Online Master's in Data Science for Jobs in Oregon

The Portland area received a lot of press in recent years with such articles as Meet the New Silicon Valley in the Huffington Post. In fact, in 2016, Bloomberg Business reported statistics from market researcher EMSI indicating that the tech workforce in Multnomah County grew 82% since 2010. This far exceeded the national rate of 29%.

According to the commercial real estate firm CBRE, the tech talent pool in the Portland market grew by 28% between 2010 and 2013—a higher rate than that of such vaunted tech areas as Austin and Silicon Valley.

Part of the reason for the explosion of tech and data science jobs in Portland is the talent available, the livability of the city, and its low rents. Office space in Portland rents for much less than in San Francisco, and major Silicon Valley entities such as Facebook, Apple, and Twitter are opening offices in Portland. Google greatly expanded its prominence in Portland in 2015 according to the Portland Business Journal.

While Portland gets the major press, demand for data scientists exists throughout Oregon. The Portland Business Journal described how Eugene’s tech scene exploded during the six-month period preceding October 2015. More than 400 tech companies call the Eugene-Springfield area home, and the identity management platform SheerID raised $5.3 million that year.

With the increasing prominence of the technology industry in Oregon, data scientists with graduate degrees are in high demand in the state. Thus, it is an excellent time to take advantage of Oregon’s educational opportunities and obtain a master’s degree in data science.

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

Students who plan on a future in data science would be well advised to begin preparations for a master’s degree as they work towards obtaining their Bachelor’s degree. Such preparations range from taking coursework related to data science to obtaining relevant work experience. Taking these steps will greatly improve their chances of being accepted into a master’s degree program in data science.

Undergraduate Degree and Master’s Prerequisite Courses

Prospective graduate students should ensure that they meet the following standards to apply to a data science program:

  • Possess a Bachelor’s of Science 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:

  • 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

Oregon provides a number of options for jobs that may satisfy the requirements for experience. Some of these positions include:

  • Data Scientist II with Early Warning Services in Austin
  • Sr. Software Engineer (Data Scientist) with Puppet Labs in Portland
  • Associate Data Scientist with ComScore in Portland
  • Business Intelligence Data Architect with Salem Hospital in Salem

It is crucial to excel as an employee, so that the employer can provide the stellar letters of recommendation to indicate that the candidate is qualified to be accepted into a Master’s degree program.

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 his or her proficiency in key data science areas. Scores in the 85th percentile will do so.

Advanced preparation is essential to score well on these standardized exams. Both students who have taken them and the testing companies advise 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. Candidates who aspire to be a data scientist must 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) evaluates a candidate’s 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®.

Bridgin Gaps in Knowledge Through MOOCs or Bridge Courses

Massive Open Online Courses (MOOCs) – If a candidate lacks particular skills, he or she has the option of gaining this competency by taking Massive Open Online Courses (MOOCs). These courses are educational programs that are hosted online with the intention of supplementing the education required to become a data scientists. While many online hosts offer MOOCs, Class Central is a course that is particularly well suited for data scientists.

Bridge Courses – Another option to supplement skills that a candidate may lack is to take part in bridge programs offered by many graduate schools. Two types of bridge programs are 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. These 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:

  • Quantifying materials
  • Advanced managerial economics
  • Ethics and law for data science
  • Network and data security
  • Experiments and casual inference
  • Statistical sampling
  • Experimental statistics
  • Applied regression and time series analysis
  • Machine learning and artificial intelligence
  • Data storage and retrieval
  • Data mining
  • Data research design and applications
  • Scaling data – macro and micro
  • File organization and database management
  • Information visualization

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 cleansing
  • Database management and file organization
  • Data mining and machine learning
  • Data and network security
  • Data collection and analysis
  • Statistical sampling
  • Research design
  • Communication and visualization
  • Programming languages such as Python and C++
  • Ethics, privacy, and relevant law

Career Opportunities for Oregon Data Scientists with Master’s Degrees

Graduates of data science Master’s programs in Oregon have a wide variety of choices for employment. Potential options include such iconic tech companies as Google, Apple, and Facebook who all have offices in Portland. Homegrown talent in the city includes Puppet Labs, Simple, and Elemental Technologies.

Additional data science employers in Portland range from Nike which utilizes big data and Hadoop to provide consumer insights and 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 in jobs in Oregon that were advertised in March 2016. These listings are informational only and should not be construed as an assurance of employment or current job offers.

Data Scientist with HealthSparq in Portland – Data scientists with this company 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, MATALB, 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 – The data scientist will join a risk management team for this Big Data-driven technology company. 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 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, Big Data technologies such as Hadoop, Lucene/Soir, 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 for this position 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 such as 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.

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