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Online Master of Science in Data Science - Curriculum

Online Master of Science in Data Science - CurriculumThe 36 credit hour, online Master of Science in Data Science program can be completed within two years, and students also have the option of choosing between two unique concentrations: Data Science for Computer Scientists and Data Science for Life Scientists.

Core Courses:

13-510 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.

YOUR OPPORTUNITY: You'll be prepared to comprehend and develop tools and techniques for understanding an organization's data.

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13-511 Concepts of Statistics I (3)

Distribution of random variables, conditional probability and independence, distributions of functions of random variables, limiting distributions.

YOUR OPPORTUNITY: As the statistical properties of data sets help identify trends and clarify conclusions, you'll understand where such statistical measures come from.

Prerequisite: 13-510

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13-512 Concepts of Statistics II (3)

Point estimation, sufficient statistics, completeness, exponential family, maximum likelihood estimators, statistical hypotheses, beta tests, likelihood ratio tests, noncentral distributions.

YOUR OPPORTUNITY: You'll become even more knowledgeable in the language and tools of statistical analysis.

Prerequisite: 13-511

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70-510 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.

YOUR OPPORTUNITY: You'll breathe life into the mathematics you've learned by pairing it with computing techniques that process and analyze large data sets.

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70-511 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.

YOUR OPPORTUNITY: You'll build your programming skills as you study and build applications that try to make sense of large collections of data.

Prerequisite: 13-511

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70-525 Encryption and Authentication Systems (3)

(Double-numbered with 68-525) This course will present key cryptologic terms, concepts, and principles. Traditional cryptographic and cryptanalytic techniques are covered plus perspective on successes and failures in cryptologic history, including both single-key algorithms and double-key algorithms. Issues in network communications, network security, and security throughout the different layers of the OSI model for data communications will also be discussed in-depth, as well as the use of cryptologic protocols to provide a variety of security services in a networked environment. Authentication, access control, non-repudiation, data integrity, and confidentiality issues will also be covered, plus key generation, control, distribution and certification issues.

YOUR OPPORTUNITY: With big data comes big responsibility. Data must be kept secure and private. This course will teach you how data is made confidential.

Prerequisite: 70-510

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70-530 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.

YOUR OPPORTUNITY: Making sense of complicated data sets requires intuitive displays. You'll learn the theory and practice of data visualization in this course.

Prerequisite: 70-511

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70-540 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.

YOUR OPPORTUNITY: You'll learn how to store large amounts of data in a way that is easy and efficient to query and organize.

Prerequisite: 70-511

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Concentration in Data Mining and Analytics for Computer Scientists (12 hours)

70-590 Data Mining and Analytics Project for Computer Scientists (3)
and choose three (3) of the following courses:
70-517 Pervasive Application Development (3)
70-550 Machine Learning (3)
70-552 Semantic Web (3)
70-555 Distributed Computing Systems (3)

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Concentration in Data Mining and Analytics for Life Scientists (12 hours)

02-509 Introduction to Computational Biology (3)
02-510 Data Systems in the Life Sciences (3)
02-512 Research in Biotechnology (3)
02-590 Data Mining and Analytics Thesis for Life Scientists (3)

Take the Next Step

Discover more about Lewis University's online Master of Science in Data Science. Call (855) 255-1082 to speak with a graduate admissions counselor or click here to request more information.

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