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Statistical methods (3 credit hours)

• stat-s 520 introduction to statistics
  • Higher level statistics course can be included with departmental approval

Data degradation and search (3 credit hours)

Select a course from the following:

• CSCI-B 551 Elements of Artificial Intelligence
• CSCI-B 555 Machine Learn
• CSCI-B 565 Data Mining
• CSCI-P 556 applied machine learning
• Engr-E 511 Machine Learning for Signal Processing
• ILS-Z 534 Search
• Info-I 606 Network Science

Data Management and Engineering (3 Credit Sounds)

Select a course from the following:

• CSCI-B 561 Advanced Database Concepts
• Engr-E 516 Engineering Cloud Computing
• info-i 535 management, access and use large and complex data

data visualization and stories (3 credit hours)

Select a course from the following:

• gra-e 583
• Engr-E 584 Scientific Visualization
• info-i 590 topics in computer science
  • Topic: Data Visualization
• STAT-S 670 Exploratory Data Analysis

Domain Data Science (6 Credit Hours)

Select two courses from the same category:

data security and privacy

• Info I 520 Security for Networked Systems
• Info-I 525 Organizational Computer Science and Business Economics
• info-i 533 systems and protocol security and information security
• Info-I 536 Basic Mathematics of Cybersecurity
• Info-I 538 Introduction to Cryptography

health and biomedical data science

• CSCI-B 609 Themes in Algorithms and Calculation Theory
  • Topic: Bioinformatics for Precision Medicine
• info-i 507 introduction to health informatics
• info-i 519 introduction to bioinformatics
• info-i 529 machine learning in bioinformatics
• info-i 590 topics in computer science
  • Topic: Data Science for Drug Detection, Health and Translation Medicine
  • Topic: SNP Discovery and Population Genetics

human robotic interaction

• CSCI-B 657 Computer Vision
• ENGR-E 523 Internet of Things
• ENGR-E 599 topics in Intelligent Systems Engineering
  • Topic: Autonomous Robotics
• info-i 527 mobile and pervasive design
• Info-I 540 Human Robot Interaction
• Info-I 542 Foundations of HCI

social data science

• egr-e 583 information visualization (can only be counted once)
• ILS-Z 639 Social Media Mining
• info-i 590 topics in computer science
  • Topic: Data and Society
  • Topic: Data visualization (can only be counted once)
• Info-I 606 Network Science (can only be counted once)

Project (3 credit hours)

Select a course from the following:

• DSCI-D 590 topics in data science
  • Topic: Data Science in Practice
• DSCI-D 699 independent study in data science

elective compartments (9 credit hours)

The remaining 9 credit hours are selected from the courses mentioned above or other data scientific courses of his or her choice at school of computer science, computing and engineering.

computing and analytical track

Students with a strong computer scientific background that wants to drive deeper into the mechanics of data science methodology can track a stricter curriculum. Students who follow the calculating and analytical route will include 15 credit hours of core course work - consisting of data systems, algorithmic foundations, data analyzes and large data infrastructures as well as 15 credit hours of the election hours.

Data Systems Foundation (3 Credit Sounds)

• CSCI-B 561 Advanced Database Concepts

Algorithmic foundation (3 credit hours)

Select a course from the following:

• CSCI-B 503 Algorithms Design and Analysis
• CSCI-B 505 Applied Algorithms
• CSCI-B 609 Themes in Algorithms and Computer Theory
  • Topic: Foundations in Data Science

Data Analytics Foundation (6 credit hours)

• stat-s 520 introduction to statistics
  • Higher level statistics course can be included with departmental approval

Select an additional course from the following:

• CSCI-B 555 Machine Learn
• CSCI-B 565 Data Mining

large data infrastructure (3 credit hours)

Select a course from the following:

• Engr-E 516 Engineering Cloud Computing
• info-i 535 management, access and use large and complex data

The remaining 15 Credit hours are selected from non-selected courses over or additional data scientific offers of his or her choice at the school of computer science, computer and engineering. A course in the data ethics or a large project is very promoted, but not required.

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