Online Master's in Data Science for Jobs in Colorado

Data scientists in Colorado are instrumental in almost every industry – from finance and insurance, to healthcare, the biotech and pharmaceutical industries; and from retail, manufacturing and marketing, to HR and tax revenue collection in the public sector.

The vast majority of data scientists are pursuing advanced degrees because of the advancement opportunities and high earning potential it brings. This number has increased in recent years, and in 2019, executive recruiting firm Burtch Works reported that 94% of data scientists hold master’s degrees. According to the firm, the median salary for a junior level analyst is $95,000, but predictive analysts who manage a team of ten or more earn salaries of $248,000 or even higher.

Not only do they get to live in Colorado, but data scientists have the opportunity to corral jobs here with cutting edge companies like AirDNA in Denver. Analysts mine through more than seven terabytes of geospatial, descriptive, and time-series data on short-term rental markets like AirBnb to provide a competitive edge in the market. Data scientists may also work at smaller outfits like Boulder’s Viola AI, which does specialized consulting in data science and data products. The high-caliber team is always shopping for new experts with a firm grasp of machine learning, Python, R, and experience in the AWS and Hadoop ecosystems.

With the opportunity for employment in many industries throughout Colorado, a higher salary, and the potential to step into senior positions, the choice to pursue a data science master’s degree could be a wise next step for any bachelor’s-prepared data professional.

Preparing for a Master’s Degree in Data Science in Colorado

You won’t get there without going through a program that fulfills certain fundamental requirements in math, statistics, and coding, though, as well as putting together several years of relevant work experience.

Undergraduate Degree and Masters Prerequisite Courses

Minimum qualifications for data science master’s programs usually include the following:

  • A bachelor’s degree in a data science related discipline such as statistics, applied math, computer science, or engineering
  • A minimum 3.0 GPA in bachelor’s coursework
  • GRE or GMAT with quantitative section scores in the 15th percentile

Prerequisite courses required by most programs include the following courses:

  • Statistics
  • Calculus I & II
  • Linear algebra
  • Programming languages

Relevant Personal and Work Experience

Most data science master’s programs require:

  • At least five years of technical work experience in data science (5-7 years is standard)
  • Experience in coding, statistics, database administration, and data analytics
  • Analytical reasoning ability demonstrated through work experience and GRE/GMAT scores
  • Experience with data structures, algorithms, and algorithm analysis
  • Knowledge of programming languages, especially Python and Java

In Colorado, data scientists may gain the required experience through many different entry level positions, including:

  • Data scientist at Market Force, a Colorado-based company which helps analyze other business’ processes and identify key areas for improvement. Data scientists at Market Force will mine data, analyze it, and provide a relevant summary of information.
  • Data scientist at Oracle Data Cloud in Colorado, a company which offers clients external data storage and resources as well as data solutions.

Data scientists may also work at local government offices and nonprofits as a way to gain the required experience.

Succeeding in Scoring Within the 85th Percentile of the GRE/GMAT Quantitative Sections

In order to be accepted into a data science master’s program, candidates must take either the GRE or the GMAT exam. Candidates must score in the 85th percentile in the quantitative section and are also expected to score highly in both the verbal and writing sections. You can prepare for either of these tests by taking practice exams.

Candidates can schedule Graduate Record Exam (GRE) practice exams through the official GRE website, as well as peruse free sample questions and prep guides. The GRE website also offers a guide on how to prepare for the quantitative section, which is especially critical for data science master’s program applicants. In the quantitative section, students will answer questions about mathematics, analyze graphs, and model and solve data problems. You will also be required to solve problems using algebra and geometry, so you better brush up on your basic math skills first.

The Graduate Management Admissions Test (GMAT) also has a quantitative section. The official GMAT website offers candidates test preparation materials and two free practice exams. Another GMAT practice exam is hosted by The Princeton Review. The GMAT quantitative section includes questions involving data analysis, word problems, numerical programs, and interpreting graphs.

Prepare for a Masters or Skip it Entirely by Attending a Data Science Bootcamp in Denver or Online

There’s another path in Colorado that can lead you to preparing for a master’s in data science, or possibly bypassing that route entirely and shortcutting your way to an entry-level job: data science bootcamps.

A data science bootcamp is just as rigorous as many formal college courses (although it does not offer college credit) – and it’s a whole lot faster. Many programs have you in and out in 9 months or less. They are arrayed at a variety of skill levels, so you can find everything from entry-level introductory programs to highly advanced expert courses.

They accomplish all this through an intensive curriculum and jam-packed schedule that will keep you hopping, working through primarily practical elements and tools of data science, including:

  • Statistical analysis in Python
  • Big Data storage in Hadoop
  • Tableau for data visualization

The programs are so popular that even universities are starting to provide them. One example in Colorado is the University of Denver Data Analytics Boot Camp. Here you will learn the in-demand skills needed to analyze Big Data and turn it into clear insights. An unusual part-time boot camp, the program runs for 24 weeks with evening and weekend courses, available on campus or through a virtual online classroom. It covers essential aspects of data analysis including introductions to statistics and quantitative techniques, the use of Python and Javascript scripting languages and ancillary stats libraries, and visualization techniques and tools like Tableau.

With solid career and portfolio support throughout the program and a dedicated career services team to help with interview preparation, you can step right into a data science job when you complete the course… or go on for your master’s with full confidence in your abilities.

Bridge Programs and Massive Open Online Course (MOOC) Options for Applicants Who Do Not Meet Admission Criteria

If you do not meet the essential skills-based admission requirements, you’ll find that most data science master’s programs offer candidates bridge programs that will cover required topics before the student starts core coursework in the program.

Bridge programs typically cover these topics:

  • Linear algebra
  • Data structures and algorithms
  • Programming languages such as R, JAVA, C++, Python

Bridge programs are about half the workload of regular master’s courses and students complete the courses at their own pace. Students enroll in bridge programs before beginning core master’s coursework, and will be required to complete the bridge programs before continuing in the program.

Massive Open Online Courses (MOOC) are online supplementary education materials for data scientists. MOOCs offer online problem modules, filmed lectures, and the opportunity to interact with professors, teaching assistants, and peers. These MOOC resources help data scientists to supplement their education outside of their master’s program and gain greater familiarity with programming languages and other relevant topics.

Earning a Master’s Degree in Data Science in Colorado

Colorado offers a handful of traditional in-state, on-campus options for data science master’s programs. However, more and more students prefer online options, with benefits that include the flexibility to schedule classes around your work life, and the fact that it opens up the field of options. Online programs offer fully accredited curriculum recognized by the US Department of Education and professional data science associations nationwide. Most data science programs require an immersion experience near the end of the program, a hands-on group project that will require even online students to visit campus.

Full time, part time, and accelerated options are available. Full time programs take 20 months on average, part-time programs can be completed in 32 months, and accelerated programs can be completed in as little as 12 months.

Students may choose from several different master’s program titles:

  • Master of Science in Data Science (MSDS)
  • Master of Information and Data Science (MIDS)
  • Master of Science in Applied Statistics, Data Mining Track
  • Graduate Certificate in Data Science
  • Data Mining and Applications Graduate Certificate
  • Curriculum and Core Coursework

Curriculum for data science master’s programs may vary, but all degrees will require a combination of the following topics:

  • Data Mining
  • Experimental Statistics
  • Data and Network Security
  • Visualization of Information
  • File Organization & Database Management
  • Statistical Sampling
  • Quantifying the World
  • New Approaches to Managerial Economics
  • Research Design and Application for Data and Analysis
  • Exploring and Analyzing Data
  • Applied Machine Learning
  • Legal, Policy and Ethical Considerations for Data Scientists
  • Applied Regression and Time Series Analysis

Key Competencies and Objectives

Data science master’s programs will teach you how to analyze data sets, quantify data, and succinctly derive insights from data analysis. Courses encompass a range of disciplines, including social sciences, computer science, statistics, management, and law.

Upon graduation, you will be expected to display competence in retrieving, organizing, and analyzing data, applying statistical analysis, designing visualizations, communicating important data points, and understanding federal requirements regarding data security.

Earning a master’s degree will give you more opportunities in the employment sphere, a higher rate of pay, and the opportunity to manage a team of other analysts.

Career Opportunities for Data Scientists in Colorado with Advanced Degrees

With a high demand for qualified data scientists spreading across virtually all industries in Colorado, data scientists with advanced degrees are integral to the success of many businesses. In 2020, tech hiring firm DICE identified data scientists and data engineers as the top two fastest growing occupations in information technology, showing plenty of runway ahead for data science careers.

The following job listings are shown as illustrative examples only and are not meant to represent job offers or provide any assurance of employment. These examples were taken from a survey of job vacancy announcements for data scientists in Colorado.

Director of Data Science at Anthem, Inc. in Denver, CO


  • Master’s in data science or a related field


  • Lead a team of data scientists in development and implementation of data solutions
  • Processing data streams in distributed computing environments
  • Lead real-time model scoring and oversee development of proprietary machine learning algorithms
  • Publish results and address constraints/limitations with business partners

Senior Data Scientist with Tetra Tech in Denver, CO


  • Masters or PhD in data science or related discipline
  • At least 7 years of experience in data science
  • Familiarity with Apache Spark, Apache Hadoop, and Java


  • Build proof of concepts for large analysis tasks
  • Deal with very large data sets for complex industrial projects
  • Design team workflows and write machine learning algorithms

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