agents on real-world applications for contemporary problems, such as deep learning, and data flows.
This course provides a theoretical and a practical understanding of machine learning focused exclusively on deep learning. The course will cover how deep learning can be used for unsupervised, classification, regression, and reinforcement learning across real world use cases, such as fraud detection, text classification, image processing, healthcare, and gaming. This course will use hands-on materials to supplement theoretical knowledge.
Elective Courses
This course presents a number of cloud computing tools and technologies, including virtualization, web services, data analysis, and integration.
This course provides a comprehensive review of data warehousing technology. Areas of study include the evolution of the modern day data warehouse; analysis and collection of business data requirements; dimensional modeling; the loading of data using extraction, transformation, and loading (ETL) processes; data quality issues; and reporting from the data warehouse using SQL and online analytical processing (OLAP) techniques. Several Oracle lab experiments are conducted to provide hands-on experience in the areas of data warehouse design, construction, data loading, and essential reporting techniques.
Prerequisite: TAKE CS-603
Big Data Analytics is about harnessing the power of data for new insights. The course covers the breadth of activities, methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry.