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M.S. in Data Science - Computer Science Concentration - Courses

Combine Data Mining Skills and Biological Understanding to Solve Pressing Problems in the Life Sciences

Courses in the M.S. in Data Science Concentration in Computer Science are designed to give students the technical savvy to design front-end systems and the mathematical skills to write algorithms that decipher large quantities of data. The concentration requires four courses: 70-590 and three elective courses.

Choose three (3) of the following courses:

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.

Learning Objectives

  1. Distinguish among various random distributions and identify which distributions most closely characterize certain natural phenomena.
  2. Compute conditional probabilities.
  3. Compute and plot distributions of random variables.
  4. Solve problems whose data are characterized by specific distribution functions.
  5. Estimate values and compute confidence intervals.

Formulate and test statistical hypotheses.

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

Prerequisite: 13-511

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70-517 Pervasive Application Development (3)

Development of web- and mobile-based front ends for large-scale data systems, with a focus of portability, accessibility, and intuitiveness.

Learning Objectives

  1. Develop software applications that run on all major computing platforms, including mobile ones.
  2. Interact with application programming interfaces provided by data services to make that data accessible to their own applications.
  3. Design user interfaces that present data and functionality clearly.
  4. Design user interfaces that comply with legislation regarding accessibility.
  5. Test the cross-platform performance of applications they write.

YOUR OPPORTUNITY: Ubiquitous data requires ubiquitous computing. This course teaches you how to create applications that run on mobile and non-traditional platforms.

Prerequisite: 70-511

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70-552 Semantic Web (3)

Expressing relationships among items in a way that enables automated, distributed analysis in an application-independent way; text mining to derive meaning from semantic networks; algorithms for processing semantic networks; developing a web of things.

Learning Objectives

  1. Explain the nature and purpose of the semantic web and identify applications that can benefit. 
  2. Construct a semantic web that meets the needs of a particular discipline or problem.
  3. Write software that processes a semantic web’s information to solve problems more efficiently.
  4. Write software that mind textual data for patterns using the services of a semantic web.
  5. Design an implementation for the “web of things” that can automatically perform specific tasks.

YOUR OPPORTUNITY: You will learn how to integrate data from sensor networks and other non-traditional systems to create pervasive data science solutions.

Prerequisite: 70-511

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70-555 Distributed Computing Systems (3)

Architecture and programming of parallel processing systems; distributed data storage techniques; multithreading and multitasking; redundancy; load balancing and management; distributed system event logging; programming techniques for maximizing the importance of distributed systems.

Learning Objectives

  1. Explain various ways to organize processors for distributed problem solving.
  2. Write programs that allocate resources efficiently to solve various parallelizable problems.
  3. Design load balancing and backup systems that support the work of distributed systems.
  4. Use system monitoring tools to maintain and troubleshoot distributed systems.

YOUR OPPORTUNITY: You will learn how to leverage parallel and distributed computing architectures to process data on a massive scale.

Prerequisite: 70-511

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And conclude with the following capstone experience:

70-590 Data Mining and Analytics Project for Computer Scientists (3)

The capstone experience for students pursuing the Computer Science concentration in Data Science. Students will develop a solution to a real-world problem in data science, document their work in a scholarly paper, and present their methodology and results to faculty and peers.

Learning Objectives

  1. Incorporate scholarly content from a variety of sources into a scholarly work of their own.
  2. Design experiments that yield data appropriate to answering a question or solving a problem.
  3. Analyze the data from experiments to draw conclusions that support or reject hypotheses.

YOUR OPPORTUNITY: You will demonstrate your skills as a data scientist by implementing a project that helps an organization make sense of the data it has collected.

Prerequisite: A minimum of 24 hours earned in the MS-DS program.

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Learn more about the M.S. in Data Science core courses.

Take the Next Step

Learn more about the online M.S. in Data Science degree program at Lewis University. Request more information or call us today at (866) 967-7046.