Master of Science
in Data Science,
Business Analytics
4-week
COURSES
Year-round
enrollment
230K+ 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 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.
Foundation Courses
For the Master of Science in Data Science degree with a specialization in Business Analytics, you must complete seven foundation courses, five 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
Performance management (PM) and supply chain management (SCM) require metrics and indicators to measure value, weaknesses, and opportunities. Using data to set objectives and measure performance through analytics has been a proven method for proactive problem-solving and business success. This course focuses on the application of data analytics to performance and process management. Topics of study include data improvement, data quality assessment, data cleansing and normalization, and process improvement.
New technologies have opened new arenas in prediction and marketing. This course introduces a variety of predictive analytics topics and explains their role in enterprise marketing. You’ll apply predictive analytics applications to derive an organization’s strategic direction. Market and product analysis will be used to illustrate the development process. Finally, you’ll learn to interpret analysis results in terms of business impact.
The uncertainties of the financial world affect the outcomes of sound investments. Probabilistic analytic models help to alleviate the ambiguity inherent in investment decision-making. In this course, you’ll explore probability theories, tools, and model-building as you learn to construct, process, and present probabilistic information. Topics of focus include: conditioning and probability trees, random variables, distributions expectation, and problem-solving for axioms of probability.
Every step of an online transaction should be considered with security in mind. In this course, you’ll examine the protocols and practices that maintain proper security and privacy while confidential data is accessed, used, transmitted, and stored. You’ll also consider how to mitigate threats and establish ethical practices that alert organizations to potential security breaches.
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 Business Analytics specialization will learn to:
- Evaluate data models to analyze the performance of supply chain processes
- Analyze data to predict business outcomes in marketing processes
- Design a probabilistic finance model to forecast business outcomes
- Apply security, privacy, and ethical measures to business processes using data and analytical models
- 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.