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The purpose of the M.S. degree program in Data Science and Analytics is to provide students with the knowledge and skills to use state-of-the-art programming tools and software packages to develop statistical models. Students will learn to use data for identifying trends and patterns, solving problems, communicating results, and recommending optimal solutions. Students will acquire knowledge, skills, and abilities about the discipline-specific scientific and theoretical concepts critical to data science and analytics.
Consists of six core courses (18 credits)
Allows students to choose a concentration in:
• Computational Data Analytics
• Business Intelligence & Analytics
• Engineering & Big Data Analytics
• Geospatial Analytics
Includes a capstone project in all concentrations
Provides project-based learning, and students will discover how to use data from different areas such as business, science, engineering, and geospatial to solve real-world problems
Graduates will be knowledgeable and skilled at developing statistical models to detect trends, organizing and managing data, and leading teams in retrieving, cleaning data, and modeling data.
Data science and analytics is being recognized as the key discipline in exploiting ever-growing data to solve challenging problems facing all economic sectors. The latest UN E-Government Survey 2018 concludes that the fourth industrial revolution and convergence of big data technologies and machine learning, is making a dramatic shift towards more data and machine-driven societies. Graduates of the proposed Master of Science degree program in Data Science and Analytics will have the skills, ability, and workplace competencies needed for employment in the field of data science. Specifically, they will have:
Career Opportunities in Various Industries
Example companies looking to hire data science and analytics graduates:
According to Paysa, average salary for a data scientist is close to $100K.
This course will explore data science as a burgeoning field. Students will learn fundamental principles and techniques that data scientists employ to mine data. They will investigate real life examples where data is used to guide assessments and draw conclusions. This course will introduce software and computing resources available to a data scientist to process, visualize, and model different types of data including big data. Cross-listed with DASC 620.
This course covers the theory and application of data visualization. This includes issues in data cleaning to prepare data for visualization, theory behind mapping data to appropriate visual representations, introduction to visual analytics, and tools used for data analysis and visualization. Modern visualization software and tools will be used to analyze and visualize real-world datasets to reinforce the concepts covered in the course.
This course will serve as an introduction for modeling data using probability and statistical methods. Topics include basic concepts of probability, Bayes theorem, frequently-occurring discrete and continuous probability distributions, as well as how to simulate data from these distributions. Basic properties of the probability distributions will be discussed, which will provide an insight into the use of these distributions in data science. The course will also cover bivariate and conditional distributions, linear correlation and statistical inference concepts that include likelihood, parameter estimation, and goodness of fit. Prerequisite: STAT 330 or equivalent or permission of the instructor.
Students entering the Master of Science program in Data Science & Analytics should meet the minimum university admission requirements (Graduate Admission)
A completed online application and associated application fee
A baccalaureate degree in computer science, electrical and/or computer engineering, mathematics, statistics, information system and technology or a related field from a regionally-accredited institution or an equivalent institution outside the U.S.; students holding bachelor's degrees in an unrelated field will need competency in topics related to basic statistics and computer science such as: differentiation and integration, vectors and matrices, determinants and matrix inverse, elementary statistics and probability, basic programming, software development and testing, and C++/java concepts.
Official copies of transcripts of all regionally-accredited institutions attended (or equivalent non-U.S. institutions)
Two letters of recommendation from individuals familiar with the applicant's professional and/or academic background
A current resume
A statement of professional goals
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
Estimated rates for the 2021-22 academic year. Rates are subject to change. Anyone that is not a current Virginia resident will be charged non-resident rates. That includes international students.
Here are a few ways for you to save on the cost of attending ODU. For more information visit University Student Financial aid
The Graduate School web page will provide updates as to resources, scholarships, assistantships. In addition:
Our enrollment coordinators are ready to help you through the admissions process.
3323 ENGR & COMP SCI BLDG, NORFOLK, VA, 23529
1000 Rollins Hall, Norfolk, VA 23529
2101 Dragas Hall, Norfolk, VA 23529