Master of Science, Non-Thesis Option, 30 credit hours

• Data Modeling and Statistical Learning
  • MATH 530: Statistical Methods
  • MATH 560: Statistical Learning I
  • MATH 561: Statistical Learning II
• Machine Learning, Data Processing and Algorithms, and Parallel Computation
  • CSCI 303: Introduction to Data Science
  • CSCI 470: Introduction to Machine Learning
  • CSCI 575: Machine Learning or CSCI 563: Parallel Computing for Scientists and Engineers
• Individualized and Domain-Specific Coursework*
  • Electrical Engineering Example Courses
    • EENG 411: Digital Signal Processing
    • EENG 509: Sparse Signal Processing
    • EENG 511: Convex Optimization and its Engineering Applications
    • EENG 515: Mathematical Methods for Signals and Systems
    • EENG 519: Estimation Theory and Kalman Filtering
  • Geophysics Example Courses
    • GPGN 533: Geophysical Data Integration & Geostatistics
    • GPGN 570: Applications of Satellite Remote Sensing
    • GPGN 605: Inversion Theory
• Professional Development
  • SYGN 502: Introduction to Research Ethics
  • SYGN 5XX: Leadership and Teamwork
  • LICM 501: Professional Oral Communication

*Electives for the third module can be designed by the student, but the plan needs to be approved by the program curriculum committee.

Post-Baccalaureate Certificate in Data Science – Foundations, 12 credit hours

• DSCI 403 / CSCI 303: Introduction to Data Science
• DSCI / CSCI 470: Introduction to Machine Learning
• DSCI / MATH 530: Statistical Methods I
• DSCI / MATH 560: Introduction to Key Statistical Learning Methods I

Post-Baccalaureate Certificate in Data Science – Computer Science, 12 credit hours

• DSCI 403 / CSCI 303: Introduction to Data Science
• DSCI / MATH 530: Statistical Methods I
• DSCI / CSCI 470: Introduction to Machine Learning
• DSCI / CSCI 575: Machine Learning

Graduate Certificate in Data Science – Statistical Learning, 12 credit hours

• DSCI 403 / CSCI 303: Introduction to Data Science
• DSCI / MATH 530: Statistical Methods I
• DSCI / MATH 560: Introduction to Key Statistical Learning Methods I
• DSCI / MATH 561: Introduction to Key Statistical Learning Methods II

Graduate Certificate in Data Science – Earth Resources, 12 credit hours

• DSCI 403 / CSCI 303: Introduction to Data Science
• GEOL 557: Earth Resource Data Science 1: Fundamentals
• GEOL 558: Earth Resources Data Science 2: Applications and Machine Learning
• One elective from the following list:
  • Geospatial Focus
    • GEGN 575: Applications of Geographic Information Systems
    • GEGN 579: Python Scripting for Geographic Information Systems
  • Petroleum Focus
    • GPGN 519: Advanced Formation Evaluation
    • GPGN 547: Physics, Mechanics, and Petrophysics of Rocks
    • GPGN 558: Seismic Data Interpretation and Quantitative Analysis
    • GPGN 651: Advanced Seismology
    • PEGN 522: Advanced Well Stimulation
    • PEGN 551: Petroleum Data Analytics – Fundamentals
  • Mining Focus
    • MNGN 548: Integrated Information and Mine Systems Management
  • Hydrology Focus
    • CEEN 581: Watershed Systems Modeling
  • Additional Options
    • DSCI / MATH 530: Statistical Methods I
    • EBGN 525: Business Analytics

Graduate Certificate in Petroleum Data Analytics, 12 credit hours

• DSCI / MATH 530: Statistical Methods I
• DSCI 403 / CSCI 303: Introduction to Data Science
• PEGN 551: Petroleum Data Analytics – Fundamentals
• PEGN 552: Petroleum Data Analytics – Applications

» VIEW CATALOG

DSCI403. INTRODUCTION TO DATA SCIENCE. 3.0 Semester Hrs.

This course will teach students the core skills needed for gathering, cleaning, organizing, analyzing, interpreting, and visualizing data. Students will learn basic SQL for working with databases, basic Python programming for data manipulation, and the use and application of statistical and machine learning toolkits for data analysis. The course will be primarily focused on applications, with an emphasis on working with real (non-synthetic) datasets. Prerequisite: CSCI101 or CSCI261.

DSCI470. INTRODUCTION TO MACHINE LEARNING. 3.0 Semester Hrs.

The goal of machine learning is to build computer systems that improve automatically with experience, which has been successfully applied to a variety of application areas, including, for example, gene discovery, financial forecasting, and credit card fraud detection. This introductory course will study both the theoretical properties of machine learning algorithms and their practical applications. Students will have an opportunity to experiment with machine learning techniques and apply them to a selected problem in the context of term projects. Prerequisite: MATH201, MATH332.

DSCI530. STATISTICAL METHODS I. 3.0 Semester Hrs.

Introduction to probability, random variables, and discrete and continuous probability models. Elementary simulation. Data summarization and analysis. Confidence intervals and hypothesis testing for means and variances. Chi square tests. Distribution-free techniques and regression analysis. Prerequisite: MATH213 or equivalent.

DSCI560. INTRODUCTION TO KEY STATISTICAL LEARNING METHODS I. 3.0 Semester Hrs.

Part one of a two-course series introducing statistical learning methods with a focus on conceptual understanding and practical applications. Methods covered will include Introduction to Statistical Learning, Linear Regression, Classification, Resampling Methods, Basis Expansions, Regularization, Model Assessment and Selection.

DSCI561. INTRODUCTION TO KEY STATISTICAL LEARNING METHODS II. 3.0 Semester Hrs.

Part two of a two-course series introducing statistical learning methods with a focus on conceptual understanding and practical applications. Methods covered will include Non-linear Models, Tree-based Methods, Support Vector Machines, Neural Networks, Unsupervised Learning.

DSCI575. MACHINE LEARNING. 3.0 Semester Hrs.

The goal of machine learning research is to build computer systems that learn from experience and that adapt to their environments. Machine learning systems do not have to be programmed by humans to solve a problem; instead, they essentially program themselves based on examples of how they should behave, or based on trial and error experience trying to solve the problem. This course will focus on the methods that have proven valuable and successful in practical applications. The course will also contrast the various methods, with the aim of explaining the situations in which each is most appropriate. Prerequisite: CSCI262, MATH201, MATH332.

*Allowance for fees based on mandatory fees charged to all students. Does not include fees for orientation, library, yearbook, refrigerator rental, voice messaging, ect.

At less than 4.5 credit hours, you may be ineligible for financial aid.