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Data Analytics student working
Master of Professional Studies in
Data Analytics

Courses

The curriculum of the 30-credit online MPS in Data Analytics can help you learn to design, deploy, and manage the technology infrastructure and data analytical processes of predictive analytics, including data aggregation, cleaning, storage, and retrieval.

You will take 9 credits in the program's core courses, 9 credits of prescribed courses offered to help you design and maintain data analytics systems and tools, and 9 credits of electives chosen in consultation with your program adviser. You will then complete your studies with the 3-credit culminating capstone experience.

Required Courses (9 credits)

  • 3
    credits

    Survey course on the key topics in predictive analytics. Students will learn methods associated with data analytics techniques and apply them to real examples using the R statistical system.

    • Prerequisite

      STAT 500 or equivalent

  • 3
    credits

    Practical benefits of data mining will be presented; data warehousing, data cubes, and underlying algorithms used by data mining software.

  • 3
    credits

    Descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one- and two-way ANOVA, chi-square tests, diagnostics.

    • Prerequisite

      one undergraduate course in statistics

Base Program Prescribed Courses (9 credits)

  • 3
    credits

    Examination of large-scale data storage technologies including NoSQL database systems for loosely-structured data, and warehouses for dimensional data.

    • Prerequisite

      INSC 521

  • 3
    credits

    Application and interpretation of analytics for real-life decision making.

    • Prerequisite

      STAT 500

  • 3
    credits

    The requirements capture, design, and development of relational database applications; analysis of business requirements and development of appropriate database systems.

Electives (select 9 credits)

  • 3
    credits

    This course covers basic as well as advanced concepts to gain a detailed understanding of Natural Language Processing tasks such as language modeling, text to speech generation, natural language understanding, and natural language generation. Students can learn the necessary skills to design a range of applications, including sentiment analysis, translating between languages, and answering questions. The practical implementation of these applications with deep neural networks is also discussed.

  • 3
    credits

    This course will teach the foundations of AI and give students a practical understanding of the field. This course gives an overview of the core concepts of AI, including the intelligent agents, knowledge and reasoning, reinforcement learning, planning and acting, belief networks, computational learning, Markov decision process, and more.

  • 3
    credits

    This course examines fundamental concepts and ideas in demography and U.S. and world population trends associated with these concepts.

  • 3
    credits

    This course provides an overview of key demographic data sets, and promotes familiarity with, and appropriate use of, these data.

  • 3
    credits

    This course provides an overview of applications in applied demography in business, nonprofit organizations, public policy, and health, including a focus on international applications.

  • 3
    credits

    This course will cover the foundations on neural networks and deep learning networks. It covers the core concepts of deep neural networks, including the convolutional neural networks for image recognition, recurrent neural networks for sequence generation, and generative adversarial networks for image generation.

    • Prerequisite

      STAT 500

  • 3
    credits

    This course will cover the main theory and approaches of reinforcement learning, along with deep learning and common software libraries and packages.

  • 3
    credits

    Examines tools and techniques required for data collection and computational procedures to automatically identify and eliminate errors in large datasets

    • Prerequisite

      STAT 500 and INSC 521

  • 3
    credits

    This course provides an exploration of current and emerging big data solutions for handling large quantities of data in real-time. In particular, this course investigates methods to design, develop, and implement several systems used for real-time data analysis and storage such as document databases, column-based databases, queueing systems, and real-time processing systems.

    • Prerequisite

      DAAN 825

  • 3
    credits

    This course will study the inter-relatedness of cyber-social and cyber-technical aspects of an organization or society as a whole.

  • 3
    credits

    This course will explore the development of analytics systems and the application of best practices and established software design principles using the Python programming language and its several toolkits.

  • 3
    credits

    This course provides a foundation in the principles, concepts, techniques, and tools for visualizing large data sets.

  • 3
    credits

    The course examines business intelligence in the era of big data. Emphasis is on the successful implementation of big data in large and small corporations that deliver extraordinary results.

  • 3
    credits

    Models and measures of vital processes (fertility, mortality, migration) and their effects on growth and age structure of human populations.

  • 3
    credits

    Exposes students to the spatial analysis tools and analytical methods applied to demographic research.

    • Prerequisite

      a graduate course in statistics

  • 3
    credits

    Introduction, intermediate, and advanced topics in SAS.

    • Prerequisite

      3 credits in statistics

  • 3
    credits

    Analysis of research data through simple and multiple regression and correlation; polynomial models; indicator variables; step-wise, piece-wise, and logistic regression.

  • 3
    credits

    Identification of models for empirical data collected over time. Use of models in forecasting.

    • Prerequisite

      STAT 462 or STAT 501 or STAT 511

  • 3
    credits

    Analysis and construction of project plans for the development of complex software products; how to manage change and cost control.

Culminating Capstone Experience (3 credits)

  • 3
    credits

    Design and implement data science and analytics systems using contemporary tools and techniques. Choice of project topic mutually determined by student and instructor. Students must complete all core and required courses before enrolling.

    • Prerequisite

      IN SC 521, DAAN 825, and DAAN 881

Course Availability

If you're ready to see when your courses will be offered, visit our public LionPATH course search (opens in new window) to start planning ahead.

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