Master of Science Data Analytics

The mission of the Master of Science in Data Analytics program is to provide graduate learners with the skills needed to be successful data analyst, operational analyst and leader in a competitive global Information Technology arena. To develop a complete and thorough understanding of the main key technologies that constitutes data science, statistics, data mining, problem analysis, big data and decision making. To provide students with the hands-on experience and the theoretical foundation of Data Analytics. The program seeks to enable students to develop technical and quantitative and data analysis techniques and skills by participating in competency-based projects focused on the development, integration, deployment, and management of data for the solution of real-world problems. The program uses sound practices, current and emerging tools and technologies, and effective teamwork approaches.  Most importantly, these topic areas are integrated throughout the curriculum. The concepts and theories learned in the program are applied to the capstone that combines academic and professional development.

Program Objectives

The primary academic objectives for this proposed new degree program will be the development of high-level skills in:

Defining and identifying problems that can be answered by…

How the Objective Conform to Stratford’s Mission

The proposed Master of Science in Data Analytics is consistent with the following themes: Learning, Engagement, Global Perspectives, and Sustainability. The proposed MSDS utilizes the broad objectives/outcomes of Stratford University’s Learning Outcomes and is aligned with the Graduate Education Mission Statement, which includes a focus on graduate programs which promotes: communication and self-discovery.

Programmatic Courses

Each course is 4.5 credits, with 54 total credits required for program core completion.
(This program is eligible for CPT for International Students)

This course focuses on the methodology to conduct cyber security risk assessments, analysis, and response. This includes identifying, classifying, and analyzing cyber threats and vulnerabilities in cyber and physical systems. Students conduct analysis in virtual labs, and create risk mitigation or response plans. Prerequisite: None.

This course focuses on analytics and incidence response. It investigates tools and techniques to handle increasing cyber attacks. The study of big security analytics can provide insights attacks and threats. Students are introduced to thinking with an analytical mindset, one that is curious, explores the data, finds patterns, and follow the trail left by the attacker. Prerequisite: None.

This course addresses the principles underlying data science and architecture, how data evolves with organizations, and the challenges organizations face in structuring and managing their data. It also discusses proven methods and technologies to solve the complex issues dealing with data. The data modelling and data model management, data quality , data governance, enterprise information management, database design, data warehousing, and warehouse design are also discussed. Prerequisite: None.

This course addresses the advanced concepts, techniques, and applications of data warehousing and data mining. Topics covered in this course include dimensional modeling, extraction-transformation-loading (ETL), online analytical processing (OLAP), data mining, decision tree, association mining, and clustering. Through relevant group projects and hands-on activities students learn the practical applications of data warehousing, data mining, and the job of a big data specialist. Prerequisite: None.

This course addresses the big data analytics application of advanced analytic techniques to very big data sets. Big data is explained as an enterprise asset and organizational and analytics tools; techniques, platforms are explored. Through relevant group projects and hands-on activities students learn the big data diverse sources, platforms, and data types. Prerequisite: None.

This capstone course gives students the opportunity to pull together and build upon what has been learned in separate IT fields and utilizes this knowledge in the analysis of complex Data analytic problems. This capstone course is de-signed to aid students in synthesizing and applying knowledge gained in earlier courses and applies these skills through actual Data Analytics cases. The capstone course provides an opportunity to showcase a final projects based on experiences in the field of IT. During the process of designing an e-portfolio, students enhance their resume, interviewing skills, and highlight experiences.  The course should be taken in a student’s final quarter. Prerequisite: Approval of the advisor.

This course discusses political, legal, economic, and ethical forces acting on business as well as the interaction of the market system and public policy process in the development of law and regulation. Prerequisite: None.

This course explores the principles and methodologies of database design, architecture, and techniques for database application. Topics covered include relational design, SQL, transaction processing, decision support, integrity, and security. Prerequisite: None.

This course explores introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The course provides a theoretical view of the fundamentals surrounding machine learning and the mathematical derivations that converts everyone these principles into practical algorithms. Prerequisite: None.

This course introduces the definitions, implementations, and applications of the most commonly used data structures used in computer science, including the concept of abstract data types. The course also introduces the basic formalism and concepts used in the analysis of algorithms and in algorithm design. The relative efficiency of the algorithms studied is estimated by the informal application of these ideas. The algorithms and data structures discussed include those for sorting, searching, graph problems, dynamic programming, and combinatorial search. Prerequisite: None.       

This course covers the essential elements of the Python programming language, including Class Libraries, packages, and exception handling. Students will be able to write python programs for both Web and stand-alone applications using primitive types, tokens, operators, and expressions. Students will also use strings, arrays, graphics, and animation tools in their programs. Emphasis in the course will be placed on the development, implementation and execution of projects with an eye to industry standards. Prerequisite: None.

This course presents material essential to developing a new competency in qualitative literacy. The course focuses on students collecting and interpreting data, descriptive and inferential statistics, and probability. Prerequisite: College Math or higher.