MS in Data Science
To address the spectrum of issues in data analytics, this curriculum has been designed to include core courses in statistical topics as well as areas for advanced applications of data analytics in unique fields. Core topics include data modeling, data management, data mining, continuous and categorical data methods and applications, teamwork, and communication. Advanced topics include how to develop, implement, and maintain the hardware and software tools needed to make efficient and effective use of big data including databases, data marts, data warehouses, machine learning, and analytic programming. State-of-the-art analytical software will be used in all courses.
The culmination of this program is a three-month capstone project where real data from sponsoring organizations or publicly available data will be used to solve specialized problems in analytical database design, programming, implementation, or optimization.
Previous academic studies or industrial experience in such areas as math, statistics, computer programming, engineering, or science are helpful prerequisites for this masters program. This degree is appropriate for both experienced professionals as well as recent college graduates.
- All entering undergraduate students without prior college level English and/or Math courses must take the ACCUPLACER mathematics and English evaluation as part of the admissions process. The results of the evaluation are printed immediately and a copy is provided to the student. Read more about Placement by Evaluation. The ACCUPLACER may be taken once at no charge. Subsequent examinations can be repeated after 14 days for a $5 fee. Applicants who have not earned an Associate’s degree from a regionally accredited institution or who have completed fewer than 90 quarter (60 semester) units of transferable college credit must have graduated from high school, passed a high school level proficiency test or received a Certificate of Proficiency from a state Department of Education.
- Applicants who are considered first time students based on the criteria below must attend an orientation course through WES Training and Development prior to enrolling.
- Never attended a college or university
- Education experience is limited to:
- Competency based education
- College level courses completed during high school
- Advanced Placement (AP) credit
- Military service or training
- Prior learning credit (CLEP, Dantes, Excelsior, portfolio assessment, etc.)
- High school graduates transferring from regionally accredited colleges and universities are admitted as degree students if their cumulative GPA is 2.0 or higher. Applicants with a GPA below 2.0 may be admitted on probationary admission if the Committee on the Application of Standards (Committee) judges that there is sufficient evidence of potential to complete college studies. Applicants below a 2.0 may submit a petition letter to their advisor for review by the Committee.
- Individual degree programs may have additional admission requirements.
- Complete an application for admission and enrollment agreement.
For more information about admission requirements, please visit the university website.
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- Fundamentals of Analytics
- Analytic Models & Data Systems
- Data Management for Analytics
- Data Mining Techniques
- Continuous Data Methods, Appl
- Categorical Data Methods, Appl
- Advanced Analytic Applications
- Analytic Capstone Project I
- Analytic Capstone Project II
- Analytics Capstone Project III
Specialization in Business Analytics
Requirements for the Specialization
- Performance MGT & SCM Process
- Prediction in Marketing
- Probabilistic Finance Models
- Analytical Security & Ethics
Specialization in Database Analytics
Requirements for Specialization
- Database Design for Analytics
- Data Warehouse Design & Devel
- Advanced SQL Programming
- Data Mining & Machine Learning
Specialization in Health Analytics
Requirements for the Specialization
- Healthcare Information Systems
- Clinical Research Analytics
- Health Outcomes Research
Scholarships & funding
Several scholarship options are available. Please check the university website for more information.
On Campus or Online
Study when and where it’s convenient for you with evening, weekend, and 100% online classes.
Please visit the university website for more information about tuition fees.
Program Learning Outcomes
- Integrate components of data analytics to produce knowledge-based solutions for real-world challenges using public and private data sources.
- Evaluate data management methods and technologies used to improve integrated use of data.
- Construct data files using advanced statistical and data programming techniques to solve practical problems in data analytics.
- Design an analytic strategy to frame a potential issue and solution relevant to the community and stakeholders.
- Develop team skills to ethically research, develop, and evaluate analytic solutions to improve organizational performance.
- Design data marts.
- Analyze complex database queries for real-world analytical applications.
- Design medium to large data warehouses.
- Evaluate machine learning methods and strategies for advanced data mining.
About this institute
Since 1971, National University (NU) has been dedicated to meeting the needs of our students by providing accessible, affordable, and achievable higher education opportunities. Since its founding, the NU community has grown to 25,000 students (400 international students from over...
Why study at National University
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