Master of Science in Data Science
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, Berkshire 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.
Core Requirements
ANA 600 Fundamentals of Analytics – 4.50
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.
ANA 605 Analytic Models & Data Systems – 4.50
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.
ANA 610 Data Management for Analytics – 4.50
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.
ANA 615 Data Mining Techniques – 4.50
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.
ANA 620 Continuous Data Methods, Appl – 4.50
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.
ANA 625 Categorical Data Methods, Appl – 4.50
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.
ANA 630 Advanced Analytic Applications – 4.50
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.
Capstone Requirements
ANA 699A Analytic Capstone Project I – 4.50
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.
ANA 699B Analytic Capstone Project II – 4.50
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.
ANA 699C Analytics Capstone Project III – 4.50
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.
Specialization in Business Analytics Requirements
BAN 640 Performance MGT & SCM Process – 4.50
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.
BAN 645 Prediction in Marketing – 4.50
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.
BAN 650 Probabilistic Finance Models – 4.50
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.
BAN 655 Analytical Security & Ethics – 4.50
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.
ANA 505 AI & Optimization Topics – 4.50
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
ANA 650 Database Design for Analytics – 4.50
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.
ANA 655 Data Warehouse Design & Devel – 4.50
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.
ANA 660 Advanced SQL Programming – 4.50
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.
ANA 665 Data Mining & Machine Learning – 4.50
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.
ANA 505 AI & Optimization Topics – 4.50
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
HCA 626 Healthcare Information Systems – 4.50
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.
COH 606 Epidemiology – 4.50
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.
ANH 604 Clinical Research Analytics – 4.50
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.
ANH 607 Health Outcomes Research – 4.50
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.
ANA 505 AI & Optimization Topics – 4.50
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
ANA 500 Python for Data Science – 4.50
Recommended Preparation: Prior experience in computer programming languages such as R is helpful.
Learn python programming language and apply to data science applications.
ANA 670 Applied Optimization Methods – 4.50
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.
ANA 675 Neural Network & Deep Learning – 4.50
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.
ANA 680 Machine Learning Deployment – 4.50
Prerequisite: ANA 675
Deploy machine learning models in the cloud. Optimize ML models for a variety of applications in industry.
ANA 505 AI & Optimization Topics – 4.50
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 Berkshire 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.
Program Learning Outcomes
As a graduate of Berkshire 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.
Admissions
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