Data scientists in Colorado are instrumental in almost every industry, from finance and insurance; to healthcare, biotech and the pharmaceutical industry; 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 job advancement opportunities and high earning potential. This number has increased in recent years, and as of 2014, executive recruiting firm Burtch Works reported that 88% of data scientists hold master’s degrees. According to Burtch Works, the median salary for a junior level data scientist is $91,000, but data scientists who manage a team of ten or more earn salaries of $250,000 or more.
In Colorado, data scientists have the opportunity to seek employment with Fortune 500 companies such as Level 3 Communications, Inc., a provider of telecommunication services. With Level 3 Communications, data scientists are responsible for designing and implementing programs which collect data from Level 3’s services as well as developing data reports and solutions for the business. Data scientists may also work in the banking and finance industry for companies such as McGraw Hill Financial. Data scientists at McGraw Hill collect data and analyze datasets by applying industry knowledge to analytical skills. Data scientists use coding and validation of data to provide solutions to company-wide issues.
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 in Colorado is the next step for many data science professionals.
Preparing for a Master’s Degree in Data Science in Colorado
Data science master’s programs require students to fulfill certain fundamental requirements such as math, hacking, and coding skills, as well as 5-7 years of relevant work experience.
Undergraduate Degree and Master’s Prerequisite Courses
Minimum qualifications for data science master’s programs usually include the following requirements:
- A bachelor’s degree in a data science related discipline such as statistics, applied math, computer science, or engineering
- A minimum of 3.0 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:
- 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, hacking, statistics, database administration, and data mining (as relevant to program)
- 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:
As a 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 relevant summary of information.
As a 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. Candidates may prepare 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 students. In the quantitative section, students will answer questions about mathematics, analyze graphs, and model and solve data problems. Students will also be required to solve problems using algebra and geometry.
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.
Bridge Programs and Massive Open Online Course (MOOC) Options for Applicants Who Do Not Meet Admission Criteria
If candidates do not meet each of the admission requirements, 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, citing benefits such as flexibility around a professional schedule and more program 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 with students in the program that will require 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 seek to teach students 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, students 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.
Master’s degrees help offer the assurance of more opportunities in the employment sphere, a higher rate of pay, and the opportunity to manage a team of other data scientists.
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 2015, the MIT Sloan Management Review survey reported that four in ten companies described a lack of analytical data skills as a key challenge in business processes, revealing the market for data scientists.
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 in February 2016.
Data Scientist at Gannett Peak Technical Services in Denver, CO
- Experience writing Python in an engineering environment
- Master’s degree or higher in data science or a related field
- Familiarity with relational databases
- Mining data from large datasets
- Writing code and overseeing incorporation of databases
- Working with a team of data scientists
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