In 2016, the Obama administration doubled down on the Big Data Research and Development initiative going on in Washington, which had injected $200 million into federal data science infrastructure and development. Drawing in agencies as diverse as the Department of Defense, DARPA, the Department of Energy, and the U.S. Geological Survey, the initiative was a steroid injection into the skilled analytics professional job market in the DC-area that even years later is still contributing to job creation.
- SMU - Master of Science in Data Science - Bachelor's Degree 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
Today, qualified data scientists are still sought after in these government offices. Working with the kind of highly sensitive information kept in government data stores comes with an extra layer of responsibility for analysts, and roles that involve unique analytic applications for safeguarding restricted information, improving defense systems, and preventing abuse within the systems. That’s not the kind of work you’re likely to find anywhere else.
Outside of government, DC-area data scientists work with firms like WPA Intelligence, pulling useful insights out of constituent data to support political campaigns. The Washington Post (TWP) also has a crack team of data scientists using the paper’s massive database of published work for research into machine learning capabilities and to develop statistical modeling systems to better understand user behavior.
In everything from banking, finance and insurance, to retail, manufacturing and marketing, to healthcare, biotechnology and pharmaceutical R&D, Washington, DC is wide open when it comes to private sector jobs for skilled data professionals.
As the expectation for what data scientists can bring to these organizations ramps up, a bachelor’s degree in computer science, statistics or data analytics just doesn’t cut it anymore. Executive recruiting firm Burtch Works found in 2019 that less than 10% of data scientists hold only a bachelor’s degree. The bottom line is that going for your master’s will open up your employment and salary prospects, and give you the chance to contribute as a team lead in your department, and a recognized leader in the field overall.
Preparing for a Master’s Degree in Data Science in DC
Master’s programs in the DC area can be pretty selective and highly competitive. Admissions departments tend to look for well-rounded candidates with some relevant work history, a high GPA in previous coursework, and entrance exam scores in the 85th percentile.
Undergraduate Degree and Master’s Prerequisite Courses
Just earning a bachelor’s degree isn’t enough to meet the entrance requirements for most DC-area graduate schools… It’ll take a 3.0 undergrad GPA in a related field like statistics, computer science, engineering, or applied math, with the curriculum including some or all of the following prerequisite coursework:
- Linear algebra
- Programming languages, especially Java and Python
Admissions departments will also consider an applicant’s work experience, GRE/GMAT exam scores, and knowledge of formal concepts in statistical science and quantitative reasoning.
It’s also to your advantage to have some practical experience in some analytics role under your belt before you apply. Jobs that give you real-world exposure to the following concepts would do the trick:
- Data structures
- Algorithms and analysis of algorithms
- Linear algebra
Data science master’s programs set highly selective admissions requirements, requiring you to have scored in the 85th percentile of the quantitative section on either the GRE or GMAT. But admissions departments will be looking at the your verbal and writing scores too, giving strong preference to candidates with excellent communication skills – something that’s critical for conveying abstract mathematical findings in terms anybody can understand.
The GRE’s quantitative section includes questions on statistics, probabilities, and Venn diagrams. Other questions cover algebra, and geometry.
The GMAT’s quantitative section is designed to measure the student’s ability to analyze data and draw conclusions. Questions cover a range of topics and consist of word problems, numerical problems, and graph analysis. To prepare for the Graduate Management Admission Test (GMAT), you can take practice exams hosted by The Princeton Review and Veritas Prep.
Prior Work Experience and Related Skills
Most data science master’s programs require 5-7 years of prior work experience in a field related to analytics. Applicants will be expected to display proficiency in the following real-world areas:
- Programming languages, especially JAVA, Python, and C++
- Data Mining
- Database Administration
In DC, applicants may gain the required experience through a few different paths that you simply won’t find anywhere else:
- With the CIA, entry-level data scientists work to develop algorithms and find patterns in large, unique, and secure datasets.
- The Inter-American Development Bank (IDB) in Washington retains data scientists to perform quantitative and qualitative analyses of data, create specialized tools, write scripts, create data visualizations, and prepare business reviews as needed.
- Crowdskout, a data science firm in Washington, DC, hires entry-level data scientists to create algorithms and models, standardize and clean data, and acquire and import data.
- There is also no shortage of opportunities in the nonprofit sector and local government offices.
Enroll in a Data Science Bootcamp in Washington DC or Online to Prepare for a Master’s Program or Go Straight into an Entry-Level Job
Another path to earning your spurs for a master’s program is a relatively new one, called a data science bootcamp. Bootcamps are every bit as intense as the name suggests, putting you through an intensive, short-term, practical course of study that will get you hands-on with cutting-edge techniques and technologies and real-world datasets.
Bootcamps don’t offer college credit and they are just as often used to move straight into employment as they are for gearing up for a master’s degree. Although in the past they have primarily been run by private, for-profit organizations, major colleges are starting to offer them as an option. A prominent example in Washington DC is the George Washington University Data Analytics Boot Camp in Arlington, and also available online.
Bootcamps can be oriented at different types of skills at every level, but the GWU camp is aimed squarely at professionals who are just cutting their teeth in the field and who need a basic grounding in the principals of statistical modeling and forecasting, along with training in the essential tools of the trade.
You only need a GED and to be 18 to apply, although 2 years of work experience and a bachelor’s are preferred. This program puts you straight into the deep end with courses in subject such as:
- Python and Python stats libraries like NumPy and Pandas
- R language programming
- Machine learning and big data analytics with Hadoop
In only 24 weeks of part-time, evening and weekend study, offered both on-campus and online, GWU gives you the grounding that you need to get into a prominent data science master’s degree program, either at GWU or another university. Or, if you prefer, the camp also has a superlative set of career services professionals on tap to help you with portfolio building and resume polishing, ready to get you started directly in an entry-level job in data science after graduation.
Bridge Programs and Massive Open Online Course (MOOC) Options for Applicants Who Do Not Meet Admission Criteria
All data science master’s programs require a strong skill set gained through prior education and work experience. If a bootcamp isn’t your preferred path to those skills, there are others. Because of the diverse nature of the requirements, not all bachelor’s prepared candidates will meet all the grad school admission requirements at area universities without a little prep work. So most master’s programs offer bridge courses to allow students who otherwise have the background to enter the program to gain the required skills before beginning master’s-level coursework.
Most universities offer two types of bridge programs:
- Fundamental (including linear algebra, algorithms and analysis of algorithms, and data structures)
- Programming (including languages such as Python, JAVA, C++)
Another option for data science master’s students who need to polish-up on a programming language or other skills is to enroll in Massive Open Online Courses (MOOCs) independent of the graduate school. MOOCs consist of online problem modules, filmed lectures, and the opportunity to interact with data science professors. MOOCs are offered by many major universities and private companies such as Udemy and Coursera, and sometimes in partnership between the two.
Earning a Master’s Degree in Data Science in DC
There are a handful of traditional on-campus master’s programs offered in Washington DC, but your horizons really open up when you start considering online programs that are available from anywhere in the country. Online programs generally offer fully accredited curriculum in three different program tracks:
- Full time (18 months)
- Part time (32 months)
- Accelerated (12 months)
Most data science master’s programs, even the ones offered online, require an immersion experience that requires students to visit campus at least a couple of times a year. An immersion experience is a hands-on group project designed to prepare master’s students for the unique challenges they can expect to encounter every day once they’re on the job.
Data scientist students can choose from several different program titles:
- Master of Science in Data Science
- Master of Information and Data Science
- Graduate Certificate in Data Science
- Data Mining Graduate Certificate
- Data Science Graduate Certificate
- Curriculum and Core Coursework
All data science master’s programs will be constructed around core concepts, though individual course names and material may vary. Core coursework will include some variation of the following topics:
- Applied regression and time series analysis
- Database management
- Advanced managerial economics
- Network and data security
- Data visualization
- Data storage and retrieval
- Quantifying materials
- Ethics and law for data science
- Experimental statistics
- Statistical sampling
- Machine learning and artificial intelligence
- Data research design and applications
- Scaling data – macro and micro
- Data mining
- Information visualization
- Experiments and causal inference
Key Competencies and Objectives
After completing a master’s degree in data science, students can expect to be proficient in several key disciplines, including using analytical methods to quantify data and derive actionable insights from it. You’ll have a firm grasp of the essentials of database management and file organization, and hold a strong understanding of network security.
Students will become proficient in current programming languages used by companies nationwide, like R and Python. Graduates will be able to provide statistical sampling, research design, data visualization, and display an understanding of historic and current laws regarding data security and the ethics of shared information.
Career Opportunities for Data Scientists in DC with Advanced Degrees
Many companies prefer to hire professionals with advanced degrees and, and are glad to pay handsomely. Mid-level professionals in the Northeast region of the U.S. working in data science earned a median annual salary of $135,000 according to Burtch Works, for example.
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 DC.
Data Scientist at The Washington Post in Washington, DC
- Master’s degree in data science or a related field
- 5-7 years of experience in related position
- Prior experience working with big data and scripting tools
- Work on a big data project team and deliver creative solutions to business problems
- Evaluate techniques in machine learning, data mining, and analytics
- Build algorithms, test hypotheses, and generate insights from data sets
Data Scientist/Analyst with Unisys IT in Washington, DC
- Master’s degree in data science or a related field
- 10 years of experience in a data science field
- Assemble program data on the Tableau software interface
- Conduct data analysis and define data sources
- Create and maintain dashboards for data
- Gather, extract, manipulate, and model data within Tableau
Data Scientist with IT Concepts Inc. in Washington, DC
- Master’s degree in data science or a related field
- Minimum of 5 years of experience working with big data
- TS/SCI security clearance
- Perform targeted data analyses, test hypotheses, prepare historical data, identify patterns
- Identify and oversee applications of data mining and analyses
- Cluster user generated content and work with large datasets