Data Science Track
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The data science track in Sacred Heart University’s computer science & information technology master’s program emphasizes the development and coding aspects of data science and data analytics.
Data science is the process of using algorithms, methods and systems to extract knowledge and insights from structured and unstructured data. It applies advanced analytics and machine learning to help users predict and optimize business outcomes.
Python, Programming with R, Data Warehousing and Statistics, along with Deep Learning, Data Architecture and Text Mining are required. Special Topics in emerging fields will also be covered.
This track requires completion of 30 credit hours (10 courses) of graduate-level coursework.
Required Courses | 24 credits
A student may test out of CS 504. Tests are administered during program orientation. Take CS 649 in the first trimester.
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.
This course discusses goals and techniques in the design, implementation, and maintenance of large database management systems: physical and logical organization; file structures; indexing; entity relationship models; hierarchical, network, and relational models; normalization; query languages; and database logic.
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.
This course provides the necessary skills to successfully navigate through the Data Science track. Topics include: data sampling, tendency and distribution of data, hypothesis testing, variations, regression and probability
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.
Given the dominance of text information over the Internet, mining high-quality information from text becomes increasingly critical. The actionable knowledge extracted from text data facilitates our life in a broad spectrum of areas, including business intelligence, information acquisition, social behavior analysis and decision making. In this course, we will cover important topics in text mining including: basic natural language processing techniques, document representation, text categorization and clustering, document summarization, sentiment analysis, social network and social media analysis, probabilistic topic models and text visualization.
Prerequisite: Take CS-650
As the prolifération of data continues, Data-Driven Decision Making, Machine Learning & Data Science continue to grow in importance. To leverage data for these and other purposes, the architecture for data must support the proper ingestion, transformation, storage, and retrieval of data. In addition, data needs to be organized, catalogued, and stored to allow access by data scientists and other analytical users. As technologists, we must consider many aspects of architecture. This course will explore the various technologies and methodologies for ingestion, transformation, storage, and retrieval of data.
Prerequisite: Take CS-650
With emerging technologies in data science growing, various topics will emerge in the field as needed by the corporate environment. This course will examine timely topics not extensively covered in other courses such as : Ethics in Data Privacy, Data Bias, Data Literacy in the Enterprise and GDPR and other regulatory restrictions around data.
Prerequisite: Take CS-650
Elective Courses | 6 credits
Choose 6 credits. Other programming electives can be selected with approval by the advisor or program director.
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.
This course presents a number of cloud computing tools and technologies, including virtualization, web services, data analysis, and integration.
This course will provide advanced concepts of Python script programming. Topics covered include Functions, Design with classes, Multithreading, Networks, Client/server programming, Searching, Sorting and Complexity analysis.
Prerequisite: Take CS-504