Master of Science in Data Science
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Learn how to blend business, computer science, and statistics to solve real-world problems.
Machine learning. Neural networks. Artificial intelligence. What does it all mean, and where is it taking the field of data science? To address the spectrum of issues in data science and analytics, National University designed an industry-current curriculum that includes core courses in statistical topics as well as four specializations for advanced applications of data science and analytics in unique fields: Artificial Intelligence and Optimization, Business Science and Analytics, Database Science and Analytics, and Health Science and Analytics.
NU’s Master of Science in Data Science focuses on 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, analytic programming, and artificial intelligence.
The National Science Foundation (NSF) has awarded a $20 million grant to a partnership of prestigious universities led by University of California, San Diego, and including National University, Yale University, the Massachusetts Institute of Technology, the University of Pennsylvania, and the University of Texas at Austin. Working together, the universities will form The Institute for Learning-enabled Optimization at Scale (TILOS).
Our five-year research partnership will focus on the optimization of artificial intelligence (AI) and machine learning. We will work closely with industry leaders to develop optimization tools that will enable real-world improvements in key industries, including chip design, robotics, and communications networks. State-of-the-art analytical software will be used in all courses.
The Western Association of Schools and Colleges (WASC) accredits public and private schools, colleges, and universities in the U.S.
Course Details
Program Requirements
- 15 courses; 67.5 quarter units
Core Requirements
- 7 courses; 31.5 quarter units
Introduction to statistical modelling and data analysis using R programming to explore data variation, model the data, and evaluate the models. Analysis and evaluation of different types of regression models and error analysis methods.
Prerequisite: ANA 600
Forms of data, gap analysis, model building, and interpretation will form the foundation for students to ethically apply data analytics to facilitate modern knowledge discovery techniques.
Prerequisite: ANA 605
Application of the data management process for analytics including acquiring and auditing data, assembling data into a modeling sample, performing basic data integrity checks, cleansing data, feature engineering and data visualization.
Prerequisite: ANA 610
Application of data mining methods and predictive modeling. Design of objectives, data selection and preparation, analytic method selection such as classification and decision trees, and predictive modeling will be used for a variety of case studies and practical industry applications.
Prerequisite: ANA 615
Application of methods for analyzing continuous data for knowledge discovery. Analytic continuous data concepts and methods are developed with practical skills in exploratory data analysis. Descriptive statistics, goodness-of-fit tests, correlation measures, single and multiple linear regression, and analysis of variance and covariance are covered. Applying continuous data methods using case studies and real world data will leverage statistical assessment and interpretation.
Prerequisite: ANA 620
Application of methods for analyzing categorical data for knowledge discovery. Analytic categorical data analysis concepts and methods are developed with practical skills in exploratory data analysis. Descriptive statistics of discrete data, contingency tables, and methods of generalized linear models are covered. Applying categorical methods using case studies and real world data will leverage statistical assessment and interpretation.
Prerequisite: ANA 625
Advanced application of data analytics methods for knowledge discovery. This course will explore several of the advanced topics in data analytics such as methods for longitudinal data, factor and principal components analysis, multivariate logistic regression, and multivariate analysis of variance (ANOVA). Application using case studies and real world data will leverage statistical assessment and interpretation.
Students must select one area of specialization.
Capstone Requirements
- 3 courses; 13.5 quarter units
Students must complete all core and area of specialization courses prior to starting the capstone course sequence.
Prerequisite: All core and specialization courses in an analytics program with a minimum GPA of 3.0 or approval of Lead Faculty.
Master’s level research in analytic project design, problem framing, and technical presentation. Team building, team collaboration, and conflict resolution are implemented in the proposal of a data science project. Strategic and technical aspects of data acquisition, data cleaning, and analytic methodology are proposed and presented to project advisors and stakeholders.
Prerequisite: ANA 699A
Continuation of master’s level research in analytic project implementation, technical writing, and project presentation. Strategic and technical aspects of data acquisition, data cleaning, and analytic methodology are implemented and presented to project advisors and stakeholders.
Prerequisite: ANA 699B
Completion of master’s level research in analytic project implementation, technical writing, and project presentation. Strategic and technical aspects of data analysis and visualization are implemented and presented to project advisors and stakeholders in a written thesis.
Specializations
Specialization in Business Analytics Requirements
- 5 courses, 22.5 quarter units
Performance Management (PM) and Supply Chain Management (SCM) require metrics and indicators to measure value, weaknesses and opportunities through business intelligence. Using data to set objectives and measure the internal and external performances through analytics has been a proven method to business success. Business analytics provide a proactive approach to identify and solve problems before it takes place. Data improvement, data quality assessment, data cleansing and normalization, methods and process improvements will be discussed.
New technologies have opened new arenas in prediction and marketing. Subjects of predictive analytics topics and its role in enterprise marketing will be discussed. The course applies predictive analytic tools to derive the organization’s strategic direction. Market and product analysis will be used to illustrate the development process. Results will be drawn from actual predictive analytics applications and interpreted in the context of business impact.
Financial world faces uncertainty that affects the outcome of sound investments. Leaders are utilizing probabilistic analytic models that alleviate ambiguity on making decision for profitable returns. Theories and practical tools focusing on model building; constructing, processing, and presenting probabilistic information will be discussed. Utilization of analytical software to solve problems on axioms of probability, conditioning and probability trees, random variables and distributions expectation.
Every step of online transactions should be considered with security in mind. Accessing the organizations’ data requires operators to apply the proper security and privacy while the data is stored, transmitted, accessed and when it is worked on. Work with confidential data involves strong ethical practices to be aware of security breaches and how to mitigate threats.
Students who have prior experience with Python Programming complete ANA 505, after ANA 680.
Investigate advanced topics in Artificial Intelligence and Optimization in state-of-the-art applications.
Specialization in Database Analytics Requirements
- 5 courses, 22.5 quarter units
Analysis of database design and implementation for analytical applications in “big data.” Topics include requirements collection, conceptual and logical database design, normalization, an introduction to SQL, and the designing of a data mart.
Prerequisite: ANA 650
A course on how to design and develop a data warehouse application for “big data”. Topics include user requirement collection, dimensional modeling, ETL (Extraction, Transformation, Loading) procedures, information access and delivery, as well as the optimization and long-term maintenance of a data warehouse.
Prerequisite: ANA 655
An in-depth treatment of data manipulation with Structured Query Language (SQL). This course covers views, triggers, sequences, reporting, sub-queries, query optimization and how to use SQL for data warehouse manipulation.
Prerequisite: ANA 660
This advanced data mining course focuses on various machine learning and artificial intelligence techniques. Topics include data mining methods ranging from classification rules, association rules, and instance-based learning to semi-supervised learning and multi-instance learning.
Students who have prior experience with Python Programming complete ANA 505, after ANA 680.
Investigate advanced topics in Artificial Intelligence and Optimization in state-of-the-art applications.
Specialization in Health Analytics Requirements
- 5 courses, 22.5 quarter units
Prerequisite: ANA 630
Effective data and information technology utilization to improve performance in healthcare organizations: including information systems, databases and analytical tools to structure, analyze and present information; legal and ethical issues affecting management of healthcare information.
Prerequisite: COH 602, or ANA 630
The study of determinants and distribution of disease and disability in human populations. Empirical analysis of population data related to morbidity and mortality. Investigation of disease outbreaks, risk factors, health outcomes and causal relationships. Critical evaluation of public health literature and study design.
Application of health data analytics to improve health results in clinical care. The focus will be on data integration and analysis from the perspective of patient care, decision support, and quality control for evidence-based solutions.
Application of health data analytics to guide decisions about the health of populations and individuals. Population and individual level data integration and analysis will be conducted to provide evidenced-based solutions in clinical trials and assessment of recovery time, patient stays, risk of complications, morbidity, and mortality.
Students who have prior experience with Python Programming complete ANA 505, after ANA 680.
Investigate advanced topics in Artificial Intelligence and Optimization in state-of-the-art applications.
Specialization in AI / Optimization
Students must complete a minimum of 22.5 quarter units for the AI/Optimization Specialization.
Total Specialization Requirements:
- 5 courses; 22.5 quarter units
Recommended Preparation: Prior experience in computer programming languages such as R is helpful.
Learn python programming language and apply to data science applications.
Prerequisite: ANA 500
Model optimization problems in a variety of applications in machine learning and artificial intelligence. Identify suitable optimization algorithms for different applications in industry.
Prerequisite: ANA 670
Apply neural network analytical methods to a variety of applications in artificial intelligence using python. Analyze deep learning predictive models in industrial applications.
Prerequisite: ANA 675
Deploy machine learning models in the cloud. Optimize ML models for a variety of applications in industry.
Students who have prior experience with Python Programming complete ANA 505, after ANA 680.
Investigate advanced topics in Artificial Intelligence and Optimization in state-of-the-art applications.
Degree and Course Requirements
To obtain National University’s Master of Science in Data Science, students must complete at least 63 graduate units. A total of 13.5 quarter units of graduate credit may be granted for equivalent graduate work completed at another regionally accredited institution, as it applies to this degree, and provided the units were not used in earning another advanced degree. Please refer to the graduate admissions requirements for specific information regarding application and evaluation.
Specializations
Master of Science in Data Science, AI/Optimization
The AI/Optimization specialization provides professionals with Python programming knowledge and skills in data science applications, including optimization methods, neural networks, deep learning, and model deployment in the cloud.
Learn MoreMaster of Science in Data Science, Database Analytics
The Database Analytics specialization prepares professionals for developing, implementing, and maintaining the tools needed to make efficient and effective use of big data. Instruction and coursework will focus on databases, data marts, data warehouses, machine learning, and analytic programming for applications in AI and optimization.
Learn MoreMaster of Science in Data Science, Business Analytics
The Business Analytics specialization is designed for professionals who want to improve business decision-making by applying scientific knowledge and tools to big data. With organizations measuring and planning every part of their operations through data analysis, demand for those with the knowledge and skills to turn raw data into better decisions has never been greater.
Learn MoreMaster of Science in Data Science, Health Analytics
The Health Analytics specialization is designed to provide data professionals with practical learning experience through application of statistical methods to help solve real-world health and life science analytics problems. Instruction and coursework will explore healthcare information management systems, epidemiology, health management, clinical research, clinical trials, and health-outcomes research.
Learn MoreMaster of Science in Data Science, AI Leadership
This specialization develops AI literacy and leadership through hands-on experimentation by developing skills to use and assess AI-enabled applications in a variety of areas. Provides an overview of legal and ethical concerns with AI to mitigate risks and optimize organizational strategies. Explores advanced AI concepts in machine learning, XAI, robotics, and chatbots, with no coding required.
Learn MoreWith rapid and ongoing advancements in technology, the field demands a steady supply of industry-current workers with the knowledge and skills to solve real-world challenges through statistical methods. The Bureau of Labor Statistics states that employment of computer and information research scientists is projected to grow 22 percent by 2030. That’s much faster than the average for all occupations.*
National University’s curriculum has been designed to include core courses in statistical topics as well as areas for advanced applications of data analytics in unique fields, including business analytics, database analytics, health analytics, and artificial intelligence and optimization.
You should also know the National Science Foundation (NSF) has awarded a $20 million grant to a partnership of prestigious universities led by University of California, San Diego, and including National University, Yale University, the Massachusetts Institute of Technology, the University of Pennsylvania, and the University of Texas at Austin. Working together, we will form The Institute for Learning-enabled Optimization at Scale (TILOS).
This five-year research partnership will focus on the optimization of artificial intelligence (AI) and machine learning. They will work closely with industry leaders to develop optimization tools that will enable real-world improvements in key industries, including chip design, robotics, and communications networks.
If you’re looking to be a difference-maker with the capacity to design innovative uses for new and existing computer technology, NU’s MS in Data Science is a great place to start building your credentials. You’ll learn from faculty members with practical experience in the field, and you’ll receive dedicated support when it comes to networking opportunities with leaders in the field.
National University’s MS in Data Science program focuses on advanced topics like 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, analytic programming, and artificial intelligence and optimization. With this knowledge, you’ll be equipped with the industry-current credentials needed to pursue in-demand positions* like:
- Data scientists
- Data analysts
- Data engineers
- Data Science and Analytics Managers
- Data architects
- Business Intelligence Analysts
Employer seeking data science professionals span a large range of service and manufacturing settings. For example, top employers of computer and IT professionals include IBM, Microsoft, and Facebook, while JP Morgan Chase, Wells Fargo, and Travelers Group regularly recruit finance and insurance professionals with an MS in Data Science.
In the consulting world, Deloitte, KPMG, and Accenture are top employers, and Humana and Anthem dominate the healthcare industry. If you’re interested in biotech or pharmaceutical manufacturing, keep Johnson and Johnson and Bayer on your radar. Ryder and Uber are two top employers in the transportation sector.
With your MS in Data Science, you can not only expect to be in demand, you’re also likely to be well compensated. The Bureau of Labor Statistics states that the median annual wage for management analysts $93,000 in May 2021, and the highest 10 percent earned more than $163,760.**
*SOURCE: Emsi Labor Analyst- Report. Emsi research company homepage at https://www.economicmodeling.com/company/ (Report viewed: April 27, 2022. DISCLAIMER: The data provided is for Informational purposes only. Emsi data and analysis utilizes government sources to provide insights on industries, demographics, employers, in-demand skills, and more to align academic programs with labor market opportunities. Cited projections may not reflect local or short-term economic or job conditions and do not guarantee actual job growth. Current and prospective students should use this data with other available economic data to inform their educational decisions.
**SOURCE: https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm#tab-5
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. Students often complete these projects for their current employers.
With this capstone, you’ll get a hands-on look at how critical data and analytics are to organizations and apply the skills you have gained throughout the program in a real-world scenario in the field.
Program Learning Outcomes
As a graduate of National University’s Master of Data Science program, you’ll understand how to:
- Integrate components of data science 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 and implement 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.
Take the first step towards a career in data science. Apply online today.
Hear From Our Faculty
Watch our video to learn more about the MS in Data Science from Dept. of Engineering and Computing featuring Dr. Ronald Uhlig, Academic Program Director, Department of Engineering.
“The design of the curriculum and the program matches what the industry is looking for: a blend of business, computer science, and statistics. The Master of Science in Data Science enables our students to apply statistical methods to solve real-world problems and prepare for careers in data science.”
– Dr. Jodi Reeves Associate Dean of COPS, Academic, Program Director, MS in Data Science
Admissions
Enrolling in a university is a big decision. That’s why our dedicated admissions team is here to guide you through the admissions process and help you find the right program for you and your career goals.
To that end, we’ve simplified and streamlined our application process, so you can get enrolled in your program right away. Because we accept and review applications year round, you can begin class as soon as next month, depending on your program and location of choice.
Learn more about undergraduate, graduate, military, and international student admissions, plus admissions information for transfer students. You can also learn more about our tuition rates and financial aid opportunities.
To speak with our admissions team, call (855) 355-6288 or request information and an advisor will contact you shortly. If you’re ready to apply, simply start your application today.
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Frequently Asked Questions
As part of an initiative to establish a series of artificial intelligence research institutes nationwide, the National Science Foundation (NSF) has awarded a $20 million grant to a partnership of prestigious universities led by University of California, San Diego, and including National University, Yale University, the Massachusetts Institute of Technology, the University of Pennsylvania, and the University of Texas at Austin.
Working together, the universities will form The Institute for Learning-enabled Optimization at Scale (TILOS). The five-year research partnership will focus on the optimization of artificial intelligence (AI) and machine learning. We’ll work closely with industry leaders to develop optimization tools that will enable real-world improvements in key industries, including chip design, robotics, and communications networks.
The MS in Data Science program is designed for adult learners and can be completed in as few as 14 months. One unique advantage NU has over other universities is that we offer 4-week courses, and our admissions team reviews applications year-round. This innovative course design lets you start sooner, finish faster, and have the flexibility to juggle your work and family commitments along with your studies.
The demand for data-driven decision makers is growing every year. All corporate functions, from marketing teams to C-suite executives, are looking to data experts for answers to major business questions. Given that, a career in data science can also be lucrative. According to the Bureau of Labor Statistics*, the median salary in 2021 for a data scientist was $131,490 per year.*
A master’s degree in data science may help you increase your earning potential in this field and become a more valuable team member in any forward-thinking company.
Data analytics is a subset of data science (which includes a trifecta of applied statistics, computer programming, and domain knowledge). Data analytics entails looking at raw data from a source (such as a database), cleaning it, processing it, and transforming it into a form that can be easily understood by others.
Data Scientists create sophisticated analytical models used to build new datasets and derive new insights from data.
Data Analysts leverage data analysis and modeling techniques to solve problems and gather insight across functional domains.
Data science jobs help organizations make data informed business decisions in a number of areas in finance, accounting, marketing, information services, operations and other critical areas. Key skills associated with data science are:
- Python
- Machine Learning
- SQL
- Data Analysis
- Computer Science
- R (statistical computing language)
- Artificial Intelligence
- Statistics
According to Emsi labor market analytics and economic data1, Data science jobs span a wide number of industry settings including :
- Computer and information technology (IBM, Microsoft, Facebook)
- Finance (banks and financial institutions like JP Morgan Chase, Wells Fargo)
- Insurance (Travelers Group, Humana, Anthem)
- Management consulting firms (Deloitte, KPMG, Accenture)
- Healthcare providers and systems (Teladoc, HCA Healthcare, Centene, Ascension)
- Biotechnology and pharmaceutical manufacturing (Johnson and Johnson, Bayer)
- Transportation (Ryder, Uber)
- Retail (Amazon)
SOURCE: Emsi Labor Analyst- Report. Emsi research company homepage at https://www.economicmodeling.com/company/ (Report viewed: 2/17/2022). DISCLAIMER: The data provided is for Informational purposes only. Emsi data and analysis utilizes government sources to provide insights on industries, demographics, employers, in-demand skills, and more to align academic programs with labor market opportunities. Cited projections may not reflect local or short-term economic or job conditions and do not guarantee actual job growth. Current and prospective students should use this data with other available economic data to inform their educational decisions.
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