2024-2025 Graduate Catalog 
    
    Nov 03, 2024  
2024-2025 Graduate Catalog

Computer Science M.S.


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(33 credits)

The MS in Computer Science at Indiana State University prepares students for a variety of cutting-edge applications in the following areas: web development, database management, bioinformatics, data science, networking, cyber-security, systems management, general software engineering, and machine learning.

Electives (13-24 credits)


Complete electives of computer science coursework at the 500/600 level for a total of at least 33 total credits of graduate coursework.  The total number of credits of 600 level coursework should be at least 17.

Students in the Bioinformatics Concentration may take coursework in Biology related to bioinformatics.

All electives should be approved by the advisor.

Culminating Experience (1-10 Credits)


The culminating experience normally involves a significant project or experience that is supervised by the faculty and is directly related to the student’s concentration and interests.

Students must choose one course based on their concentration:

Bioinformatics Concentration:

BIO 692 - Research in Biology 1-10 Credits

Or

BIO 699 - Master’s Thesis 6 Credits

Data Science and Professional Concentration:

CS 685 - Software Project 3-6 Credits

Academic Concentration:

CS 695 - Computer Science Research 3-6 Credits

Or

CS 699 - Computer Science Internship 0-3 Credits

Choose a concentration


Choose a concentration - Academic, Bioinformatics, Data Science, or Professional.

Academic Concentration (9-15 credits)


The Academic Concentration ensures a strong foundation in computer systems and algorithms.  This foundation can be excellent preparation for either applying for competetive industry software engineering positions, or applying for PhD programs.  The skills and knowledge can also be applied across other areas of computer science.

Students in this concentration are required to take CS 695 for a minimum of 3 credits.

Systems (3-6 credits)


Complete CS 671 Operating Systems II.  If CS 456 not taken at ISU, complete CS 556.

Algorithms (3-6 credits)


Complete CS 658 Algorithms II.  If CS 458 not taken at ISU, complete CS 558.

Bioinformatics Concentration (20 credits)


The Bioinformatics concentration ensures knowledge and skills in bioinformatics algorithms, the most important software packages and pipelines, how biological data is acquired, and important bioinformatics questions.  The concentration prepares students to make immediate contributions in this field at the boundary between biology, medicine, and computer science.

CS Foundations (6 credits)


Take CS 600 and either CS 500 or CS 501.

Computer Science (6 credits)


Complete CS 618 and a CS database course (CS 557 or CS 617).

Biology and Bioinformatics (8 credits)


Complete the following courses in Biology that are related to Bioinformatics.

Data Science Concentration (18 credits)


The Data Science Concentration gives a strong foundation in the mathematics, algorithms, and software packages used to manage various types of data, analzye data, and communicate results.  This includes exploratory data analysis, predictive modeling, descriptive modeling, and model evaluation.  The concentration prepares students for positions in data science and related areas.

Professional Concentration (18 credits)


The Professional Concentration gives a foundation in the core of computer science and ensures students gain skills and knowledge in important application areas.  The concentration is recommended for those coming to computer science from other disciplines and those looking for a wide exposure to in-demand skills and areas.  Students are prepared to take positions in a variety of important application areas.

Interconnected Computing core (3 credits)


Complete at least one interconnected computing course numbered 601-609.

Data Management and Analysis core (3 credits)


Complete at least one course in data management and analysis from CS courses numbered 610-618.

Core Elective (3 credits)


Complete one additional course in interconnected computing or data management and analysis.

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