Data Science Certificate - Curriculum
Lewis University’s online Graduate Certificate in Data Science is an 18-credit hour program that can be completed in as little as 12 months.
MATH-51000 Mathematics for Data Scientists (3)
Differentiation and integration of functions; basic matrix operations; linearization; linear and nonlinear optimization techniques; clustering and similarity measures, introduction to probability and statistics, basic computational algorithms. Includes frequent illustration of concepts using mathematical computation tools.
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.