Online Master of Science in Data Science - Curriculum
Become fluent in data mining and analysis with Lewis University’s online Master of Science in Data Science. Highlights of the curriculum include:*
- Program can be completed in two years.
- Students have the option to choose a concentration in Computational Biology and Bioinformatics or Computer Science.
- Program consists of 33 credit hours including:
- 7 core courses (21 credit hours)
- 4 concentration courses (12 credit hours)
- Students without undergraduate coursework in Data Science or a related field can still be accepted, but may need take up to two courses (6 credit hours) to provide the required foundation for them to pursue advanced study.
MATH-51100 Concepts of Statistics 1 (3)
Distribution of random variables, conditional probability and independence, distributions of functions of random variables, limiting distributions.
CPSC-51000 Introduction to Data Mining and Analytics (3)
Overview of the field of data mining and analytics; large-scale file systems and Map-Reduce, measures of similarity, link analysis, frequent item sets, clustering, e-advertising as an application, recommendation systems.
CPSC-51100 Statistical Programming (3)
Programming structures and algorithms for large-scale statistical data processing and visualization. Students will use commonly available data analysis software packages to apply concepts and skills to large data sets and will also develop their own code using an object-oriented programming language.
CPSC-53000 Data Visualization (3)
The theory and practice of visualizing large, complicated data sets to clarify areas of emphasis. Human factors best practices will be presented. Programming with advanced visualization frameworks and practices will be demonstrated and used in group programming projects.
CPSC-54000 Large-Scale Data Storage Systems (3)
The design and operation of large-scale, cloud-based systems for storing data. Topics include operating system virtualization, distributed network storage, distributed computing, cloud models (IAAS, PAAS and SAAS), and techniques for securing cloud and virtual systems.
CPSC-55000 Machine Learning (3)
Algorithms for enabling artificial systems to learn from experience; supervised and unsupervised learning; clustering, reinforcement learning control. Students will write programs that demonstrate machine-learning techniques.