Online Master's in Data Science for Jobs in Washington

The consulting firm McKinsey and Company released the findings of a research study in March 2015 showing that Washington State ranked second in the nation for big data-related jobs.

Massive amounts of unorganized, seemingly disparate data serve as the impetus for new ways for Amazon to break records in online commerce. Bringing structure to formless data has helped make Expedia the world’s premier travel booking site. Businesses and governments alike turn to Algorithmia as a clearinghouse for task-specific algorithms that may lead to all new discoveries in everything from pharmaceutical development to environmental monitoring.

Seattle’s historically strong software engineering market, the many Silicon Valley tech companies putting down roots in the region, and the high-tech startups that call the greater Seattle area home combine to create a scenario where the companies everybody wants to work for compete for top talent.

Although big data encompasses a vast array of jobs, data scientists are undoubtedly among the most sought after. These technically and creatively proficient professionals bring the right skills and mindset to put recent breakthroughs in technology to good use working for Seattle’s smartest companies.

And their value is undeniable. According to tech industry staffing firm Robert Half Technology, data scientists in the Seattle, Bellevue, Tacoma area are offered starting salaries in the range of $130,000-$183,000—the highest in the nation.

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

While a bachelor’s degree is still an acceptable route to entry-level employment in the data science profession, the master’s degree in data science has become the widely accepted standard for those with their sights set on senior-level positions. Graduate-level data science programs prepare tomorrow’s leaders in data science through a rigorous curriculum—one that demands only the most qualified students.

Admission requirements for master’s degree programs in data science include:

  • Undergraduate degree in a related discipline (or the completion of specific undergraduate prerequisites) with a competitive GPA
  • Competitive GRE/GMAT scores in the quantitative reasoning section
  • Previous experience in a quantitative discipline
  • Admissions essay/interview
  • Professional letters of recommendation

Undergraduate Degree and Master’s Prerequisite Courses

Most students entering a master’s degree program in data science have an undergraduate degree in a quantitative discipline like:

  • Applied mathematics
  • Engineering
  • Operations research
  • Computer science
  • Statistics
  • Physics

When considering candidates for program admission, most graduate schools look not only for competitive undergraduate GPAs, but competitive GPAs in specific undergraduate courses like:

  • Programming languages (C++, Python, JAVA, R)
  • Linear algebra
  • Data structures
  • Algorithms and algorithm analysis

While some institutions require candidates to hold an undergraduate degree in a particular major, others only stipulate that candidates be able to show proof of having completed the necessary undergraduate courses.

Online Data Science Bootcamps to Get You Job-Ready or to Prepare for a Master’s Program

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Filling Gaps in Functional Knowledge Through Bridge Courses or Massive Open Online Courses (MOOCs) 

Students applying to a master’s program in data science with gaps in functional knowledge related to math and statistics or programming and computer science still have the opportunity to qualify for the rigors of a master’s program before beginning graduate work.

Bridge Courses are designed to accommodate candidates that are highly desirable and meet most entrance requirements, but that may have some gaps in functional knowledge. Bridge courses are offered directly through the school providing the master’s program in data science and are typically structured as either fundamental bridge courses (programming languages to include SAS, R, Python, C++ and Java) or mathematics bridge courses (math concepts related to algorithm analysis, statistical methods and linear algebra). Bridge courses allow newly enrolled students to become proficient in these areas before making the transition to master’s-level coursework.

Massive Open Online Courses (MOOCs) are available as online courses completely independent of the master’s program. Offered through a variety of providers, MOOCs provide students with the convenience and flexibility of a completely online format and the freedom to become proficient in the following areas before even applying to a graduate program:

  • Machine learning
  • Linear algebra, statistical methods and algorithm analysis
  • Database administration
  • programming languages

Relevant Personal and Work Experience

A related bachelor’s degree and an impressive undergraduate GPA may be an important component for admission into a master’s degree in data science, but relevant work experience is arguably just as important.

Many colleges and universities search for candidates with experience in areas such as:

  • Data analytics
  • Data mining
  • Pattern recognition
  • Text analytics

Fortunately, there are many junior-level data scientist positions in Washington State that provide candidates with plenty of opportunities for earning the necessary experience. Examples include:

  • Data analytics position with Amazon’s Marketplace team
  • Programming position contributing to Microsoft’s Cloud Computing Platform

Preparing for Success on the GRE/GMAT

Colleges and universities often turn to GRE/GMAT scores when determining candidate eligibility, particularly for candidates without much work experience. Schools tend to prefer candidates who have scored in the 85th percentile in the quantitative section of these exams.

Adequate preparation allows data science master’s programs candidates to improve their chances of success on these graduate exams:

GRE Revised General Test

The GRE Revised General Test’s Quantitative Reasoning Section assesses a candidate’s:

  • Basic Mathematical Skills
  • Understanding of elementary mathematical concepts
  • Ability to reason quantitatively and to model and solve problems with quantitative methods

The Math Review document provides a detailed overview of the exam’s quantitative section.

GMAT Examination

The quantitative reasoning section of the GMAT measures a candidate’s ability to analyze data and draw conclusions using reasoning skills.

Study tools, sample questions, and videos help test takers prepare for the exam.

Earning a Master’s Degree in Data Science in Washington

Colleges and universities may title their data science master’s programs a number of different ways:

  • Master of Computational Data Science
  • Master of Information and Data Science (MIDS)
  • Master of Science in Statistics: Data Science
  • Master of Science in Data Science (MSDS)

In addition to offering master’s degrees in data science in a traditional, full-time format, which takes about 18 to 24 months to complete, some schools offer both part-time and accelerated formats. Part-time master’s degrees in data science take about 32 months to complete, while some accelerated programs take as little as 12 months to complete.

Online master’s degrees in data science have also become a regular offering among colleges and universities. Online programs allow students to complete the majority of their curriculum through web-based study, including self-paced lectures and interactive case studies. Some institutions require students to attend an on-campus immersion experience, which provides an opportunity for collaboration and networking with peers, professors, and local companies.

Curriculum and Core Coursework

A master’s degree in data science includes about 30 credits of coursework in topics that cover:

  • Ethics and privacy
  • Research design
  • Communicating results
  • Cleansing
  • Mining and exploring
  • Data visualization
  • Applied machine learning
  • Data mining
  • Experimental statistics
  • File organization and data management

Program Competencies and Objectives

A master’s degree prepares tomorrow’s data scientists to analyze and manage large data sets and recognize strategies for solving real world problems.

A project-based, interdisciplinary curriculum prepares students to retrieve, visualize, and analyze data, interpret the results, and communicate findings.

Graduates are able to understand the legal and ethical considerations associated with data privacy and security, apply statistical analyses and machine learning techniques as to identify patterns and make predictions, and apply creative methods to everything from the initial inquiry to the interpretation of results.

Career Opportunities for Data Scientists in Washington with Advanced Degrees

The following job descriptions (sourced in March 2016), although not a guarantee or assurance of employment, provide some insights into the types of professional opportunities available to data scientists with advanced degrees in Washington:

Data Scientist-Traffic, Redfin: Seattle, WA


  • Understand and improve key digital conversion funnels
  • Work with terabytes of clickstream data and the most robust real estate data sets available
  • Automate manual processes, guide A/B test design, and analyze results
  • Promote self-service analytics through Tableau


  • At least three years of on-the-job experience
  • Master’s in predictive analysis, mathematics, statistics, operations research, computer science engineering, or one of the hard sciences
  • Expertise with statistical analysis software
  • Expertise with at least one programming language
  • Expertise with data extraction

Data Scientist, Nordstrom: Seattle, WA


  • Engage broadly with the business to frame, structure, and prioritize business problems
  • Communicate insights and recommend areas for further data discovery
  • Design optimization algorithms, develop and deploy new analytical tools
  • Leverage large-scale, multiple data sources and structures
  • Analyze large, complex data sets
  • Design and oversee design of experiments for marketing tests


  • Master’s or PhD in statistics, applied math, operations research, economics, or a related quantitative discipline, with experience
  • At least 5 years of experience designing experiments, extracting, manipulating, and analyzing data
  • At least 5 years of hands-on experience implementing large-scale data analytic solutions
  • Fluency with advanced statistical and machine learning techniques
  • Programming skills in the Hadoop environment

Data Scientist/Senior Data Scientist, Xerox: Redmond, WA


  • Understand the business problem and context and relate those to data sources
  • Define the problem and select appropriate algorithms/techniques based on business, computational and accuracy requirement
  • Perform elements of data cleaning, feature selection, and organize experiments in conjunction with best practices
  • Develop algorithms and build data model sets for use in applications and process improvements


  • Master’s or higher in data mining or machine learning; or the equivalent practical analytics/modeling experience
  • Background in healthcare analytics preferred
  • Experience in open source coding and communities
  • Proven track record of analyzing data and bringing value across a range of projects

Data Scientist, Expedia, Inc.: Bellevue, WA


  • Apply advanced analytic techniques, including machine learning, data mining, and statistical modeling
  • Develop analytical insights through assumption testing, sensitivity analysis, and perform complex data analysis
  • Leverage large-scale, multiple data sources and structures
  • Analyze large, complex data sets
  • Work with technology and product teams for model release
  • Communicate insights and recommend areas for further data discovery


  • Master’s in computer science, finance, statistics, or a related field
  • At least 2-3 years in the industry
  • Knowledge in cloud platform solutions
  • Ability to leverage data and models for creating impactful change
  • Ability to write clean and concise code in Python, R, or equivalent languages
  • Solid understanding of statistics

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