M.S. in Data Science & Analytics

A Data-Driven Program, By Design!

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The Master of Science in Data Science and Analytics is a 30-credit hour, non-thesis degree program. The curriculum consists of a core of 18 credits (6 courses), a choice of four concentrations, and a capstone project. The four concentrations are in:

  • Artificial Intelligence and Machine Learning
  • Business Intelligence & Analytics
  • Engineering & Big Data Analytics
  • Geospatial Analytics

The objective of the core is to lay the foundation required by a data scientist working in any field. Core courses will establish proficiency in data discovery, collection, processing, and cleaning; exploratory data analysis using statistics and visual analytics; and statistical modeling for prediction/forecasting. The capstone project in all concentrations will provide the opportunity to synthesize knowledge from coursework to solve real-world problems.

Graduates in the data science program may find careers in these roles:

    • Data Scientist
    • Data Engineer
    • Data Analyst
    • Machine Learning Scientist
    • Machine Learning Engineer
    • Applications Architect
    • Statistician
    • Enterprise Architect
    • Business Intelligence (BI) Developer
    • Computer and Information Systems Manager
    • Database Administrator

    Core Courses (18 Credits)

    • CS 620/DASC 620 Introduction to Data Science and Analytics (3 credits)

    • CS 624 Data Analytics and Big Data*(3 credits)

    • CS 625 Data Visualization* (3 credits)

    • STAT 603 Statistical/Probability Models for Data Science and Analytics* (3 credits)

    • STAT 604 Statistical Tools for Data Science and Analytics* (3 credits)

    Capstone

    DASC 690 Capstone Project* (3 credits)

    *The capstone project will provide an opportunity for students to synthesize knowledge from their coursework and apply it to solve real-world data analytics problems.

    Concentrations

    This concentration prepares students to transform data into actionable information for organizations seeking data-driven recommendations. The coursework addresses methods and tools used to store, access, and analyze data to support business decision-making. Students learn how to identify, manage, retrieve, and analyze data in order to gain insight and use the resulting information to make informed business decisions. Students select four courses (12 credits) in consultation with the faculty advisor.

    Select two courses from (6 credits):

    • BNAL 503 Data Exploration and Visualization (3 credits)
    • BNAL 515 Advanced Business Analytics with Big Data Applications (3 credits)
    • BNAL 721 Simulation Modeling for Business Systems (3 credits) is preferred, but if not offered, BNAL 576 Simulation Modeling and Analysis for Business Systems (3 Credits) may be substituted for BNAL 721 with permission of the concentration coordinator if BNAL 721 is not offered.

    Select two courses from (6 credits):

    • IT 650 Database Management Systems (3 credits)
    • IT 651 Business Intelligence (3 credits)
    • IT 652 Information and Communications Technology for Big Data (3 credits)

    The purpose of this concentration is to prepare students to enter rapidly emerging fields related to data science and analytics. The coursework addresses relevant data analytics topics such as video analytics, algorithms and data structures, and information retrieval. Students learn computational data analysis, data visualization, and natural language processing.

    Students select four courses from the list below (12 credits) in consultation with the faculty advisor:

    • CS 522 Machine Learning I (3 credits)
    • CS 532 Web Science (3 credits)
    • CS 550 Database Concepts (3 credits)
    • CS 569 Data Analytics for Cybersecurity (3 credits)
    • CS 580 Introduction to Artificial Intelligence (3 credits)
    • CS 722 Machine Learning II (3 credits)
    • CS 725 Information Visualization (3 credits)
    • CS 733 Natural Language Processing (3 credits)
    • CS 734 lnformation Retrieval (3 credits)

    The purpose of this concentration is to provide students with a thorough understanding of the methods and technologies to handle big data and to instill engineering problem-solving skills rooted in big data solutions. It will further prepare them to become professionals trained in advanced data analytics, with the ability to transform large streams of multiple data sources into understandable and actionable information for the purpose of making decisions. The coursework (12 credits) will enable students to learn and practice the following competencies: data collection, data storage, processing and analyzing data, reporting statistics and patterns, drawing conclusions and insights and making actionable recommendations.

    Select two core courses from (6 credits):

    • ENMA 754 Big Data Fundamentals (3 credits)
    • MSIM 715 High Performance Computing and Simulation (3 credits)
    • ECE 607 Machine Learning 1 (3 credits)

    Select two elective courses from (6 credits):

    • ECE 784 Computer Vision (3 credits)
    • MSIM 695 Topics in Visualization for Big Data Analytics (3 credits)
    • MSIM 574 Transportation Data Analytics (3 credits)
    • MAE 740 Autonomous and Robotic Systems Analysis and Control (3 credits)
    • CEE 722 Parallel Cluster Computing Methods (3 credits)
    • ECE 651 Statistical Analysis and Simulation (3 credits)
    • ECE 780 Machine Learning II (3 credits)

    This concentration enables MS Data Science students to develop advanced skills and expertise in geospatial science and technology. Incorporating Geographic Information Systems (GIS), remote sensing, and location-based data allows data scientists to uncover spatial patterns. The concentration provides for a foundation across the breadth of geospatial technology to prepare data for analysis, perform suitability analysis, spatial predictive modeling, geostatistics, and space-time pattern mining and object detection. The concentration coursework (12 credits) incorporates advanced geovisualization and webmapping technology to also enhance cartography analytics and communications.

    Required core courses for this concentration (6 credits):

    • GEOG 600 Geospatial Data Analysis (3 credits)
    • GEOG 601 Spatial Statistics and Modeling (3 credits)

    Select two elective courses (6 credits):

    • GEOG 525 Internet Geographic Information Systems (3 credits)
    • GEOG 532 Advanced GIS (3 credits)
    • GEOG 562 Advanced Spatial Analysis (3 credits)
    • GEOG 590 Applied GIS/Cartography (3 credits)
    • GEOG 5XX Programming GIS (3 credits)
    • GEOG 519 Spatial Analysis of Coastal Environments (3 credits)
    • GEOG 520 Marine Geography (3 credits)
    • GEOG 573 GIS in Emergency Management (3 credits)
    • GEOG 595 Topics: Geospatial Field Techniques (3 credits)

    Total concentration credit hours required: 12

    DASC Capstone Project (3 credits)
    MS DASC degree also requires a capstone project. For students seeking this concentration, they must complete a project focusing on geospatial analysis when taking the following:

    DASC 690 Capstone Project

    How To Apply

    • Bachelor's degree from regionally accredited institution or equivalent
    • Official transcripts from all institutions attended
    • Resume
    • Statement of professional goals
    • Undergraduate coursework or experience in computer science, mathematics, statistics, information technology, engineering, or a related field
    • Two letters of recommendation
    • GRE scores, with a 50% or better attainment on quantitative reasoning (or waiver)
    • Current scores on the Test of English as a Foreign Language (TOEFL) of at least 230 on the computer based TOEFL or 80 on the TOEFL iBT

    Additional Resources

    Contact

    ODU Graduate School
    2102 Monarch Hall
    Norfolk, VA 23529
    757-683-4885 (office)
    datascience@odu.edu