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M.S. in Data Science - Computational Biology and Bioinformatics - Courses

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

Courses in the online M.S. in Data Science in Computational Biology and Bioinformatics concentration* are designed to heighten your understanding of data analysis as it relates to the fields of health and biology. The concentration requires 12 credit hours and is made up of four courses.

Concentration Course Descriptions

BIOL-50900 Introduction to Computational Biology (3 credits)

This course will cover the computational techniques used to access, analyze, and interpret the biological information in common types of biological databases and the biological questions that can be addressed by such methods, applicable to the study of the context of genes within the same genome and across different genomes, the study of molecular sequence data for the purpose of inferring the function, interactions, evolution and structure of biological molecules, and the study of annotation and ontology.

Learning Objectives

  1. Identify the most common data sources used by computational biologists.
  2. Describe the kind of problems for which common data sources are most commonly used.
  3. Use online biological data sources to solve problems in the life sciences.
  4. Create visualizations using biological data analysis tools that help clarify issues involved in problems.
  5. Describe how computational biology has helped solve a range of problems in the healthcare industry.
  6. Project how computational biology can be used to solve current problems in the healthcare industry.

YOUR OPPORTUNITY: You'll discover opportunities for applying data mining and analysis techniques to the huge volumes of data that describe biological and healthcare delivery systems.

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BIOL-51000 Data Systems in the Life Sciences (3 credits)

This is a continuation of BIOL-50900. Students will examine how bioinformatics, statistics and computation are being used to support the discovery of new biomedical knowledge and learn the basics of computational methods used to analyze molecular sequences and structures.

Learning Objectives

  1. Write mathematical equations that describe biological systems.
  2. Write queries against biological data systems that extract the data needed to solve a problem.
  3. Use Matlab to model, visualize, and solve systems in the life sciences.
  4. Design laboratory experiments based on the knowledge gained from computer simulation.
  5. Based on results from laboratory experiments, refine queries and simulations to increase the accuracy of computer simulations of biological systems.

YOUR OPPORTUNITY: You'll learn how healthcare systems store and analyze data using existing information systems.

Prerequisite: BIOL-50900

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BIOL-51200 Research in Biotechnology (3 credits)

Methods and sources for conducting research in biotechnology. A series of guest presentations will expose students to current trends in and applications of biotechnology. Students will conduct their own research based on these presentations. Use of primary sources, data collection techniques, and ethical conduct of research will be emphasized.

Learning Objectives

  1. Identify the primary journals and publications in the field of computational and data-drive life science.
  2. Construct survey instruments that yield interpretable results.
  3. Discriminate among ethical and unethical choices in conducting scholarly research.
  4. Propose extensions to existing research that integrate findings from multiple sources.

YOUR OPPORTUNITY: You'll discover the cutting edge of research in data science in the life sciences and prepare to conduct your own research for your thesis.

Prerequisite: BIOL-51000

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BIOL-59000 Data Mining and Analytics Thesis for Life Scientists (3 credits)

The student will pursue a research project that makes a scholarly contribution to existing knowledge and practice in the field of data analytics as it is applied to the Life Sciences. The student will write a formal thesis that documents the conduct, results, and conclusions of his or her project. Upon successful completion of the thesis, the student will submit the paper for review by a thesis committee consisting of faculty in the Biology and Computer Science departments, along with possibly additional experts from industry. The student will make an oral defense of the work to the thesis committee. Prerequisite: 02-510 and a minimum of 24 hours earned in the MS-DMA program.

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 a given hypothesis.

YOUR OPPORTUNITY: You'll establish yourself as an expert in data mining as it applies to the life sciences through your own research conducted under the guidance of Lewis professor.

Prerequisite: BIOL-51200

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Take the Next Step

Learn more about the curriculum of the online M.S. in Data Science in Computational Biology and Bioinformatics  concentration program. Request more information or call us today at (866) 967-7046.