Data Science
Professors
Soutir Bandyopadhyay, Applied Mathematics and Statistics
Dorit Hammerling, Applied Mathematics & Statistics
Teaching Professor
Wendy Fisher, Computer Science
Teaching Assistant Professors
Nathan Lenssen, Applied Mathematics and Statistics
Zibo Wang, Computer Science
Research Associate Professor
Zane Jobe, Geology and Geological Engineering
Professor Emeritus
Douglas Nychka, Applied Mathematics & Statistics
Degrees Offered
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Data Science Master of Science (Non-Thesis)
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Data Science Master of Science (Non-Thesis) Online
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Data Science Graduate Certificate - Statistical Learning
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Data Science Graduate Certificate - Earth Resources
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Data Science Graduate Certificate - Petroleum Data Analytics
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Data Science Graduate Certificate - Business Analytics
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Data Science Graduate Certificate - Foundations
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Data Science Graduate Certificate - Computer Science
Data Science Master of Science (Non-Thesis)
The field of Data Science draws on elements of computer science, statistics and interdisciplinary applications to address the unique needs of gaining knowledge and insight through data analysis. This Masters Non-Thesis program is designed to give candidates a foundation in statistics and computer science and also provide knowledge in a particular application domain of science or engineering. The balance between these three elements is a strength of the program and can prepare candidates for Data Science careers in industry, government, or for further study at the PhD level. Moreover, the coursework will be flexible and tailored to each candidate. For example, the program will allow a candidate to increase his/her skills in data analytics while developing a focused area of application or alternatively allow a candidate with depth in an area of application to gain skills in statistics and computer science.
Program Requirements
Students take 18 credits of core courses, 3 credits of professional development coursework and at least 9 credits of electives for a minimum total of 30 credits.
Core Courses
| Data Modeling and Statistical Learning | ||
| MATH530 | INTRODUCTION TO STATISTICAL METHODS (Online Only) Students who have passed MATH335 should replace MATH530 with any of the following: MATH500, MATH531, MATH532, MATH533, MATH534, MATH535, MATH536, MATH537, MATH538, or MATH551 | 3.0 |
| MATH560 | INTRODUCTION TO KEY STATISTICAL LEARNING METHODS I (Online Only) | 3.0 |
| MATH561 | INTRODUCTION TO KEY STATISTICAL LEARNING METHODS II (Online Only) | 3.0 |
| Machine Learning, Data Processing and Algorithms, and Parallel Computation | ||
| DSCI503 | ADVANCED DATA SCIENCE | 3.0 |
| DSCI570 | INTRODUCTION TO MACHINE LEARNING | 3.0 |
| DSCI575 | ADVANCED MACHINE LEARNING | 3.0 |
Professional Development Requirement
Choose courses from the table below in any combination to meet or exceed 3 credits.
| Professional Development | ||
| SYGN502 | INTRODUCTION TO RESEARCH ETHICS | 1.0 |
| SYGN5XX | LEADERSHIP AND TEAMWORK | 1.0 |
| SYGN683 | ORAL COMMUNICATION SKILLS | 1.0 |
| SYGN684 | WRITING SKILLS | 2.0 |
| LICM501 | PROFESSIONAL ORAL COMMUNICATION | 1.0 |
| EBGN562 | STRATEGIC DECISION MAKING | 3.0 |
| EBGN563 | MANAGEMENT OF TECHNOLOGY AND INNOVATION | 3.0 |
| EBGN566 | TECHNOLOGY ENTREPRENEURSHIP | 3.0 |
| EBGN577 | LEADING & MANAGING HIGH PERFORMING TEAMS | 3.0 |
| PHGN503 | RESPONSIBLE CONDUCT OF RESEARCH | 1.0 |
| SYGN598 | SPECIAL TOPICS (Graduate Internship) | 1-3 |
Electives
Electives are designed by the student to fulfill their interests and career goals. The intent is to give you the knowledge and skills to apply data science techniques in various fields. Although this individualized module can draw on graduate courses from across the university the courses given below are approved options:
| BIOL510 | BIOINFORMATICS | 3.0 |
| CBEN505 | NUMERICAL METHODS IN CHEMICAL ENGINEERING | 3.0 |
| CBEN507 | APPLIED MATHEMATICS IN CHEMICAL ENGINEERING | 3.0 |
| CBEN509 | ADVANCED CHEMICAL ENGINEERING THERMODYNAMICS | 3.0 |
| CBEN511 | NEUROSCIENCE, MEMORY, AND LEARNING | 3.0 |
| CBEN516 | ADVANCED TRANSPORT PHENOMENA | 3.0 |
| CBEN518 | REACTION KINETICS AND CATALYSIS | 3.0 |
| CBEN530 | TRANSPORT PHENOMENA | 3.0 |
| CBEN532 | TRANSPORT PHENOMENA IN BIOLOGICAL SYSTEMS | 3.0 |
| CBEN624 | APPLIED STATISTICAL MECHANICS | 3.0 |
| CBEN625 | MOLECULAR SIMULATION | 3.0 |
| CEEN505 | NUMERICAL METHODS FOR ENGINEERS | 3.0 |
| CEEN506 | FINITE ELEMENT METHODS FOR ENGINEERS | 3.0 |
| CEEN519 | RISK ASSESSMENT IN GEOTECHNICAL ENGINEERING | 3.0 |
| CEEN530 | ADVANCED STRUCTURAL ANALYSIS | 3.0 |
| CEEN531 | STRUCTURAL DYNAMICS | 3.0 |
| CEEN533 | MATRIX STRUCTURAL ANALYSIS | 3.0 |
| CEEN546 | STATISTICAL METHODS FOR RELIABILITY AND ENGINEERING DESIGN | 3.0 |
| CEEN566 | MICROBIAL PROCESSES, ANALYSIS AND MODELING | 3.0 |
| CHGC504 | METHODS IN GEOCHEMISTRY | 3.0 |
| CHGN507 | ADVANCED ANALYTICAL CHEMISTRY | 3.0 |
| CHGN583 | PRINCIPLES AND APPLICATIONS OF SURFACE ANALYSIS TECHNIQUES | 3.0 |
| CSCI507 | INTRODUCTION TO COMPUTER VISION | 3.0 |
| CSCI532 | ROBOT ETHICS | 3.0 |
| CSCI536 | HUMAN-ROBOT INTERACTION | 3.0 |
| CSCI544 | ADVANCED COMPUTER GRAPHICS | 3.0 |
| CSCI555 | GAME THEORY AND NETWORKS | 3.0 |
| CSCI561 | THEORY OF COMPUTATION | 3.0 |
| CSCI562 | APPLIED ALGORITHMS AND DATA STRUCTURES | 3.0 |
| CSCI563 | PARALLEL COMPUTING FOR SCIENTISTS AND ENGINEERS | 3.0 |
| CSCI564 | ADVANCED COMPUTER ARCHITECTURE | 3.0 |
| CSCI565 | DISTRIBUTED SYSTEMS | 3.0 |
| CSCI571 | ARTIFICIAL INTELLIGENCE | 3.0 |
| CSCI572 | COMPUTER NETWORKS II | 3.0 |
| CSCI574 | THEORY OF CRYPTOGRAPHY | 3.0 |
| CSCI575 | ADVANCED MACHINE LEARNING | 3.0 |
| CSCI576 | DEEP LEARNING | 3.0 |
| CSCI577 | ADVANCED ELEMENTS OF GAMES AND GAME DEVELOPMENT | 3.0 |
| CSCI578 | BIOINFORMATICS | 3.0 |
| CSCI581 | QUANTUM PROGRAMMING | 3.0 |
| CSCI582 | COMPUTING BEYOND CPU'S | 3.0 |
| CSCI583 | IOT SECURITY AND PRIVACY | 3.0 |
| CSCI585 | INFORMATION SECURITY PRIVACY | 3.0 |
| EBGN509 | MATHEMATICAL ECONOMICS | 3.0 |
| EBGN525 | BUSINESS ANALYTICS | 3.0 |
| EBGN526 | STOCHASTIC MODELS IN MANAGEMENT SCIENCE | 3.0 |
| EBGN527 | BUSINESS OPTIMIZATION MODELS | 3.0 |
| EBGN528 | INDUSTRIAL SYSTEMS SIMULATION | 3.0 |
| EBGN547 | FINANCIAL RISK MANAGEMENT | 3.0 |
| EBGN559 | SUPPLY CHAIN ANALYTICS | 3.0 |
| EBGN560 | DECISION ANALYTICS | 3.0 |
| EBGN570 | ENVIRONMENTAL ECONOMICS | 3.0 |
| EBGN571 | MARKETING ANALYTICS | 3.0 |
| EBGN590 | ECONOMETRICS I | 3.0 |
| EBGN594 | TIME-SERIES ECONOMETRICS | 3.0 |
| EBGN645 | COMPUTATIONAL ECONOMICS | 3.0 |
| EENG507 | INTRODUCTION TO COMPUTER VISION | 3.0 |
| EENG509 | SPARSE SIGNAL PROCESSING | 3.0 |
| EENG511 | CONVEX OPTIMIZATION AND ITS ENGINEERING APPLICATIONS | 3.0 |
| EENG514 | DATA SCIENCE FOR ELECTRICAL ENGINEERING | 3.0 |
| EENG515 | MATHEMATICAL METHODS FOR SIGNALS AND SYSTEMS | 3.0 |
| EENG519 | ESTIMATION THEORY AND KALMAN FILTERING | 3.0 |
| EENG521 | NUMERICAL OPTIMIZATION | 3.0 |
| EENG528 | COMPUTATIONAL ELECTROMAGNETICS | 3.0 |
| EENG581 | POWER SYSTEM OPERATION AND MANAGEMENT | 3.0 |
| EENG584 | POWER SYSTEM RISK MANAGEMENT | 3.0 |
| GEGN532 | GEOLOGICAL DATA ANALYSIS | 3.0 |
| GEGN568 | POINT CLOUD DATA ANALYSIS IN EARTH SCIENCE AND ENGINEERING | 3.0 |
| GEGN579 | PYTHON SCRIPTING FOR GEOGRAPHIC INFORMATION SYSTEMS | 3.0 |
| GEGN583 | MATHEMATICAL MODELING OF GROUNDWATER SYSTEMS | 3.0 |
| GEGN586 | NUMERICAL MODELING OF GEOCHEMICAL SYSTEMS | 3.0 |
| GEGN671 | LANDSLIDES: INVESTIGATION, ANALYSIS & MITIGATION | 3.0 |
| GEOL527 | SWIR (SHORT WAVELENGTH INFRA-RED) SPECTRAL ANALYSIS | 1.0 |
| GEOL613 | GEOLOGIC RESERVOIR CHARACTERIZATION | 3.0 |
| GPGN658 | SEISMIC WAVEFIELD IMAGING | 3.0 |
| HASS578 | GLOBAL ENVIRONMENTAL ISSUES | 3.0 |
| MATH500 | LINEAR VECTOR SPACES | 3.0 |
| MATH501 | APPLIED ANALYSIS | 3.0 |
| MATH506 | COMPLEX ANALYSIS II | 3.0 |
| MATH510 | ORDINARY DIFFERENTIAL EQUATIONS AND DYNAMICAL SYSTEMS | 3.0 |
| MATH514 | APPLIED MATHEMATICS I | 3.0 |
| MATH515 | APPLIED MATHEMATICS II | 3.0 |
| MATH531 | THEORY OF LINEAR MODELS | 3.0 |
| MATH532 | SPATIAL STATISTICS | 3.0 |
| MATH533 | TIME SERIES ANALYSIS AND ITS APPLICATIONS | 3.0 |
| MATH534 | MATHEMATICAL STATISTICS I | 3.0 |
| MATH535 | MATHEMATICAL STATISTICS II | 3.0 |
| MATH536 | ADVANCED STATISTICAL MODELING | 3.0 |
| MATH537 | MULTIVARIATE ANALYSIS | 3.0 |
| MATH538 | STOCHASTIC MODELS | 3.0 |
| MATH540 | PARALLEL SCIENTIFIC COMPUTING | 3.0 |
| MATH551 | COMPUTATIONAL LINEAR ALGEBRA | 3.0 |
| MATH552 | KERNEL-BASED APPROXIMATION METHODS | 3.0 |
| MATH557 | INTEGRAL EQUATIONS | 3.0 |
| MATH559 | ASYMPTOTICS | 3.0 |
| MATH572 | MATHEMATICAL AND COMPUTATIONAL NEUROSCIENCE | 3.0 |
| MATH582 | STATISTICS PRACTICUM | 3.0 |
| MEGN502 | ADVANCED ENGINEERING ANALYSIS | 3.0 |
| MEGN535 | MODELING AND SIMULATION OF HUMAN MOVEMENT | 3.0 |
| MEGN536 | COMPUTATIONAL BIOMECHANICS | 3.0 |
| MEGN544 | ROBOT MECHANICS: KINEMATICS, DYNAMICS, AND CONTROL | 3.0 |
| MEGN545 | ADVANCED ROBOT CONTROL | 3.0 |
| MEGN553 | COMPUTATIONAL FLUID DYNAMICS | 3.0 |
| MEGN554 | ORBITAL MECHANICS | 3.0 |
| MEGN571 | ADVANCED HEAT TRANSFER | 3.0 |
| MEGN586 | LINEAR OPTIMIZATION | 3.0 |
| MEGN587 | NONLINEAR OPTIMIZATION | 3.0 |
| MEGN588 | INTEGER OPTIMIZATION | 3.0 |
| MEGN592 | RISK AND RELIABILITY ENGINEERING ANALYSIS AND DESIGN | 3.0 |
| MEGN686 | ADVANCED LINEAR OPTIMIZATION | 3.0 |
| MEGN688 | ADVANCED INTEGER OPTIMIZATION | 3.0 |
| MNGN502 | GEOSPATIAL BIG DATA ANALYTICS | 3.0 |
| PEGN501 | APPLICATIONS OF NUMERICAL METHODS TO PETROLEUM ENGINEERING | 3.0 |
| PEGN513 | RESERVOIR SIMULATION I | 3.0 |
| PEGN590 | RESERVOIR GEOMECHANICS | 3.0 |
| PEGN624 | COMPOSITIONAL MODELING - APPLICATION TO ENHANCED OIL RECOVERY | 3.0 |
| PHGN511 | MATHEMATICAL PHYSICS | 3.0 |
| PHGN519 | FUNDAMENTALS OF QUANTUM INFORMATION | 3.0 |
| PHGN520 | QUANTUM MECHANICS I | 3.0 |
| PHGN521 | QUANTUM MECHANICS II | 3.0 |
| PHGN530 | STATISTICAL MECHANICS | 3.0 |
| ROBO517 | INTRODUCTION TO COMPUTER VISION | 3.0 |
| ROBO550 | MECHATRONICS | 3.0 |
| ROBO554 | ROBOT MECHANICS: KINEMATICS, DYNAMICS, AND CONTROL | 3.0 |
| ROBO565 | ADVANCED ROBOT CONTROL | 3.0 |
| ROBO572 | ROBOT ETHICS | 3.0 |
| ROBO576 | HUMAN-ROBOT INTERACTION | 3.0 |
Sample Course Schedule
| First Year | ||||
|---|---|---|---|---|
| Fall | lec | lab | sem.hrs | |
| DSCI503 | ADVANCED DATA SCIENCE | 3.0 | ||
| DSCI570 | INTRODUCTION TO MACHINE LEARNING | 3.0 | ||
| MATH530 | INTRODUCTION TO STATISTICAL METHODS | 3.0 | ||
| ELECT | Elective | 3.0 | ||
| 12.0 | ||||
| Spring | lec | lab | sem.hrs | |
| DSCI575 | ADVANCED MACHINE LEARNING | 3.0 | ||
| MATH560 | INTRODUCTION TO KEY STATISTICAL LEARNING METHODS I | 3.0 | ||
| ELECT | Elective | 3.0 | ||
| LICM501 | PROFESSIONAL ORAL COMMUNICATION | 1.0 | ||
| 10.0 | ||||
| Second Year | ||||
| Fall | lec | lab | sem.hrs | |
| MATH561 | INTRODUCTION TO KEY STATISTICAL LEARNING METHODS II | 3.0 | ||
| ELECT | Elective | 3.0 | ||
| SYGN502 | INTRODUCTION TO RESEARCH ETHICS | 1.0 | ||
| SYGN5XX | LEADERSHIP AND TEAMWORK | 1.0 | ||
| 8.0 | ||||
| Total Semester Hrs: 30.0 | ||||
Mines Combined Undergraduate / Graduate Degree Program
Students enrolled in Mines’ combined undergraduate/graduate program may double count up to six credits of graduate coursework to fulfill requirements of both their undergraduate and graduate degree programs. These courses must have been passed with “B-” or better, not be substitutes for required coursework, and meet all other University, Department, and Program requirements for graduate credit.
Students are advised to consult with their undergraduate and graduate advisors for appropriate courses to double count upon admission to the combined program.
Master of Data Science (Non-Thesis) Online
The Master of Data Science (Non-Thesis) program is designed to give candidates a foundation in statistics and computer science and also provide knowledge in a particular application domain of science or engineering. The balance between these three elements is a strength of the program and can prepare candidates for Data Science careers in industry, government, or for further study at the PhD level. Throughout is an emphasis on working in teams, creative problem solving, and professional development.
This non-thesis master’s program gives students a foundation in statistics and computer science, while also providing knowledge in a particular application domain of science or engineering. It is pitched a higher level of statistics and computer science than one would encounter in a typical data analytics curriculum.
The balance between these elements is a strength of the program and prepares students for data science careers in industry, government or for further study at the PhD level. The emphasis on some foundational knowledge will prepare students to be more innovative in their approach to data analysis and not rely on simply using software packages in a standard way. Moreover, the three elective courses can be tailored to each student's interests. This program allows students to either increase their skill in data analytics while developing a focused area of application or alternatively to allow a student with depth in one area of application to gain skills in statistics and computer science.
Program
Students take 18 credits of core courses, 3 credits of professional development coursework and at least 9 credits of electives for a minimum total of 30 credits.
Core Courses
| MATH530 | INTRODUCTION TO STATISTICAL METHODS Students who have passed MATH335 should replace MATH530 with any of the following: MATH500, MATH531, MATH532, MATH533, MATH534, MATH535, MATH536, MATH537, MATH538, or MATH551 | 3.0 |
| MATH560 | INTRODUCTION TO KEY STATISTICAL LEARNING METHODS I | 3.0 |
| MATH561 | INTRODUCTION TO KEY STATISTICAL LEARNING METHODS II | 3.0 |
| DSCI503 | ADVANCED DATA SCIENCE | 3.0 |
| DSCI570 | INTRODUCTION TO MACHINE LEARNING | 3.0 |
| DSCI575 | ADVANCED MACHINE LEARNING | 3.0 |
| Total Semester Hrs | 18.0 | |
Professional Development Requirement
Choose courses from the table below in any combination to meet or exceed 3 credits.
| EBGN562 | STRATEGIC DECISION MAKING | 3.0 |
| EBGN577 | LEADING & MANAGING HIGH PERFORMING TEAMS | 3.0 |
| EBGN563 | MANAGEMENT OF TECHNOLOGY AND INNOVATION | 3.0 |
| SYGN598 | SPECIAL TOPICS (Graduate Internship) | 1-3 |
| SYGN599 | INDEPENDENT STUDY | 1-3 |
Electives
Electives are designed by the student to fulfill their interests and career goals. The intent is to give you the knowledge and skills to apply data science techniques in various fields. Although this individualized module can draw on graduate courses from across the university the courses given below are approved options:
Online Electives
| AMFG523 | DESIGN AND ANALYSIS OF EXPERIMENTS | 3.0 |
| CBEN524 | COMPUTER-AIDED PROCESS SIMULATION | 3.0 |
| CCUS520 | CLIMATE CHANGE AND SUSTAINABILITY | 3.0 |
| CSCI585 | INFORMATION SECURITY PRIVACY | 3.0 |
| CSCI587 | CYBER PHYSICAL SYSTEMS SECURITY | 3.0 |
| EBGN525 | BUSINESS ANALYTICS | 3.0 |
| EBGN560 | DECISION ANALYTICS | 3.0 |
| EBGN562 | STRATEGIC DECISION MAKING | 3.0 |
| EBGN571 | MARKETING ANALYTICS | 3.0 |
| EENG509 | SPARSE SIGNAL PROCESSING | 3.0 |
| EENG510 | ADVANCED DIGITAL SIGNAL PROCESSING | 3.0 |
| EENG515 | MATHEMATICAL METHODS FOR SIGNALS AND SYSTEMS | 3.0 |
| EENG519 | ESTIMATION THEORY AND KALMAN FILTERING | 3.0 |
| EENG577 | ADVANCED ELECTRICAL MACHINE DYNAMICS FOR SMART-GRID SYSTEMS | 3.0 |
| EENG585 | AI FOR POWER AND RENEWABLE ENERGY SYSTEMS | 3.0 |
| GEGN575 | APPLICATIONS OF GEOGRAPHIC INFORMATION SYSTEMS | 3.0 |
| GEGN580 | APPLIED REMOTE SENSING FOR GEOENGINEERING AND GEOSCIENCES | 3.0 |
| GEGN588 | ADVANCED PLANETARY GEOGRAPHIC INFORMATION SYSTEMS | 3.0 |
| GEGN590 | GIS-BASED REAL WORLD LEARNING PROJECT I - FUNDAMENTALS | 3.0 |
| GEGN592 | GIS-BASED REAL WORLD LEARNING PROJECT II - ADVANCED APPLICATIONS | 3.0 |
| GEOL557 | EARTH RESOURCE DATA SCIENCE 1: FUNDAMENTALS | 3.0 |
| GEOL558 | EARTH RESOURCE DATA SCIENCE 2: APPLICATIONS AND MACHINE-LEARNING | 3.0 |
| GPGN545 | INTRODUCTION TO DISTRIBUTED FIBER-OPTIC SENSING AND ITS APPLICATIONS | 3.0 |
| GPGN558 | SEISMIC DATA INTERPRETATION AND QUANTITATIVE ANALYSIS | 3.0 |
| MNGN502 | GEOSPATIAL BIG DATA ANALYTICS | 3.0 |
Certificate Programs in Data Science
Program Requirements
There are five Certificates in Data Science. Applicants for each are required to have an undergraduate degree to be admitted into the Certificate programs. Course prerequisites, if any, are noted for each Certificate program.
Students working toward one of the Data Science Certificates are required to successfully complete 12 credits, as detailed below for each Certificate. The courses taken for the Certificates can be used towards a Master’s or PhD degree at Mines, however courses used for one Data Science Certificate cannot also be counted toward another Data Science Certificate.
Graduate Certificate in Data Science - Statistical Learning (12 credits)
The Data Science - Statistical Learning Graduate Certificate is an online or residential program focusing on statistical methods for interpreting complex data sets and quantifying the uncertainty in a data analysis. The Certificate also includes gaining new skills in computer science but is grounded in statistical models for data, also termed statistical learning, rather than algorithmic approaches. Students will develop an essential skill set in statistical methods most commonly used in data science along with the understanding of the methods' strengths and weaknesses. Moreover, the coursework will cover a broad range of applications making it relevant for varied scientific and engineering domains.
Applicants must have completed the following courses, or their equivalents, with a B- or better: CSCI261 and CSCI262 Data Structures, MATH332 Linear Algebra and MATH334 Introduction to Probability.
| DSCI503 | ADVANCED DATA SCIENCE | 3.0 |
| DSCI530 | STATISTICAL METHODS I | 3.0 |
| DSCI560 | INTRODUCTION TO KEY STATISTICAL LEARNING METHODS I | 3.0 |
| DSCI561 | INTRODUCTION TO KEY STATISTICAL LEARNING METHODS II | 3.0 |
Graduate Certificate in Data Science - Earth Resources (12 credits)
The Graduate Certificate in Data Science - Earth Resources is an online program building on the foundational concepts in data science as it pertains to managing surface and subsurface Earth resources and on specific applications (use cases) from the petroleum and minerals industries as well as water resource monitoring and remote sensing of Earth change. The Certificate includes one core introductory Data Science course, two courses specific to Earth resources and one elective.
| DSCI503 | ADVANCED DATA SCIENCE | 3.0 |
| GEOL557 | EARTH RESOURCE DATA SCIENCE 1: FUNDAMENTALS | 3.0 |
| GEOL558 | EARTH RESOURCE DATA SCIENCE 2: APPLICATIONS AND MACHINE-LEARNING | 3.0 |
| ELECTIVE | (1) ELECTIVE FROM LIST BELOW | 3.0 |
Graduate Certificate in Data Science - Earth Resources Electives (select ONE (1) from the list below):
| Geospatial Focus: | ||
| GEGN575 | APPLICATIONS OF GEOGRAPHIC INFORMATION SYSTEMS | 3.0 |
| GEGN579 | PYTHON SCRIPTING FOR GEOGRAPHIC INFORMATION SYSTEMS | 3.0 |
| Petroleum Focus: | ||
| GPGN519 | ADVANCED FORMATION EVALUATION | 3.0 |
| GPGN547 | PHYSICS, MECHANICS, AND PETROPHYSICS OF ROCKS | 3.0 |
| GPGN558 | SEISMIC DATA INTERPRETATION AND QUANTITATIVE ANALYSIS | 3.0 |
| GPGN651 | ADVANCED SEISMOLOGY | 3.0 |
| PEGN522 | ADVANCED WELL STIMULATION | 3.0 |
| PEGN551 | PETROLEUM DATA ANALYTICS - FUNDAMENTALS | 3.0 |
| Mining Focus: | ||
| MNGN548 | INFORMATION TECHNOLOGIES FOR MINING SYSTEMS | 3.0 |
| Hydrology Focus: | ||
| CEEN581 | WATERSHED SYSTEMS MODELING | 3.0 |
| Additional Options: | ||
| DSCI/MATH530 | STATISTICAL METHODS I | 3.0 |
| EBGN525 | BUSINESS ANALYTICS | 3.0 |
Graduate Certificate in Petroleum Data Analytics (12 credits)
The Graduate Certificate in Petroleum Data Analytics is an online program building on the foundational concepts in statistics and focusing on the data foundation of the oil and gas industry, the challenges of Big Data to oilfield operations and on specific applications (use cases) for petroleum analytics. The Certificate includes two core introductory Data Science courses and two course specific to petroleum engineering.
| DSCI503 | ADVANCED DATA SCIENCE | 3.0 |
| DSCI530 | STATISTICAL METHODS I | 3.0 |
| PEGN551 | PETROLEUM DATA ANALYTICS - FUNDAMENTALS | 3.0 |
| PEGN552 | PETROLEUM DATA ANALYTICS - APPLICATIONS | 3.0 |
Graduate Certificate in Business Analytics
The certificate is an online or residential program. The requirements are to complete the core course and two elective courses: | Core Course | ||
| EBGN525 | BUSINESS ANALYTICS | 3.0 |
| Elective Courses | ||
| EBGN559 | SUPPLY CHAIN ANALYTICS | 3.0 |
| EBGN560 | DECISION ANALYTICS | 3.0 |
| EBGN571 | MARKETING ANALYTICS | 3.0 |
Course substitutions may be approved on a case-by-case basis by the certificate director. Completing the certificate will also position students to apply to either the master of science in engineering and technology management degree or the master of science in data science degree, as the certificate courses can be applied to either degree.
Graduate Certificate in Data Science - Foundations (12 credits)
The Data Science - Foundations Graduate Certificate is an online or residential program focusing on the foundational concepts in statistics and computer science that support the explosion of new methods for interpreting data in its many forms. The Certificate balances an introduction to data science with teaching basic skills in applying methods in statistics and machine learning to analyze data. Students will gain a perspective on the kinds of problems that can be solved by data intensive methods and will also acquire new analysis skills outside of the certificate. Moreover, the coursework will cover a broad range of applications, making it relevant for varied scientific and engineering domains.
Applicants must have completed the following courses, or their equivalents, with a B- or better: CSCI261 and CSCI262 Data Structures, MATH332 Linear Algebra and MATH334 Introduction to Probability.
| DSCI503 | ADVANCED DATA SCIENCE | 3.0 |
| DSCI570 | INTRODUCTION TO MACHINE LEARNING | 3.0 |
| MATH530 | INTRODUCTION TO STATISTICAL METHODS | 3.0 |
| MATH560 | INTRODUCTION TO KEY STATISTICAL LEARNING METHODS I |
Graduate Certificate in Data Science - Computer Science (12 credits)
The Data Science - Computer Science Graduate Certificate is an online or residential program focusing on data science concepts within computer science (e.g., computational techniques and machine learning) plus prerequisite knowledge (e.g., probability and regression). The aim of this certificate is to help students develop an essential skill set in data analytics, including (1) deriving predictive insights by applying advanced statistics, modeling, and programming skills, (2) acquiring in-depth knowledge of machine learning and computational techniques, and (3) unearthing important questions and intelligence for a range of industries, from product design to finance.
Applicants must have completed the following courses, or their equivalents, with a B- or better: CSCI261 and CSCI262 Data Structures, MATH213 Calculus III and MATH332 Linear Algebra. DSCI530 Statistical Methods I, will serve as the MATH201 Probability and Statistics prerequisite for the two machine learning courses of the certificate (DSCI570 Introduction to Machine Learning and DSCI575 Machine Learning).
| DSCI503 | ADVANCED DATA SCIENCE | 3.0 |
| MATH530 | INTRODUCTION TO STATISTICAL METHODS | 3.0 |
| DSCI570 | INTRODUCTION TO MACHINE LEARNING | 3.0 |
| DSCI575 | ADVANCED MACHINE LEARNING | 3.0 |
Courses
DSCI503. ADVANCED DATA SCIENCE. 3.0 Semester Hrs.
(I, II) This course will teach students the core skills needed for gathering, cleaning, organizing, analyzing, interpreting, and visualizing data. Students will use the python programming language and related toolkits for data manipulation and the use and application of statistical and machine learning for data analysis. The course will be primarily focused on applications, with an emphasis on working with real (non-synthetic) datasets. Students will propose and design a semester project using a dataset from their domain of interest, leveraging the concepts and skills acquired from this course (e.g., data analysis, ethical considerations, evaluation and synthesis of results, storytelling and visualization). Prerequisite: CSCI200 with a grade of C- or higher or CSCI262 with a grade of C- or higher, MATH201 or MATH334 OR Graduate level standing and at least CSCI128 or equivalent.
View Course Learning Outcomes
- Conduct data acquisition using a varied set of techniques structured and unstructured datasets; including raw data files, SQL databases, online repositories, and programmatically through web scraping and APIs.
- Apply preprocessing strategies to complex and dynamic datasets using industry-standard toolkits and machine learning algorithms to extract features, reduce dimensionality, remove errors, inconsistencies, and missing values.
- Differentiate between machine learning approaches such as classification, regression, clustering, and neural networks for predictive analytics and pattern recognition.
- Evaluate the predictive power of the different statistical and machine learning methods to solve real-world data science problems.
- Develop storytelling and visualization techniques to effectively communicate (exploratory) or persuade (explanatory) findings to a specific audience.
- Critically asses ethical considerations and challenges related to data collection and analysis.
- Construct a comprehensive data science project from inception to presentation, integrating the various techniques and tools learned throughout the course.
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 using the R Data Analysis Environment. Confidence intervals and hypothesis testing for means and variances. Chi square tests. Distribution-free techniques and regression analysis. Students are expected to have knowledge of probability covered in MATH334 or an equivalent course. Prerequisite: MATH334 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. Prerequisite: DSCI530 or MATH530.
DSCI561. INTRODUCTION TO KEY STATISTICAL LEARNING METHODS II. 3.0 Semester Hrs.
Equivalent with MATH561,
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. Prerequisite: DSCI560 or MATH560.
DSCI570. INTRODUCTION TO MACHINE LEARNING. 3.0 Semester Hrs.
(I, II) 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. Graduate students must complete a more challenging project that utilizes complex machine learning algorithms, requiring a deeper understanding of machine learning approaches and critical thinking. Prerequisite: DSCI503.
View Course Learning Outcomes
- Apply supervised, unsupervised, reinforcement machine learning models and deep learning models to solve problems in areas such as prediction, recognition and classification.
- Explore and develop with various tools, techniques and libraries in Python for data processing, feature extraction, visualization, validation and evaluation.
- Create data visualization tools, techniques, and libraries in Python to visualize high dimensional or complex data for stakeholders.
- Determine ethical implications through interpretability of big data and results from the application of various machine learning models.
- Design and develop a machine learning product that solves their chosen real-world challenge.
- Create a video presentation that succinctly outlines the problem, solutions, conclusions, and lessons learned regarding product development for the stakeholders.
DSCI575. ADVANCED 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: DSCI570.
DSCI576. DEEP LEARNING. 3.0 Semester Hrs.
Machine Learning is a key component of Artificial Intelligence that allows computers to generate accurate outcomes without being explicitly programmed. Over the past decade, machine learning has powered speech recognition, augmented reality, self-driving vehicles, and biometric authentications. The purpose of this course is to bridge general machine learning concepts to real world applications. This course provides a broad introduction to machine learning (supervised learning, unsupervised learning, feature learning), deep learning, computer vision, natural language processing, and reinforcement learning. During the course, students will design a project that applies concepts from class to several different real-world problems and challenges and create applications to solve them. Prerequisite: CSCI470.
View Course Learning Outcomes
- Design deep learning models to address supervised and unsupervised learning tasks implemented in Python that meet evaluation criteria such as accuracy, F1 score, and mean-square error.
- Apply state-of-the-art deep learning architectures for computer vision tasks involving object detection, image generation, and self-driving vehicles.
- Develop deep learning models for natural language processing tasks using pre-trained transformers.
- Utilize Markov Decision Process, Monte Carlo Method, and deep reinforcement learning techniques to solve sequential decision-making problems.
- Evaluate ethical challenges in deep learning models, including bias, fairness, transparency, and interpretability.
- Demonstrate clear communication of deep learning results and insights to both technical and non-technical audiences.