Our 8 course, 32-credit online Master of Science in Data Science degree prepares you to harness the power of big data using the latest tools and analytical methods. Progress through your studies according to your schedule with as much or as little support as you need.
Key features of the Master of Science in Data Science include:
- Curriculum based on real world application of skills based on the DAMA International DMBoK Framework.
- Learn to apply all elements of the data process in the context of the data workflow of companies and organizations today.
- Data management and data engineering are covered in depth, as well as advanced statistical modeling, algorithms, and machine learning.
- Whether you are an expert or a novice programmer, the program is personalized to enhance your ability to use cutting edge tools across the data life-cycle.
- Capstone project allows you to solve real world problems including projects from your employer.
Foundations of Data Management provide students exposure to fundamental data management skills used in modern information systems that support various operational and functional areas within a business organization. Topics covered in the course has an emphasis on how data is fundamentally identified, organized, described and managed as the most valued asset within an organization. Course emphasis is also on applied learning of concepts and skills for relational data modeling and querying. This course will help prospective Data Science and Analytic business professionals to develop and apply data management skills that will be essential to the success in subsequent coursework.
This course provides students with a foundation in statistical basic statistical analysis, focusing primarily on descriptive univariate statistics. Topics addressed in this course include variables and their properties, measurement scales, descriptive analyses of continuous and categorical variables, Central Limit Theorem, univariate and bivariate estimation, and hypothesis testing logic and procedures. Students will be exposed to hands-on computational examples using R and SPSS as they learn how to apply the various statistical concepts covered in this course to real-life business situations.
This course will endow students with a foundation in basic concepts comprising research design and analytic planning, as looked at from the perspective of applied business analytics. The topics covered in this course include the continuum of data--information--knowledge, an introduction to the sampling theory, an overview of the concept and the application of statistical significance, confidence intervals and statistical inference, and an introduction to the key tenets of statistical impact measurement. Lastly, students will also be given an overview of the step-by-step process of analytic planning.
This course offers students a hands-on introduction to the basic concepts, practices and applications of developing forward-looking - i.e., predictive - statistical models. Content-wise, the class begins with a conceptual overview of multivariate statistics, followed by a discussion of commonly used types of multivariate predictive models. The second part of the course focuses on hands-on applications of model fitting and evaluation using scripring (R and/or SPSS Statistics) and GUI/object-based (SPSS Modeler) applications.
This course introduces data science students to the foundations of statistical machine learning, which are automated methods of broadly scoped - i.e., descriptive, predictive, explanatory - data analyses. The first part of the course offers a conceptual overview of established and emerging machine learning algorithms, followed, in the second part, by a discussion and hands-on introduction to some of the more widely used machine learning platforms (e.g. R, SAS Enterprise Miner, SPSS Modeler). Some of the specific topics include unsupervised and supervised learning, text mining, data preparation and result interpretation.
This course focuses on the effective communication of data analysis and its insights and implications. Students will learn the principles and techniques for information visualization and representation as well as verbal and written communication. Students will develop proficiency in several of the latest tools for visualization. Students will use real-world business scenarios to gain experience designing and building data visualization and communication. Best practices will be highlighted and students will receive tailored individual coaching and feedback sessions to accelerate skill improvement.
This course introduces students to the current and emerging topics and considerations addressing the issue of data governance, usage and security; furthermore, this course also investigates the ethical and legal right and responsibilities of data analysts, and delves into questions that emerge from developing, storing, analyzing and using data. Issues including intellectual property, data ownership, storage security and safeguards as well as the human impact of using data are investigated; case studies and scenario explorations in a range of industries from consumer products and retail to government and social justice are used to illustrate and apply the concepts discussed in class, with the goal of preparing students to manage these often grey issues during their career.
The final course in the Data Science curriculum, the Capstone is a practicum focused on an analytic skill set that is an essential part of a data scientist skillset: multi-source analytics. Unlike the earlier courses which combined theory and practice, this course is heavily skewed toward the latter, giving students the opportunity to focus the bulk of the 8 week course period on fine-tuning their hands-on computational programming and related skills.