Online Master's in Data Science for Jobs in Washington, DC

In DC, many different industries rely on data scientists to fill critical roles creating and managing data systems. In March 2012, the US government broadened career opportunity for data scientists when it announced a $200 million dollar initiative for Big Data Research and Development. This initiative impacted the Department of Defense, DARPA, the Department of Energy, and the U.S. Geological Survey. Today, qualified data scientists are highly sought after in these government offices, playing vital roles in safeguarding restricted information, improving defense systems, and preventing abuse within the systems.

In DC, data scientists are hired by firms such as WPA Opinion Research, which employs data scientists to work with political campaigns. With WPA, data scientists manage data sets relating to campaign information and write concise reports of findings. The Washington Post (TWP) also hires qualified data scientists in DC as part of their big data analytics team. Data scientists maintain TWP’s big data platform and build machine learning and statistical modeling systems to supplement the database. In addition to these opportunities, data scientists in DC may pursue employment within many different industries, including banking, finance and insurance; retail, manufacturing and marketing; and healthcare, biotechnology and the pharmaceutical industry.

As the field of data science expands across the US, more and more bachelor’s prepared professionals are seeking master’s degrees. Executive recruiting firm Burtch Works recorded that 88% of data scientists held master’s degrees in 2015, noting that “It’s incredibly rare for someone without an advanced degree to have the technical skills necessary to be a data scientist.” Pursuing a master’s degree in data science offers a broader scope of occupational opportunities, a significant salary increase, and the opportunity to manage data science teams.

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

Master’s degree candidates must display an excellent track record of educational experience and work history. Admissions departments seek well-rounded candidates and set highly selective admissions standards, looking for candidates with a high GPA in previous coursework, entrance exam scores in the 85th percentile or higher, and relevant work history in the field.

Undergraduate Degree and Master’s Prerequisite Courses

Minimum requirements to gain entrance to a data science master’s program include:

  • Minimum of 3.0 in previous coursework
  • Bachelor’s degree in a related field (statistics, computer science, engineering, applied math)

Applicants must have completed the following prerequisite courses:

  • Statistics
  • Calculus
  • Linear algebra
  • Programming languages, especially JAVA and Python

Admissions departments will also consider applicant’s work experience, GRE/GMAT exam scores, and knowledge of prerequisite concepts.

Universities seek out students who have already gained practical experience in the field of data science. Applicants will be expected to have prior experience with the following concepts:

  • Data structures
  • Algorithms and analysis of algorithms
  • Linear algebra

GRE/GMAT Exams

Data science master’s programs set highly selective admissions requirements, requiring applicants to have scored in the top 15 percent of either the GRE or GMAT’s quantitative section. In addition, admissions departments look at the applicant’s verbal and writing scores, placing a strong emphasis on candidates with excellent communication skills.

The GRE’s quantitative section will evaluate the candidate’s ability to perform data analysis. The section includes questions on statistics, probabilities, and Venn diagrams. Additional questions will cover arithmetic, algebra, and geometry.

The official GRE website offers preparation tips and two free practice tests. For additional preparation, students may enroll in practice exams hosted by the Princeton Review.

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), students may take practice exams hosted by The Princeton Review and Veritas Prep.

Prior Work Experience and Related Skills

All data science master’s programs require 5-7 years of prior work experience in a data science related field. Applicants will be expected to display proficiency in the following areas:

  • Programming languages, especially JAVA, Python, and C++
  • Coding
  • Hacking
  • Data Mining
  • Database Administration

In DC, applicants may gain the required experience through several different channels:

The CIA in Washington, DC hires entry level, bachelor’s-prepared professionals in data science. In the CIA, entry level data scientists work on a team a professionals, managing advanced software to develop algorithms and find patterns in large, unique, and secure datasets.

The Inter-American Development Bank (IDB) in Washington, DC hires bachelor’s-prepared data science professionals. IDB data scientists perform quantitative and qualitative analyses of data, create data tools to mine data, 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 work as part of a larger team. Data scientists create algorithms and models, standardize and clean data, and acquire and import data.

In addition, data scientists in DC may seek out employment in many local industries, including the nonprofit sector and local government offices, to gain required skills such as data mining, data analysis, and script writing.

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. Because of the diverse nature of the requirements, not all bachelor’s prepared candidates may meet each individual requirement. To combat this issue, most master’s programs offer bridge courses to allow entering students to gain the required skills before beginning master’s 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 learn required skills, such as additional programming languages, is to enroll in Massive Open Online Courses (MOOCs). MOOCs consist of online problem modules, filmed lectures, and the opportunity to interact with data science professors. MOOCs are hosted through many online sources, one of which is Class Central.

Earning a Master’s Degree in Data Science in DC

There are currently no traditional on-campus master’s programs in DC; however, several flexible online programs are available throughout the country. Online programs 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 online programs, require an immersion experience which will require students to visit campus. An immersion experience is a hands-on group project designed to prepare master’s students for the unique challenges of the data science workforce.

Data scientist students may 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 a variety 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 deriving insights from data sets. Students will become proficient in 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. 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.

Master’s programs offer a wealth of knowledge which will serve the data scientist in the work force, offer a higher rate of pay, and allow management positions over entry level data scientists.

Career Opportunities for Data Scientists in DC with Advanced Degrees

In DC, data science professionals with master’s degrees may seek employment in many different sectors. Many companies prefer to hire professionals with advanced degrees and offer a higher rate of pay to master’s certified data scientists.

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 in March 2016.

Data Scientist at The Washington Post in Washington, DC

Requirements:

  • 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

Responsibilities:

  • 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

Requirements:

  • Master’s degree in data science or a related field
  • 10 years of experience in a data science field

Responsibilities:

  • 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 ITC Concepts Inc. in Washington, DC

Requirements:

  • Master’s degree in data science or a related field
  • Minimum of 5 years of experience working with big data
  • TS/SCI security clearance

Responsibilities:

  • 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

Back to Top