MS in Data Science and Analytics

Graduates will be able to individually acquire and stage large data sets, design and conduct experiments, and analyze results for complex data analytical problems using their foundational and specialized data science tools and techniques; taking a problem from conceptualization stage through to the production of data-derived business intelligence.

The special skills the graduating students will acquire or possess include:

  • Real-world  experience  in applying state-of-the-mi data science tools and  techniques to solve industry, academic, and/or business data and decision-making challenges.
  • A clear understanding of the ethics and security mechanisms required to safeguard large-scale data collections that contain sensitive and critical information.
  • A comprehensive understanding of modern data analytics, statistical analysis, and visualization tools that facilitate timely, large data analysis.
  • A solid foundational understanding of database systems, database design, and information retrieval; allowing exploitation of a broad spectrum of data repositories  and  streaming  data systems.
  • A demonstrated ability to effectively communicate to a broad audience the relevant information derived from large data collections using a variety of visualization and presentation methods. Students will be able to convey the meanings behind specific data analysis techniques to audiences of various technical  knowledge.
  • Training in the latest data analytic methods and tools; including fundamental and advanced statistical and mathematical principles upon which advanced  data analysis techniques are built (machine learning, pattern recognition, data mining, etc.).
  • Specialized, advanced training in a chosen Emphasis Area, such as biotechnology, high-performance computing, strategic communications, human  centered  data science  design, etc.

The proposed Masters degree in Data Science & Analytics is designed to provide students the necessary knowledge and skill sets across various disciplines needed to meet the high industry demand for data scientists. All students will take "Core Courses" that will provide a foundation of knowledge and an introduction to state-of-the-art technology in Big Data, database design, data ethics, and visualization of high­ dimensional and high-volume data.

To understand real-world Big Data issues in context, students will  select three  courses in  an emphasis area. These elective courses will support in-depth analyses  and training  on data analytic techniques,  issues,  and problems  students will face within  a given  Emphasis area. Students will take a Case Study course to gain hands-on experience with large data  sets and use the relevant technology and techniques. A Capstone project will enable students to refine and demonstrate knowledge and skills learned throughout the program. Both courses will provide students with mentoring from faculty, as well as insight from industry partners.

Total credits required for graduation: 34

 Total credits for general education courses: 19

First Year
FallCreditsSpringCreditsSummerCredits
Introduction to Data Analytics3Data Mining and Information Retrieval3Statistical & Mathematical Foundaions of Data Analytics3
Database and Analytics3Big Data Security3Case Studies3
Data and Informaiton Ethics1Data Visualization3 
 7 9 6
Second Year
FallCreditsSpringCredits 
Emphasis Area Course 1 Emphasis Area Course 3  
Emphasis Area Course 2 Capstone  
 0 0  
Total Credits: 22

Emphasis Area Requirements

Biotechnology

In addition to the core program objectives, graduates of the Masters of Science in Data Science and Analytics who pursue the Biotechnology emphasis area will achieve the following educational objectives:

  • Possess an in depth understanding of the data analytics needs in biotechnology industry  and healthcare  systems in the US  and worldwide;
  • Have the ability to practice their analytic skill sets to applications in agriculture, human medicine, and health information systems in genomics, proteomics, phonemics, and electronic medical records;
  • Be able to present and interpret their data analytic results with actionable plans in biotech  and healthcare industry.
INFOINST 8005Applications of Bioinformatics Tools in Biological Research3
High-throughput Biomedical Data Analysis3
Biomedical Information Mining and Interpretation3

High Performance Computing Emphasis Learning Objectives

Graduates of the Master of Science in Data Science and Analytics who pursue the High Performance Computing (HPC) emphasis area will achieve the following educational objectives, in addition to the core program objectives while becoming immersed in Big Data  computational  ecosystems.

  • Students will have  an in depth understanding  of the state-of-the-art  technologies  which enable big data analytics and high performance computing; such that they can successfully investigate the data and analytical needs, then guide the decision making process  on deployments  into HPC infrastructure.
  • Students will acquire knowledge to exploit cloud-based computing infrastructure, including virtualization, distributed architectures, on-demand resource scaling, container technology, and other cloud-based computing concepts in support of Big Data  management,  processing,  and analytics.
  • Students will have a thorough understanding  of advanced  technologies  and techniques in Big Data analytics which facilitate the extraction of new data intelligence using state-of-the-mi,  leading  analytical platforms.
  • Students will gain a solid understanding of techniques for exploiting advanced co­ processing hardware, including graphics processing units (GPU) and many-core units (e.g., Intel Phi) to achieve cost effective, massively  parallel data analytics
CMP_SC 7001Topics in Computer Science (Cloud Computing)3
CMP_SC 7001Topics in Computer Science (Big Data Analysis)3
CMP_SC 8850Computer Networks II3

Strategic Communication and Data Journalism

Graduates of the Master of Science in Data Science and Analytics who pursue the Strategic Communication and Data Journalism emphasis area will achieve the following educational objectives, in addition to the core program objectives.

  • Students will have in-depth capabilities and understanding of big data management, including gathering and interpreting customer and viewer behavior patterns and developing strategies to enhance marketing objectives for organizations  and clients.
  • Students will have highly-marketable skills in analyzing media markets  and will  be able to develop sophisticated methodologies to optimize usage of apps, social networks,  and other technologies  for media businesses  and brands.
  • Students will be able to apply their analytic skills to understanding and optimizing patterns of search, programmatic advertising buying and behavioral targeting.
  • Students will have a deep understanding of issues of privacy and ethics in obtaining and utilizing data from a broad range  of   sources.
  • Students will be able to obtain and analyze publicly-available data in a variety of structured and unstructured formats. As such, they  will develop an understanding of open-records  laws and how to effectively  use  them.
  • Students will develop the skills necessary to work as a data journalist for a news organization.
  • As data journalism and data science /analytics are constantly evolving, students will  develop strategies that enable them to continue to learn   on the job.
  • Students will develop an understanding of their audiences and how to best communicate with them.
JOURN 7263Digital Strategy II3
JOURN 7236Psychology in Advertising3
JOURN 7248Media Strategy and Planning3
JOURN 7430Computer-Assisted Reporting3
JOURN 7432Advanced Data Journalism3
JOURN 7440Mapping for Stories and Graphics2

Human Centered Data Science Design

Students will develop a deep understanding of the theoretical foundations and hands-on experience necessary to understand the strengths and limitations of different analytical methods.

  • Human Centered Data Science Design combines both the technical (databases, social networking, data mining, and text mining) and social (economic, ethical, policy, and political)  aspects  of data analytics.
  • Students will build an understanding of the complex interplay between the decisions made during the collection, curation, and transformation steps in the information lifecycle,  and their  impact on the analytical methods  that  should be employed.
IS_LT 9410Seminar in Information Science and Learning Technology (Introduction to Human Centered Data Science Design)3
IS_LT 7310Seminar in Information Science and Learning Technology (Human-Computer Interaction)3
IS_LT 9410Seminar in Information Science and Learning Technology (Learning Analytics)3
IS_LT 9410Seminar in Information Science and Learning Technology (Digital Humanities and Information)3
IS_LT 9410Seminar in Information Science and Learning Technology (Metadata)3
IS_LT 9410Seminar in Information Science and Learning Technology (Learning Analytics)3

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