Massive amounts of unorganized, seemingly disparate data have been giving Amazon the upper hand in online commerce for years… Bringing structure to travel trends and location 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 has been a legend in the software game for decades. Add to that the full spectrum of Silicon Valley giants that have set up shop here alongside the homegrown startups popping up like mushrooms after a good rain, and you have a scenario where the companies that everybody wants to work for are competing with each other for top talent. According to CompTIA, in 2020 Seattle had the fourth highest concentration of technology talent in the country– and it’s still not enough to meet demand.
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 Seattle get starting salary offers in the range of $134,000 to $229,000… and you can bet that holding a master’s is standard at the top end of that range.
Preparing for a Master’s Degree in Data Science in Washington
While a bachelor’s degree is still a workable route to entry-level employment in data science, a master’s degree 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 through a rigorous curriculum—one that demands only the most qualified students.
Admission requirements for master’s degree programs in data science are pretty stringent, and often 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 Can Prepare You For a Master’s or Entry-Level Employment
It’s possible you didn’t make all the right moves at the undergraduate level and ended up with a degree in something that was maybe not so quantitative, even though in your heart of hearts, you know you are destined to be a master’s-qualified data scientist. But what’s in your heart isn’t going to matter to a master’s admissions committee as much as what is on your diploma.
Well, you don’t have to start over entirely with a new bachelor’s degree to get into a master’s program. Instead, in only a few weeks or months, you can put together the kind of hands-on experience and data science knowledge that master’s admissions committees salivate over.
The bad news is that you’re going to have to sweat your way through an intensive data science bootcamp to get it.
Actually, bootcamps aren’t all that bad. Many are now available online and part-time, like these two that are available to Washington residents from major universities:
The work is definitely intensive, but it’s also extremely practical; you won’t get bored as you learn from experienced instructors who have real-world experience in areas like:
- AI and machine learning analysis
- Hadoop and NoSQL data stores
- R and Python programming
- Social media and big data mining
- Fundamentals of statistics
- Data visualization through Leaflet.js or D3.js
And you’ll do that learning in realistic projects undertaken on teams with your fellow students, often using live data and focusing on problems that you would find in actual jobs in the field. Those same projects will go into a portfolio that career services staff will help you shape into the ideal presentation of your new skills that you can present to potential employers or master’s admissions committees.
These entry-level programs can effectively fill all the gaps in your knowledge and experience, and get you back on track to get that master’s degree you have been dreaming of.
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 slightly smaller 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 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
- Pattern recognition
- Text analytics
Fortunately, there are many junior-level data scientist positions in the greater Seattle area and throughout Washington State that provide candidates with plenty of opportunities to get the kind of experience they need with just an undergraduate degree under their belts. Just a couple 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 improve their chances of success on these graduate exams:
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.
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
Universities offer their data science master’s programs under a number of different titles that might include:
- 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.
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
- Cleansing and munging data
- 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 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, 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
Responsibilities:
- 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
Requirements:
- 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
Responsibilities:
- 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
Requirements:
- 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, Expedia, Inc.: Seattle, WA
Responsibilities:
- 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
Requirements:
- 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