Program Description
The 36-credit interdisciplinary Data Science Master’s Degree provides a strong background in data science methodologies. The program has strong emphasis on the areas of programming, data management, data mining, machine learning and statistics. The degree culminates in a course in project management, a real world internship experience and a two-semester capstone Data Science Project.
The data science technologies have completely changed the way businesses do strategic planning to manage their operations more efficiently and to serve their customers more effectively. These developments have created tremendous opportunities for innovation by tapping the enormous potential for the benefit of society. Graduates will be prepared to participate in these innovative opportunities.
The program endeavors to maximize interaction between faculty and students.
Program Requirements
Data Science concerns itself with volume, variety and velocity of data being generated. It is about understanding data, processing, and discovering value in data, visualizing it and communicating results. This is applicable to many areas such as marketing, business intelligence or health care management, and a professional skills component
There are 36 credits total in the degree, which can be completed in four semesters by a student taking nine credits per semester. Classes are scheduled in the evening making it easier for working professionals. A student can also complete the degree at a slower pace if so desired.
All students will take a set of core courses in three areas: Statistics, Computer Science and Data Science. Students will also complete plus courses (as defined by the SUNY Professional Science Master’s initiative), a project report and an internship. There are six credits of statistics, which will include modeling and use of software packages. The Computer Science component will be nine credits in algorithms and programming, database, and data warehousing. The six credits of Data Science will cover the standard and up-to-date techniques of Machine Learning and Data Mining with examples using real world data. The plus courses will teach students necessary skills for the workplace, such as Project Management and Communications and Presentation. For the applied portion of the degree, students will work in teams using all of their previously gained knowledge to solve a real world problem demonstrating the data science cycle. Additionally, students will be required to complete an internship.
Program Learning Outcomes
The program’s primary student learning outcomes are:
- Students will demonstrate knowledge of efficient algorithmic processes and “good” programming skills in several languages used in the implementation of efficient solutions to big data problems.
- Students will demonstrate the ability to clean data, integrate data from distinct sources and store the data in the appropriate databases for analyses.
- Students will be able to do basic and advanced statistical analysis on big data sets. This knowledge will help the students to develop solutions to complex big data problems.
- Students will demonstrate strong written and oral communication skills.
- Students will demonstrate the ability to understand the big picture of a complex data science problem and analyze the data to answer undirected and directed questions.
- Students will demonstrate the ability to function as a team member and to lead effectively in challenging management positions in local and global environments.
- Students will demonstrate their ability to apply the skills and the knowledge gained in the classroom to real world data science problems.