The master’s degree in data analytics will consist of 32 credits designed to equip graduates with the skills necessary to excel in the field.
Introduces the key concepts of data analytics and data science as applied to solving data-centered business problems in many industries. Emphasizes principles and methods covering the process from envisioning the problem to applying data science techniques to deploying the results to improve a business and help make decisions. Topics include an introduction to data-analytic thinking; application of data science solutions to business problems; very high-level data mining techniques, an intro to the SAS software suite, and achieving and sustaining competitive advantage with data analytics. Students will read and analyze data analytics case studies in various industries.
An intermediate statistics course which builds off of the prerequisite statistics course all students in the program must have. Introduces key statistical methods for applying data analytics. Introduces statistical thinking – starting with an interesting question and using data and software tools to form a reasonable conclusion. Covers statistical analysis of both categorical and quantitative data. Most analysis will be performed in SAS Studio which will further integrate SAS into the curriculum.
A hands-on data analytics course for structured data using SAS Enterprise Miner. Covers the skills that are required to assemble analysis flow diagrams using the rich tool set of SAS Enterprise Miner for both pattern discovery (segmentation, association, and sequence analyses) and predictive modeling (decision tree, regression, and neural network models). Course includes defining a SAS Enterprise Miner project and explore data graphically, modifying data for better analysis results, building and understanding predictive models such as decision trees and regression models, comparing and explaining complex models, generating and using score code, applying association and sequence discovery to transaction data. Upon completion, students will have a set of practical data analytics skills and know how to apply these skills in a variety of business environment and with many types of structured data.
Practical survey course covering database and data warehouse fundamentals. Emphasizes SQL (simple and complex queries), the Extract-Transformation-Load (ETL) process, relational versus non-relational databases (and why relational databases can be a problem for analysis), an exploration of different database systems (Oracle, Microsoft SQL Server, etc.), data warehousing concepts, normalization/de-normalization, and cloud data warehousing. Course provides practical skills for database querying and allows provides a foundational knowledge of database concepts so that students can work better with the database administration staff.
A hands-on course emphasizing the importance of data visualization in understanding data. Designed for those who have never used data visualization software before, this course will utilize either Tableau, Microsoft Power BI, or SAS Visual Analytics to prepare student to create reports and dashboards at all levels of an organization. Students will learn exploratory and explanatory data analysis and learn how to ask the right questions about what is needed in a visualization. Students will assess how data and design work together and learn which visualization to use in various situations. Students will learn how to balance the goals of their stakeholders with the needs of their end-users and be able to structure and organize a digital story for maximum impact.
Covers the impact of big data on business and what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. No previous programming experience is required and all code will be provided to students. Software basics in Hadoop with MapReduce, Spark, Pig and Hive. By following along with provided code, students will experience how one can perform predictive modeling and leverage graph analytics to model problems. By the end of the course students will be able to perform basic big data analysis on a large provided data set.
A survey and case study course emphasizing the importance of data privacy, and security. We need to share data in organizations, but the more we share it, the more it becomes necessary to protect it. By the end of the course, students will understand the legal, social, and ethical ramifications of data security and privacy as well as the concepts behind data guardianship and custodianship and data permissions. Special attention will be given to industry-specific data privacy laws (HIPAA, FERPA, PCI DSS, etc.).
A special topics course which will explore current major trends in the data analytics landscape. Topics may include natural language processing, fraud prevention, social media analysis, the role of analytics in financial management, and artificial intelligence. Special guest instructors may be invited to teach this course. If no special topics can be highlighted, this course will focus on unstructured data analysis in SAS.
A final course in the program which will require students to prepare and present a comprehensive data analysis project in collaboration with their own organization or a sponsor organization. Students will be required to choose a faculty advisor and an external advisor in their organization. The final deliverable of this program will be a major paper and presentation.
*Stonehill College reserves the right to change or adjust course numbering and/or course descriptions.