Data Science
Graduate Program at Colorado School of Mines

Data Science
Graduate Program at Colorado School of Mines

Data Science Program Overview

The Master of Science in Data Science program gives students a foundation in statistics and computer science as well as the knowledge to apply these skills to a particular science or engineering discipline. The program follows a 3 X 3 + 1 design: three modules, each consisting of three 3-credit courses in a particular aspect of data science (data modeling and statistical learning; machine learning, data processing and algorithms and parallel computation; individualized and domain-specific coursework), plus a mini-module of three 1-credit professional development courses.

The five certificate programs are designed for college graduates and professionals seeking entry into this emerging field.

The 12-credit Post-Baccalaureate Certificate in Data Science – Foundations provides an introduction to foundational concepts in statistics and computer science as well as applying methods to analyze data. Students will gain a perspective on the kinds of problems that can be solved by data-intensive methods.

The Post-Baccalaureate Certificate in Data Science – Computer Science, also 12 credits, focuses on data science concepts within computer science. It helps students develop essential skills, 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.

The 12-credit Graduate Certificate in Data Science – Statistical Learning covers statistical methods for interpreting complex data sets and quantifying the uncertainty in a data analytics. The program helps students gain new skills in computer science but is grounded in statistical models for data rather than algorithmic approaches.

The Graduate Certificate in Data Science – Earth Resources, 12 credits, builds on the foundational concepts in data science as it pertains to managing surface and subsurface Earth resources and on specific applications from the petroleum and minerals industries as well as water resource monitoring and remote sensing of Earth change.

The Graduate Certificate in Petroleum Data Analytics, 12 credits, is focused on the data foundation of the oil and gas industry, the challenges of Big Data to oilfield operations and on specific applications for petroleum analytics.

 

Data Science Program Facts

Request for additional information

Fill out this form to receive more information about this exciting program.

Loading...

Video: The Graduate Experience at Mines

Program Details

Master of Science, Non-Thesis

  • A bachelor’s degree in engineering, computer science, physical sciences, mathematics, economics or equivalent quantitative coursework, with a GPA of at least 3.0 on a 4.0 scale. Some facility in a computer language and mathematics, including linear algebra, is recommended in order to complete this program in three semesters.
  • A personal statement explaining how one’s professional goals, training and experience are a good fit for a data science program
  • A TOEFL score of 79 or higher (or 550 for the paper-based test, 213 for the computer-based test) is required for international applications or applicants whose native language is not English. In lieu of a TOEFL score, an IELTS score of 6.5 or higher will be accepted.

Certificate Programs

  • Applicants must have an undergraduate degree.
  • For the Post-Baccalaureate Certificate in Data Science – Foundations and the Graduate Certificate in Data Science – Statistical Learning, 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.
  • For the Post-Baccalaureate Certificate in Data Science – Computer Science, 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. MATH530 Statistical Methods I will serve as the MATH201 Probability and Statistics prerequisite for the two machine learning courses of the certificate (CSCI470/DSCI470 Introduction to Machine Learning and CSCI575/DSCI575 Machine Learning).

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.

 Colorado ResidentOut-of-State Student
Tuition**$16,650$37,350
Fees*$2,412$2,412
Room & Board$16,700$16,700
Books & Supplies$1,500$1,500
Misc. Expenses$1,800$1,800
Transportation$1,300$1,300
Total$40,362$61,062
**Cost per credit hour$925$2,075

*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.

Soutir Bandyopadhyay

Soutir Bandyopadhyay

Rank - Associate Professor

Email - sbandyopadhyay@mines.edu

Phone - 303-273-3677

Wendy Fisher

Wendy Fisher

Rank - Assistant Department Head and Teaching Associate Professor

Email - wfisher@mines.edu

Phone - 303-273-3195

Dorit Hammerling

Dorit Hammerling

Rank - Associate Professor

Email - hammerling@mines.edu

Phone - 303-384-2272

Doug Nychka

Doug Nychka

Rank - Professor

Email - nychka@mines.edu

Phone - 303-384-2469

Paul Sava

Paul Sava

Rank - Professor and C.H. Green Chair of Exploration Geophysics

Email - psava@mines.edu

Phone - 303-384-2362

Michael Wakin

Michael Wakin

Rank - Professor

Email - mwakin@mines.edu

Phone - 303-273-3607

Hua Wang

Hua Wang

Rank - Associate Professor

Email - huawang@mines.edu

Phone - 303-384-2326

Student Testimonial

Career Outcomes

  • Algorithm Engineer
  • Analyst
  • Analyst, Partnerships Science
  • Analytical Programmer, Biostatistics and Data Science
  • Analytics Consultant
  • Analytics Strategy Manager
  • Applied Scientist
  • Artificial Intelligence Data Scientist
  • Bioinformatics Data Engineer
  • Bioinformatics Scientist
  • Business Analytics
  • Business Intelligence Developer
  • Business Intelligence Engineer
  • Client Solutions Statistician
  • Clinical Data Manager
  • Clinical Trial Data Science Consultant
  • Cloud Data Engineer
  • Data Analyst
  • Data and Applied Scientist
  • Data Architect
  • Data Science Analyst
  • Data Science Engineer
  • Data Science Manager
  • Data Science Strategist
  • Data Scientist
  • Data Visualization
  • Decision Science Analyst
  • Deep Learning Researcher
  • Financial Modeling and Research
  • Machine Learning Engineer
  • Mathematical Statistician
  • Model Validation Data Scientist
  • People Analytics
  • Product Strategy Leader
  • Programmer Analyst
  • Quantitative Analyst
  • Quantitative Modeling / Data Science Associate
  • Quantitative Researcher
  • Quantitative Risk Analyst
  • Research Science Manager
  • Research Scientist
  • Robotics and Artificial Intelligence Research Scientist
  • Scientific Programmer
  • Senior Programmer and Data Analyst
  • Software Engineer – Mathematical Modeling
  • Solution Designer
  • Spatial Data Scientist
  • Statistical Programmer
  • Statistician
  • System Engineer
  • Systems Analyst
  • Systems Modeling and Optimization
  • Test Engineer

The Harvard Business Review called data scientist “the sexiest job of the 21st century,” all the way back in 2012.

Glassdoor has named data scientist the No. 2 best job in America for 2021, after holding the top spot four years running, from 2016 to 2019, and taking No. 3 in 2020. That’s all no surprise, given the median base salary of $107,801 and a job satisfaction rating of 4.0 on a scale of 1 to 5.

LinkedIn’s 2020 Emerging Jobs Report, which spotlights jobs experiencing tremendous growth, ranked data scientist at No. 3, with 37 percent annual growth since 2015 and average pay of $143,000 per year.

The data science field has topped the report three years running, and LinkedIn says it’s a specialty that continues to grow across all industries. “Our data indicates some of this growth can likely be attributed to the evolution of previously existing jobs, like statisticians, and increased emphasis on data in academic research,” the report says.

Skills unique to the job include machine learning, data science, Python, R and Apache Spark, and the top industries hiring data scientists are information technology and services, computer software, internet, financial services and higher education.

Data engineer also made LinkedIn’s list at No. 8, with 33 percent annual growth since 2015. “Data has quickly become every company’s most valuable resource, and they need savvy engineers that can build infrastructure to keep it organized,” the report says. “Industries from retail to automotive are snapping up this hard-to-hire talent.”

Data specialists were among the tech positions in high demand across North America, according to human resources consulting company Robert Half, along with business intelligence analysts and machine learning specialists.

According to the company’s 2020 salary guide, data jobs were well compensated in the United States. Here’s a sampling of job titles and median annual salaries:

  • Big data engineer: $163,250
  • AI architect: $143,750
  • Data architect: $141,250
  • Data scientist: $125,250
  • Data modeler: $101,750
  • Business intelligence analyst: $110,250
  • Data analyst / report writer: $100,250
  • Data warehouse analyst: $99,250

Grad School Insights

Resources

Our Home is Golden

See what it’s like to live in one of the country’s best small towns, with easy access to the great outdoors and the thriving hub that is Denver.

Quick Tour

From high above and on the ground, see what makes Colorado School of Mines, its campus and its people special.

Connecting to your Future

Mines students are top of the list for industry recruiters—find out what makes our grads special and how we help you on your journey.