# Master’s Non-Thesis

##### Program Prerequisites

The Masters Non-thesis program requires a Bachelor’s degree in engineering, computer science, physical sciences, mathematics, economics or equivalent quantitative coursework, with a minimum grade-point average on 3.0 on a 4.0 scale. In preparation for the programming, probability and mathematical foundation required to succeed in the program, we recommend applicants have taken MATH 332 – LINEAR ALGEBRA, MATH 334 – INTRODUCTION TO PROBABILTY, and CSCI 261 – PROGRAMMING CONCEPTS, or comparable courses elsewhere.

##### Program Details

The Masters in Data Science program will follow a 3 × 3 + 3 × 1 design: three modules each consisting of three, 3-credit courses, plus a mini-module comprising 3-credit hours for three, 1-credit courses.

##### Program Modules

**1. Data modeling and statistical learning (Contact Douglas Nychka).***Students will take all three of the following classes.*

• DSCI/MATH 530 – STATISTICAL METHODS

• DSCI/MATH 560 – STATISTICAL LEARNING I

• DSCI/MATH 561 – STATISTICAL LEARNING II

**2. Machine learning, data processing and algorithms, and parallel computation (Contact Hua Wang).***Students will take all three of the following classes.*

• DSCI 403 / CSCI 303 – INTRODUCTION TO DATA SCIENCE (Prerequisite: CSCI 261 – PROGRAMMING CONCEPTS)

• DSCI/CSCI 470 – INTRODUCTION TO MACHINE LEARNING

• DSCI/CSCI 575 – MACHINE LEARNING or CSCI563 PARALLEL COMPUTING FOR SCIENTISTS AND ENGINEERS

**3. Individualized and domain specific coursework.***Graduate courses in this module will be tailored to each student’s interests and the**suitability will be managed by the program curriculum committee.*

Some examples are:

- Business Analytics (Contact Michael Heeley)
- EBGN 525 – BUSINESS ANALYTICS
- EBGN 560 – DECISION ANALYSIS
- EBGN 571 – MARKETING ANALYTICS

- Chemical and Biological Engineering (Contact Ning Wu)
- CBEN 420 – MATHEMATICAL METHODS IN CHEMICAL ENGINEERING
- CBEN 624 – APPLIED STATISTICAL MECHANICS
- CBEN 625 – MOLECULAR SIMULATION

- Data Science for Systems of Extractive Mining Industries-Mining and Oil & Gas (Contact Sebnem Duzgun)
- MNGN 548 – INFORMATION TECHNOLOGIES FOR MINING SYSTEMS
- MNGN 598A – GEOSPATIAL BIG DATA ANALYTICS
- MNGN 598C – INNOV8x

- Economics (3 out the following 4 courses) (Contact Michael Heeley)
- EBGN 590 – ECONOMETRICS AND FORECASTING
- EBGN 690 – ADVANCED ECONOMETRICS
- EBGN 525 – BUSINESS ANALYTICS
- EBGN 594 – TIME SERIES ECONOMETRICS

- Electrical Engineering (Contact Michael Wakin) 3 out the following 4 courses
- EENG 411 – DIGITAL SIGNAL PROCESSING
- EENG 511 – CONVEX OPTIMIZATION AND ENG. APPLICATIONS
- EENG 515 – MATHEMATICAL METHODS FOR SIGNALS AND SYSTEMS
- EENG 519 – ESTIMATION THEORY AND KALMAN FILTERING

- Geophysics (Contact Paul Sava)
- GPGN 533 – GEOPHYSICAL DATA INTEGRATION & GEOSTATISTICS
- GPGN 570 – APPLICATIONS OF SATELLITE REMOTE SENSING
- GPGN 605 – INVERSION THEORY

- Quantum Engineering (Contact Serena Eley)
- PHGN 519 – Fundamentals of Quantum Information
*(Required)**Choose 2 of the following:* - CSCI 581 – Quantum Programming
- PHGN 545 – Quantum Many-Body Physics
- PHGN 435/535 – Interdisciplinary Microelectronics Processing Laboratory
- PHGN 532/EEGN 532 – Low temperature and Microwave Measurements for Quantum Applications

- PHGN 519 – Fundamentals of Quantum Information

Specializations:

- Electricity Markets
- EBGN 510 – NATRUAL RESOURCE ECONOMICS
- EBGN 632 – PRIMARY FUELS
- EBGN 645 – COMPUTATIONAL ECONOMICS

- Oil and Gas Markets
- EBGN 530 – INTERNATIONAL ENERGY MARKETS
- EBGN 632 – PRIMARY FUELS
- EBGN 594 – TIME SERIES ECONOMETRICS

- Mineral Markets
- EBGN 535 – METALS MARKETS
- EBGN 547 – FINANCIAL RISK MANAGEMENT
- MNGN 528 – GRADUATE MINING GEOLOGY

##### Program Mini Module

Professional skills and career development*Students will take the following three courses, or other courses with prior approval from the Program Director.*

• SYGN502 INTRODUCTION TO RESEARCH ETHICS

• SYGN598 LEADERSHIP AND TEAMWORK

• LICM501 PROFESSIONAL ORAL COMMUNICATION

# 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.

##### Post-Baccalaureate 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 Post-Baccalaureate 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.

DSCI403 INTRODUCTION TO DATA SCIENCE

DSCI470 INTRODUCTION TO MACHINE LEARNING

DSCI530 STATISTICAL METHODS I

DSCI560 INTRODUCTION TO KEY STATISTICAL LEARNING METHODS I

##### Post Baccalaureate 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 Post Baccalaureate 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.

DSCI403 INTRODUCTION TO DATA SCIENCE

DSCI530 STATISTICAL METHODS I

DSCI470 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.

DSCI403 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.

DSCI403 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

DSCI403 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.