Master of Science
in Data Science,
AI/Optimization
4-week
COURSES
Year-round
enrollment
220K+ Alumni Worldwide
Overview
The Master of Science in Data Science program prepares you for a career in data science by teaching you how to apply statistical methods in solving real-world problems. Core coursework includes data modeling; data management; and mining of continuous, categorical, and multivariable data. Advanced specializations focus on artificial intelligence and optimization, business analytics, database analytics, or health analytics. The program culminates in a three-month capstone where real data from sponsoring organizations (or, alternatively, publicly available data) will be used in a team project to demonstrate your mastery in data acquisition, cleaning, analysis, modeling, visualization, and reporting.
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.
Foundation Courses
For the Master of Science in Data Science degree with a specialization in AI/Optimization, you must complete seven foundation courses, four specialization courses, and three capstone courses. Completion of all foundation and specialization courses is required prior to starting the capstone course sequence.
Course Details
Foundation Course Listings
Course Name
An introduction to statistical modeling and data analysis. This course uses R programming to explore data variation, model data, and evaluate models. You’ll also learn to analyze and evaluate different types of regression models and error analysis methods.
In this course, you’ll learn to apply data analytics to facilitate modern knowledge discovery techniques. Coursework will focus on different forms of data, gap analysis, model building, and interpretation as foundations for analytical study.
This course applies the data management process to analytics. You’ll explore and learn the processes of acquiring and auditing data, assembling data into a modeling sample, performing basic data integrity checks, cleansing data, feature engineering, and data visualization.
An examination of data mining methods and predictive modeling. Through a variety of case studies and practical industry applications, you’ll explore design objectives, data selection and preparation, classification and decision tree methods, and predictive modeling.
In this course, you’ll apply methods for analyzing continuous data for knowledge discovery. Analytic continuous data concepts and methods are developed with practical skills in exploratory data analysis. Areas of focus will include descriptive statistics, goodness-of-fit tests, correlation measures, single and multiple linear regression, and analysis of variance and covariance. Coursework will use case studies and real world data to leverage statistical assessment and interpretation.
This course explores and applies methods for analyzing categorical data for knowledge discovery. Analytic categorical data analysis concepts and methods are developed with practical skills in exploratory data analysis. Areas of focus include descriptive statistics of discrete data, contingency tables, and methods of generalized linear models. Instruction will use case studies and real world data to leverage statistical assessment and interpretation.
An examination of advanced applications of data analytics for knowledge discovery. This course explores several advanced techniques in data analytics, including methods for longitudinal data, factor and principal components analysis, multivariate logistic regression, and multivariate analysis of variance (ANOVA). Coursework will use case studies and real world data to leverage statistical assessment and interpretation.
Specialization Courses
Those with no prior Python programming experience must complete ANA 500 prior to
ANA 670. Those with Python experience will complete ANA 505 after ANA 680.
Course Name
In this course, you’ll learn the Python programming language and apply it to a variety of data science applications.
This course teaches you to model optimization problems in a variety of machine learning and AI (artificial intelligence) applications. Instruction will also focus on identifying suitable optimization algorithms for different applications in industry.
This course applies neural network analytical methods to a variety of AI (artificial intelligence) applications using Python. You’ll also learn to analyze deep learning predictive models in industrial applications.
A study of how to deploy machine learning models in the cloud. Coursework will concentrate on optimizing machine learning models for a variety of applications in industry.
An introduction to and investigation of advanced topics in AI (artificial intelligence) and optimization in various state-of-the-art applications.
Capstone Courses
Course Name
The first of three capstone courses, this class comprises the first stage of your master’s thesis project. Through your research of analytic project design, problem framing, team-building, collaboration, and technical presentation, you’ll propose a data science project to advisors and stakeholders. Your team’s submission should include strategic and technical aspects of data acquisition, data cleaning, and analytic methodology.
This course is a continuation of your master’s-level research in analytic project implementation, technical writing, and project presentation. Your team’s data science project will include strategic and technical aspects of data acquisition, data cleaning, and analytic methodology for presentation to your project advisors and stakeholders.
In this course, your team will complete and present your master’s-level data science project. The finished project will include strategic and technical aspects of data analysis and visualization, and will be presented in a written thesis to your project advisors and stakeholders.
Learning Outcomes
Students earning the Master of Science in Data Science with a AI/Optimization specialization will learn to:
- Use Python for AI and machine learning applications in data science
- Explore optimization methods and algorithms
- Evaluate neural networks and deep learning models
- Deploy machine learning models in the cloud
- 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 to improve integrated data use
- Construct data files using statistical and data programming to solve practical problems in data analytics
- Design and implement an analytic strategy for a potential issue relevant to the community and stakeholders
- Develop team skills to research, develop, and evaluate analytic solutions to improve organizational performance
Program Disclosure
Successful completion and attainment of National University degrees do not lead to automatic or immediate licensure, employment, or certification in any state/country. The University cannot guarantee that any professional organization or business will accept a graduate’s application to sit for any certification, licensure, or related exam for the purpose of professional certification.
Program availability varies by state. Many disciplines, professions, and jobs require disclosure of an individual’s criminal history, and a variety of states require background checks to apply to, or be eligible for, certain certificates, registrations, and licenses. Existence of a criminal history may also subject an individual to denial of an initial application for a certificate, registration, or license and/or result in the revocation or suspension of an existing certificate, registration, or license. Requirements can vary by state, occupation, and/or licensing authority.
NU graduates will be subject to additional requirements on a program, certification/licensure, employment, and state-by-state basis that can include one or more of the following items: internships, practicum experience, additional coursework, exams, tests, drug testing, earning an additional degree, and/or other training/education requirements.
All prospective students are advised to review employment, certification, and/or licensure requirements in their state, and to contact the certification/licensing body of the state and/or country where they intend to obtain certification/licensure to verify that these courses/programs qualify in that state/country, prior to enrolling. Prospective students are also advised to regularly review the state’s/country’s policies and procedures relating to certification/licensure, as those policies are subject to change.
National University degrees do not guarantee employment or salary of any kind. Prospective students are strongly encouraged to review desired job positions to review degrees, education, and/or training required to apply for desired positions. Prospective students should monitor these positions as requirements, salary, and other relevant factors can change over time.