Master of Science
in Data Science,
AI Leadership
4-week
COURSES
Year-round
enrollment
240K+ Alumni Worldwide
Overview
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.
Foundation Courses
For the Master of Science in Data Science degree with a specialization in AI Leadership, 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
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.
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
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.
Learning Outcomes
Students earning the Master of Science in Data Science with a AI Leadership specialization will learn to:
- Explain how AI models work, including how they are developed and trained.
- Explore emerging AI laws, regulations, and policies.
- Analyze opportunities to leverage AI in support of organizational business strategies.
- Communicate technical information about AI systems effectively with a variety of stakeholders.
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.