Computational Analytics Specialization

The focus of the specialization in Computational (Data) Analytics is to allow students to further explore and specialize in the areas of large-scale data analytics and architectures from theory to practice with more of a computational focus. This ranges from Machine Learning, Text and Linguistic Analytics, Graph Analytics, Visual Analytics to Map Reduce, noSQL Databases, Analytics in Cybersecurity, High Performance Computing and Cloud Computing.

Specialization Educational Objectives

In addition to the core objectives, a student graduating with a Bachelor of Science degree with a major in Data Analytics with a specialization in Computational Analytics will demonstrate:

  • an understanding of the principles governing an advanced computational focus area (Cyber-security, Machine Intelligence, Systems/Theory, Linguistics and Text analytics).
  • an advanced understanding of and the ability to use analytic techniques in one or more focus areas.

Degree Planning Resources

Take a look at the suggested sample 4-year plan [pdf] and the advising curriculum sheet [pdf] for Computational Analytics. These should be used as guides only. Semester offerings are subject to change.

Specialization Requirements

Students in the Computational Analytics specialization must take 10 credit hours of coursework from the electives listed below and also complete a 4 credit hour capstone course. Courses are grouped to show possible focus areas but students may select any combination of courses (having met appropriate pre-requisites) to meet the 14 credit hours requirement.

  • Ling 2000*: Introduction to Language in the Humanities (3 cr hrs)

*As noted on the GE advising page, students choosing the Linguistics and Text Analytics focus should take Linguistics 2000 - Introduction to Language in the Humanities. This course is a pre-req for the Ling 4x00 courses and may also be used to satisfy the GE requirement in the Culture and Ideas category.

Electives: Cyber-security Focus

  • CSE 3461: Computer Networking and Internet Technologies (3 cr hrs)
  • CSE 4471: Information Security (3 cr hrs)
  • Choose one of the following:
    • CSE 5472: Information Security Projects (3 cr hrs)
    • CSE 5473: Network Security (3 cr hrs)
  • CSE 591x/Stat 4194**: Capstone in Data Analytics (4 cr hrs)

Electives: Machine Intelligence Focus

  • CSE 2331: Foundations II: Data Structures and Algorithms (3 cr hrs)
  • CSE 3521: Survey of Artificial Intelligence I: Basic Techniques (3 cr hrs)
  • Choose two of the following courses:
    • CSE 5245: Introduction to Network Science (3 cr hrs)
    • CSE 5523: Machine Learning and Statistical Pattern Recognition (3 cr hrs)
    • CSE 5524: Computer Vision for Human-Computer Interaction (3 cr hrs)
    • CSE 5526: Introduction to Neural Networks (3 cr hrs)
  • CSE 591x/Stat 4194**: Capstone in Data Analytics (4 cr hrs)

Electives: Core (Systems or Theory) Focus

  • Choose one of the following:
    • CSE 2331: Foundations II: Data Structures and Algorithms (3 cr hrs)
    • CSE 2431: Systems II: Introduction to Operating Systems (3 cr hrs)
  • CSE 3901, 3902, 3903: CSE Junior Project Choice (4 cr hrs)
  • Choose one of the following courses:
    • CSE 5245: Introduction to Network Science (3 cr hrs)
    • CSE 5361: Numerical Methods (3 cr hrs)
    • CSE 5441: Introduction to Parallel Computing (3 cr hrs)
  • CSE 591x**: CSE Capstone Course (4 cr hrs)

Electives: Linguistics and Text Analytics Focus

  • Ling 5801: Computational Linguistics I (3 cr hrs)
  • Ling 5802: Computational Linguistics II (3 cr hrs)
  • CSE 5525: Foundations of Speech and Language Processing (3 cr hrs)
  • Choose one of the following:
  • CSE 591x/Stat 4194/Ling 5xxx**: Capstone in Data Analytics (4 cr hrs)

***The Department of Computer Science and Engineering offers numerous capstone courses that vary by topic. Data Analytics majors may choose any course numbered 591x. CSE 5914 and 5915 are preferred. The Department of Statistics also offers a capstone course numbered Stat 4194 that can be taken in place of the CSE capstone options.

 

[pdf] - Some links on this page are to .pdf files. If you need these files in a more accessible format, please contact data-analytics@osu.edu.

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