Graduate Programs

Degree Programs

Data Science Graduate Certificate

The Graduate Certificate in Data Science (GCDS) is a fully online graduate program that encompasses a broad range of courses on topics such as cloud computing, visualization, high-performance computing, machine learning,  and data analysis.  This professional certificate allows students the opportunity to tailor their curriculum to suit their interests.

Upon successful completion of the Graduate Certificate in Data Science, students have the option of applying earned credits towards the Online Master of Science in Data Science.  Those who apply by the established deadlines will receive Direct Admission to the MS program and credits earned will transfer automatically.

Curriculum

Students must complete 12 graduate credit hours with a grade of B or higher to earn the certificate; a cumulative GPA of 3.00 or higher must be achieved by program completion, with no outstanding grades (Incompletes). All coursework must be completed within two (2) years of entering the certificate program. No credits may be transferred from another graduate or undergraduate program to satisfy the requirements.  The only exception to this policy is through the Indiana University Graduate School's Continuing Non-Degree (CND) Program. Students may apply no more than three (3) credit hours of approved graduate coursework in non-degree status; the course must be earned with a grade of B or higher.

Courses must be selected from the approved list of graduate courses below, unless otherwise approved by the Director of Data Science Graduate Studies; any four (4) courses may be taken for the certificate.  Students are encouraged to consult with the Luddy Office of Online Education for course recommendations, availability, etc.

  • CSCI-B 505 Applied Algorithms
  • CSCI-B 551 Elements of Artificial Intelligence
  • CSCI-B 561 Advanced Database Concepts
  • CSCI-B 657 Computer Vision
  • DSCI-D 590 topics in Data Science
    • Topic: Advanced Data Science On-Ramp *
    • Topic: Applied Data Science
    • Topic: Basic Data Science On-Ramp *
    • Topic: Data Science for Drug Discovery, Health and Translational Medicine
    • Topic: Data Visualization
    • Topic: Introduction to Business Analytics Modeling
    • Topic: Introduction to NLP for Data Science
    • Topic: Introduction to Python Programming
    • Topic: Real World Data Science
    • Topic: SQL and noSQL
    • Topic: Time Series Analysis 
  • ENGR-E 511 Machine Learning for Signal Processing
  • ENGR-E 516 Engineering Cloud Computing
  • ENGR-E 517 High Performance Computing
  • ENGR-E 533 Deep Learning Systems
  • ENGR-E 534 Big Data Applications
  • ENGR-E 583 Information Visualization
  • ENGR-E 616 Advanced Cloud Computing
  • ILS-Z 534 Search
  • ILS-Z 636 Data Semantics
  • ILS-Z 639 Social Media Mining
  • INFO-I 520 Security for Networked Systems
  • INFO-I 525 Organizational Informatics and Economics of Security
  • INFO-I 526 Applied Machine Learning
  • INFO-I 529 Machine Learning in Bioinformatics
  • INFO-I 533 Systems and Protocol Security and Information Assurance
  • INFO-I 535 Management, Access, and Use of Big and Complex Data
  • INFO-I 606 Network Science
  • SPCN-P 507 Data Analysis and Modeling for Public Affairs
  • SPCN-V 506 Statistical Analysis for Effective Decision-making
  • STAT-S 520 Introduction to Statistics
  • STAT-S 681 Topics in Applied Statistics
    • Topic: Introduction to Regression Models and Nonparametrics
(*) No more than a total of three (3) credit hours may be earned in Basic and Advanced Data Science On-Ramp

 

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