Master’s Non-Thesis
The MSNT degree comprises 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. Prospective students are expected to have a grounding in math and computer science, with full application details on the Data Science admissions page.
Core Courses
The core courses cover data modeling, statistical learning, machine learning, data processing and algorithms, and parallel computation.
- DSCI/MATH 530 – STATISTICAL METHODS
- DSCI/MATH 560 – STATISTICAL LEARNING I
- DSCI/MATH 561 – STATISTICAL LEARNING II
- DSCI 503 / CSCI 503 – ADVANCED DATA SCIENCE
- DSCI/CSCI 570 – INTRODUCTION TO MACHINE LEARNING
- DSCI/CSCI 575 – MACHINE LEARNING
Electives
Electives are designed by the student to fulfill their interests and career goals. The intent is to develop the knowledge and skills to apply data science techniques in various fields. Although students can draw on graduate courses from across the university the catalog has the most current list of approved courses. Elective options include:
- CBEN 624 – APPLIED STATISTICAL MECHANICS
- CBEN 625 – MOLECULAR SIMULATION
- CSCI 571 – ARTIFICIAL INTELLIGENCE
- CSCI 581 – QUANTUM PROGRAMMING
- EBGN 525 – BUSINESS ANALYTICS
- EBGN 571 – MARKETING ANALYTICS
- EBGN 590 – ECONOMETRICS AND FORECASTING
- EBGN 594 – TIME SERIES ECONOMETRICS
- EENG 515 – MATHEMATICAL METHODS FOR SIGNALS AND SYSTEMS
- EENG 519 – ESTIMATION THEORY AND KALMAN FILTERING
- GEGN 579 – PYTHON SCRIPTING FOR GIS
- GPGN 533 – GEOPHYSICAL DATA INTEGRATION & GEOSTATISTICS
- GPGN 570 – APPLICATIONS OF SATELLITE REMOTE SENSING
- MATH 533 – TIME SERIES ANALYSIS AND ITS APPLICATIONS
- MATH 572 – MATHEMATICAL AND COMPUTATIONAL NEUROSCIENCE
- MEGN 535 – MODELING AND SIMULATION OF HUMAN MOVEMENT
- MNGN 548 – INFORMATION TECHNOLOGIES FOR MINING SYSTEMS
- MNGN 502 – GEOSPATIAL BIG DATA ANALYTICS
- PHGN 519 – FUNDAMENTALS OF QUANTUM INFORMATION
- ROBO 554 – ROBOT MECHANICS: KINEMATICS, DYNAMICS, AND CONTROL
Professional Development
Students take one to three courses totaling three credits in topics to help develop professional skills. Below are some example courses, with the full list of approved courses in the catalog.
- SYGN502 INTRODUCTION TO RESEARCH ETHICS
- SYGN598 GRADUATE INTERNSHIP
- SYGN683 ORAL COMMUNICATION SKILLS
- SYGN684 WRITING SKILLS
- LICM501 PROFESSIONAL ORAL COMMUNICATION
- EBGN563 MANAGEMENT OF TECHNOLOGY AND INNOVATION
- EBGN566 TECHNOLOGY ENTREPRENEURSHIP
Choose what works for you!
Both online and on-campus options are available for all Data Science degrees.
Certificate Programs in Data Science
The Data Science program currently offers five certificates. Each certificate consists of four graduate level courses and are a mix of data science and a specific area of application. Applicants are required to have an undergraduate degree to be admitted into the certificate programs. Course prerequisites, if any, are noted for each certificate program and list courses offered at Mines. However, comparable coursework at other institutions will be accepted. (Applicants are encouraged to contact the Data Science Program Director if they feel their background merits waiving the prerequisite coursework based on work experience or other factors.)
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 - Foundations
Program Prerequisites
Applicants are required to have an undergraduate degree and must have completed the following courses, with a B- or better: CSCI261 and CSCI262 Data Structures, MATH332 Linear Algebra and MATH334 Introduction to Probability, or comparable courses elsewhere.
Program Details
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.
DSCI503 INTRODUCTION TO DATA SCIENCE
DSCI570 INTRODUCTION TO MACHINE LEARNING
DSCI530 STATISTICAL METHODS I
DSCI560 INTRODUCTION TO KEY STATISTICAL LEARNING METHODS I
Graduate Certificate in Data Science - Computer Science
Program Prerequisites
Applicants are required to have an undergraduate degree, and must have completed the following courses with a B- or better: CSCI261 and CSCI262 Data Structures, MATH213 Calculus III and MATH332 Linear Algebra, or comparable courses elsewhere. DSCI530 Statistical Methods I, will serve as the MATH201 Probability and Statistics prerequisite for the two machine learning courses of the certificate (DSCI470 Introduction to Machine Learning and DSCI575 Machine Learning).
Program Details
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.
DSCI503 INTRODUCTION TO DATA SCIENCE
DSCI530 STATISTICAL METHODS I
DSCI570 INTRODUCTION TO MACHINE LEARNING
DSCI575 MACHINE LEARNING
Graduate Certificate in Data Science - Statistical Learning
Program Prerequisites
Applicants are required to have an undergraduate degree and must have completed the following courses with a B- or better: CSCI261 and CSCI262 Data Structures, MATH332 Linear Algebra and MATH334 Introduction to Probability, or comparable courses elsewhere.
Program Details
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.
DSCI503 INTRODUCTION TO DATA SCIENCE
DSCI530 STATISTICAL METHODS I
DSCI560 INTRODUCTION TO KEY STATISTICAL LEARNING METHODS I
DSCI561 INTRODUCTION TO KEY STATISTICAL LEARNING METHODS II
Graduate Certificate in Data Science - Earth Resources
Program Prerequisites
Applicants are required to have an undergraduate degree and must have completed the following courses with a B- or better: CSCI261 and CSCI262 Data Structures, MATH332 Linear Algebra and MATH334 Introduction to Probability, or comparable courses elsewhere.
Program Details
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 INTRODUCTION TO DATA SCIENCE
GEOL557 EARTH RESOURCE DATA SCIENCE 1: FUNDAMENTALS
GEOL558 EARTH RESOURCE DATA SCIENCE 2: APPLICATIONS AND MACHINE-LEARNING
ELECTIVE (1) ELECTIVE (see list of approved electives in the Academic Catalog listing for this certificate)
Graduate Certificate in Petroleum Data Analytics
Program Prerequisites
Applicants are required to have an undergraduate degree, and must have completed the following courses with a B- or better: CSCI261 and CSCI262 Data Structures, MATH332 Linear Algebra, or comparable courses elsewhere.
Program Details
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.
DSCI530 STATISTICAL METHODS I
DSCI503 INTRODUCTION TO DATA SCIENCE
PEGN551 PETROLEUM DATA ANALYTICS – FUNDAMENTALS
PEGN552 PETROLEUM DATA ANALYTICS – APPLICATIONS
Graduate Certificate in Business Analytics
Program Prerequisites
Applicants are required to have an undergraduate degree to be admitted into the certificate program.
Program Details
The certificate is an online or residential program. Students are required to complete the following three courses:
EBGN525 BUSINESS ANALYTICS
EBGN560 DECISION ANALYTICS
EBGN571 MARKETING ANALYTICS
Course substitutions can be approved on a case-by-case basis by the certificate directors. Completing the certificate will also position students to apply to either the Master of Science-Engineering and Technology Management degree or the Master of Science in Data Science degree, as all the certificate courses can be applied to either degree.