Overview
Data Science is a rapidly evolving field that integrates computer science, statistics, and various interdisciplinary applications to extract meaningful insights from data. The Master of Science in Data Science programme at Colorado School of Mines is structured to provide students with the necessary skills and knowledge to excel in this dynamic area. The programme is designed for those who possess a bachelor’s degree and are looking to deepen their understanding of data analysis, modelling, and computational techniques.
Why Data Science at Colorado School of Mines?Colorado School of Mines is renowned for its exceptional return on investment and is ranked as the top public university in Colorado. The institution is committed to providing cutting-edge education and research opportunities in data science, supported by state-of-the-art facilities and partnerships with industry leaders. The projected job growth for mathematicians and statisticians is 33% from 2020 to 2030, indicating a strong demand for skilled professionals in this field.
Tuition Fee BreakdownThe tuition fees for the Data Science programme are as follows:
- International Fee: USD 42,840 per year
- Domestic Fee: USD 42,840 per year
- Local Fee: USD 19,116 per year
Visit the Fees and Funding section for a breakdown in your local currency.
SyllabusThe curriculum is structured around a 3 X 3 + 1 model, consisting of three modules, each with three 3-credit courses, plus a mini-module of three 1-credit professional development courses. The modules cover:
- Data Modelling and Statistical Learning
- Machine Learning, Data Processing, and Algorithms
- Individualised and Domain-Specific Coursework
Students must also have a solid foundation in probability and mathematics, with recommended courses including Linear Algebra and Introduction to Probability.
Careers with Data ScienceGraduates of the Data Science programme are well-equipped to pursue various roles in the industry. Career opportunities include:
- Data Scientist
- Data Analyst
- Machine Learning Engineer
- Business Intelligence Developer
- Quantitative Analyst
- Algorithm Engineer
- Cloud Data Engineer
- Statistical Programmer
Alumni have found success in sectors such as healthcare, finance, technology, and academia, leveraging their skills to solve complex problems and drive data-driven decision-making.
Programme Structure
Courses include:
- Statistical Methods
- Statistical Learning
- Digital Signal Processing
- Sparse Signal Processing
- Convex Optimization and its Engineering Applications
- Mathematical Methods for Signals and Systems
Key information
Duration
- Full-time
- 12 months
Start dates & application deadlines
- Starting
- Apply before , National
-
- Starting
- Apply before , International
- Apply before , National
-
- Starting
- Apply before , International
-
Language
Prepare for Your English Test
AI-powered IELTS feedback. Clear, actionable, and tailored to boost your writing & speaking score. No credit card or upfront payment required.
- Trusted by 300k learners
- 98 accuracy using real exam data
- 4.9/5 student rating
Credits
Delivered
Campus Location
- Golden, United States
Disciplines
Data Science & Big Data View 461 other Masters in Data Science & Big Data in United StatesWhat students do after studying
Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
Prepare for Your English Test
AI-powered IELTS feedback. Clear, actionable, and tailored to boost your writing & speaking score. No credit card or upfront payment required.
- Trusted by 300k learners
- 98 accuracy using real exam data
- 4.9/5 student rating
Other requirements
General requirements
Certificate
- Resume or Curriculum Vitae (CV)
- Statement of Purpose: Not Required. Suggested if GPA is less than 3.0/4.0
- Transcript(s): Required. Must be submitted for all schools attended (unofficial transcripts accepted for admissions review and must show successful completion of any required prerequisite course(s).
- Bachelor’s degree
- Letters of Recommendation: Required – two letters. Only one letter of recommendation is required for current Mines students or Mines alumni.
- Resume or Curriculum Vitae (CV)
- Statement of Purpose
- Transcript(s): Required. Must be submitted for all schools attended (unofficial transcripts accepted for admissions review and must show successful completion of any required prerequisite course(s).
Tuition Fees
-
International Applies to you
Applies to youNon-residents42840 USD / year≈ 42840 USD / year - Out-of-State42840 USD / year≈ 42840 USD / year
-
Domestic
Applies to youIn-State19692 USD / year≈ 19692 USD / year
Living costs
Golden
The living costs include the total expenses per month, covering accommodation, public transportation, utilities (electricity, internet), books and groceries.
Financing
Interested in financing your studies? Find a student loan that works for you.
Get the funding you need to study in the U.S. or Canada - with a process that's fast, simple, and built for international students.
- Flexible loans from US$2,001 to US$100,000
- Fixed interest rates -- no inflation surprises
- No upfront fees or prepayment penalties
- Accepted at 500+ universities across the U.S. & Canada
- 100% online application -- instant conditional offer
- Free visa & career support through our Path2Success program
Funding
In order for us to give you accurate scholarship information, we ask that you please confirm a few details and create an account with us.
Scholarships Information
Below you will find Master's scholarship opportunities for Data Science.
Available Scholarships
You are eligible to apply for these scholarships but a selection process will still be applied by the provider.
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility
Read more about eligibility