Portlandia might have made us famous for being the place where hipsters go to retire, but we’ve gotten a lot of press for the work being done by the tragically unhip inhabitants of the Silicon Forest too: the gushy Meet the New Silicon Valley piece in the Huffington Post… Bloomberg Business reporting on how the tech workforce in Multnomah County grew 82% in the six-year period between 2010 and 2016… and then another 10% in the years since according to a report from the Columbia-Willamette Workforce Collaborative.That same report identifies 40% of data scientists in the region as being employed in the tech sector.
- Grand Canyon University - B.S. in Business Information Systems and M.S. in Data Science
- SMU - Master of Science in Data Science - No GRE Required.
- Syracuse University - M.S. in Applied Data Science: GRE Waivers available
- UC Berkeley - Master of Information and Data Science Online - Bachelor's Degree Required.
- Syracuse University - Master of Information Management Online
With a year-over-year growth rate of almost 4 percent, the region outstrips U.S. job growth and nationwide tech job growth rates completely.
Part of the reason for the explosion is the talent that’s on tap here, the livability of the city, and its comparatively low rents, drawing in all the Silicon Valley big boys – Facebook, Apple, Twitter, Google – to open up offices or at least do a little prospecting. And even without them, we’ve got hundreds of local start-ups like Digimarc and Puppet that are bringing together the best minds in the business.
While Portland gets all the attention, there’s a lot going on in Eugene too, with more than 400 tech companies calling the Eugene-Springfield area home.
Data scientists with graduate degrees are in high demand across the state… and companies are more than willing to pay the high salaries they command.
Preparing for a Master’s Degree in Data Science in Oregon
If you are in search of those six-figure compensation packages, you’d better start planning your education early. You’ve got a lot of competition to get into a relatively small number of data science graduate program slots, and everyone else has their eyes on the prize, too. That means you need to be taking coursework related to data science at the undergraduate level, and beefing up your portfolio of experience long before you apply to a master’s program. Taking these steps will greatly improve their chances of being accepted into a master’s program.
Undergraduate Degree and Master’s Prerequisite Courses
Prospective graduate students are generally expected to meet the following standards to be accepted to a data science program:
- Hold a bachelor’s degree in a quantitative field such as statistics, computer science, applied math, or engineering
- Demonstrate coursework in such key disciplines as statistics, linear algebra, calculus I and II, programming languages, and quantitative methods
- Earn a minimum GPA of 3.0
Gaining Relevant Personal and Work Experience
Typically, graduate schools seek applicants with highly relevant professional experience on top of solid academic preparation. Demonstrating you can walk the walk involves:
- At least five years of technical work experience that ideally should demonstrate quantitative skills
- Personal experience related to programming, database administration, data mining, coding, hacking, mathematics, or statistics
- Strong communication skills
Preparing to Score Within the 85th Percentile on the Quantitative Sections of the GRE/GMAT Exams
Doing well on the GRE and/or GMAT exams is an excellent way for an applicant to demonstrate your proficiency in key data science areas. Scores in the 85th percentile will do nicely, if you can manage it.
Since practice makes perfect, advanced preparation is essential to score well on these standardized exams. Both students who have taken them and the testing companies themselves recommend taking practice tests on sample math problems until they become second nature.
The GRE has several sections, and the quantitative one is particularly important, since it evaluates the candidate’s skills in algebra, geometry, data analysis, and arithmetic. You’ll want to pay particular attention to such statistical processes as standard deviations and probabilities. Test takers can find sample questions and free practice exams at the official GRE website.
The General Management Admissions Test (GMAT) similarly evaluates your quantitative, writing, and verbal abilities. Graduate school admissions departments expect high scores in all of these areas, but data science programs will pay particular attention to the 37 questions that assess problem solving and data efficiency. Candidates can take GMAT practice exams through Veritas Prep and The Princeton Review.
Data Science Boot Camps in Portland and Online Can Help You Meet Master’s Program Entry Requirements and Prepare for a Job
There are more paths opening to the world of data science all the time, and one of the latest is through data science boot camps.
Boot camps are as tough and relentless as they sound, but for data science, at least you don’t have to do any twenty-mile ruck marches. Instead, you’ll spend your time crunching data and being taught how to perform common scrubbing, analytical, and interpretive tasks using some of the most common tools in use in the field today. Boot camps don’t dwell on theory, but home in directly on the practical applications of data science, which make them a perfect, fast-paced introduction to the field that you can get through in just a few months in most cases.
Once offered almost exclusively by independent providers that most people never heard of, today major colleges like the University of Oregon are enlisting grunts into bootcamps of their own. The University of Oregon Data Analytics Boot Camp will teach you basic data science skills in 24 weeks of intense part-time study, either at their Portland campus or online through a virtual classroom experience.
That experience includes topics like:
- HTML5/CSS visualization tools
- SQL and NoSQL databases
- Machine learning
Those are some weighty topics so they’d like you to have a bachelor’s degree and a couple years experience, but at the end of the day it is an entry-level program so they’ll take you even if you don’t. More advanced boot camps cater to higher skill levels and have more rigorous requirements.
The UO camp will get your graduate program application up to par with the skills you need to start advanced courses, and at the same time prepare you to transition directly to the workforce. The UO program has career planning assistance that includes a dedicated profile coach and a career director who stick with you throughout the program, ensuring you have plenty of offers waiting for you by the time you graduate.
Bridging Gaps in Knowledge Through MOOCs or Bridge Courses
Since data science is relatively new, it’s likely you’ll still have a few gaps in your academic knowledge. But there are ways to fill those in along the way, either on your own, or with the assistance of the grad school itself.
Massive Open Online Courses (MOOCs) – If you happen to know what your knowledge gaps are, and don’t have too many of them, then you may be able to fill them through Massive Open Online Courses (MOOCs). These courses are educational programs that are basically just really big, accessible versions of subject-specific college courses. In fact, many are offered by big-name colleges, sometimes in cooperation with private providers. You get to pick and choose your topics, and the costs are low, so it’s a straightforward path to prepare yourself—if you have the self-discipline to get through.
Bridge Courses – Another option to supplement skills that a you may lack is to take part in bridge programs offered by the graduate school itself. Recognizing that many incoming candidates will have a few pieces missing coming in, schools open up their own undergraduate courses on a case-by-case basis to cover some of the most common deficiencies that you might have. Two types of bridge programs are generally available:
- Fundamental bridge programs – courses in linear algebra, data structures, along with algorithms and their analysis
- Programming bridge programs – training in such essential programming languages as C++, JAVA, and Python
Earning a Master’s Degree in Data Science in Oregon
To earn a Master of Science in Data Science, prospective students in Oregon can select from respected online programs that are available throughout the country, or some of the excellent on-site degrees that state schools have put together. The online programs offer a great deal of flexibility for working professionals, since options for study range from 12-month accelerated programs to 32-month part-time programs. Standard full-time online master’s programs generally take 18 months to complete.
While the initial coursework is conducted entirely online, most data science master’s programs require that students study on campus during their final semester. Such immersion experiences combine intensive coursework with the opportunity to interact with professors and peers.
Degree programs available include:
- Master of Information and Data Science (MIDS)
- Master of Science (MS) in Data Science
- Master of Science in Data Science (MSDS)
- Graduate Certificate in Data Science
- Data Mining and Application Graduate Certificate
Core Curriculum Content
While the specific course requirements vary in different master’s programs, the core courses will cover essential skills required for data science positions. All programs will cover these topics:
- Ethics and law for data science
- Network and data security
- Experiments and casual inference
- Applied regression and time series analysis
- Machine learning and artificial intelligence
- Data mining
- Data research design and applications
Most online programs offer students the chance to apply their training to real-world problems during the immersion experience. Students have the opportunity to work together in small teams and spearhead a data science project.
Key Competencies and Objectives
Graduates of data science programs acquire a wide range of proficiencies in core areas and have the skills to work in a number of such areas:
- Data cleaning and munging
- Database management and file organization
- Data and network security
- Data collection techniques
- Statistical sampling
- Programming languages such as Python and C++
Career Opportunities for Oregon Data Scientists with Master’s Degrees
Data science master’s grads go on to work at the Portland offices of iconic tech companies like Google, Apple, and Facebook. Homegrown talent in the city includes Puppet Labs, Urban Airship, and New Relic.
Additional data science employers in Portland range from Nike, which uses big data to fine-tune data driven strategies, to Vesta—the data driven pioneer in enabling secure electronic payments.
While the tech spotlight in Oregon is currently on Portland, opportunities for careers in high-profile data science abound in other parts of the state. For instance, Intel’s facility in Hillsboro includes a large data science team working to develop next-generation wearable sensor applications.
Shown below are representative listings for data science jobs in Oregon. These listings are informational only and do not represent an assurance of employment or current job offers.
Data Scientist with HealthSparq in Portland – Data scientists here use machine learning and advanced statistical analyses to design, develop, and implement data-driven solutions. Candidates must understand advanced analytics and have practical experience applying natural language processing, data mining methodologies, and machine learning algorithms. HealthSparq also requires that its data scientists have experience with tools such as SAS, R, Matlab, Scala, Python, or Ruby and can create complex SQL queries.
Applicants with a Master’s or PhD in a strongly quantitative field such as applied mathematics, statistics, operations research, physics, computer science, or econometrics are preferred. In addition, applicants must have 3 years of related work experience or equivalent combination of education and experience.
Analytic Scientist/Data Scientist with Vesta in Tigard – Data scientists here do data-driven risk management. Applicants must have a strong knowledge of state-of-the-art algorithms in machine learning, graph analysis, and statistical modeling.
The position has a strong requirement for SQL programming and data warehousing skills along with experience in at least one programming language. Candidates much be familiar with open source Python-based ML libraries and cloud computing (AWS). Applicants must have at least a master’s degree in computer science, applied math, or computer science with a year of machine learning/data mining.
Machine Learning Data Scientist with Intel in Hillsboro – Applicants must have expertise in machine learning and Big Data technologies such as Hadoop, Lucene/Solr, or HBase, analytics packages such as Mahout, R, Matlab, Weka, or Octave, scripting languages such as Python or Perl, and programming languages such as SQL, C/C++, or Java.
Intel typically requires that candidates have an advanced degree in mathematics, computer science, statistics, operations research, machine learning, or equivalent expertise. Applicants with a master’s degree must have at least three years of post-graduate industry experience.
Lead Hadoop Data Engineer Consumer Digital Technology with Nike in Portland – This position involves building a next generation analytics platform in the cloud using emerging and mature Big Data technology and tools. Applicants must have hands-on development experience with Hadoop and tools like Sqoop, Hive, Impala, Pig, and Spark. Excellent scripting skills in one or more languages is an additional requirement along with strong experience with SQL and Relational databases. Candidates must also be familiar with a variety of data formats and protocols such as AVRO, RC, and JSON.
Nike requires a master’s or bachelor’s degree in computer science or a related field along with 7+ years of experience in large-scale software development and 4-5 years of experience with high volume data processing and data analytics.