Online Master's in Data Science for Jobs in New York

It’s only natural that the center of global finance has a soft spot for master’s-prepared data scientists. And it sort of goes without saying that the planet’s most powerful investment banks and financial services firms are more than ready to tap their nearly bottomless resources to pay top dollar incentives in a game where even the slightest edge is worth tens of millions. According to Burtch Works, a technical recruiting firm, as of 2020, almost a quarter of predictive analytics professionals in the U.S. were employed in the financial services sector. We’re not surprised.

But the other three quarters need to find a niche too.

Business and government have at least one thing in common – an unwieldy stream of unorganized and unstructured data to deal with that comes pouring in by the minute. There is a veritable gold mine of to be found in all those messy numbers, revealing valuable insights that can inform strategic decision-making and trim costs to the tune of billions.

The NYC Mayor’s Office of Data Analytics (MODA) uses the power of big data to improve critical services, empower frontline workers, save money and, most importantly, save lives. Nearly a decade on and the program is still going strong as a central part of optimizing government, influencing everything from emergency response times to economic development to tax enforcement to public education.

Industry and government are hungry for the kind of cost savings and revenue bumps that come from prizing actionable insights from the data they’ve gotten so good at collecting, and they’re glad to pay top dollar as they compete to recruit data science talent.

Preparing for a Master’s Degree in Data Science in New York

Not just everyone gets into these highly selective master’s programs, though. You’ll need to be prepared to prove your ability to succeed in this highly technical discipline with the right kind of undergraduate degree and previous experience. In fact, more often than not, you’ll need a combination of the two.

Undergraduate Degree and Masters Prerequisite Courses

One of the most important qualifications students interested in graduate work in data science need is a bachelor’s in computer science, operations research, engineering, applied mathematics, statistics, or another math-heavy field.

Failing that test may not be the end of the line, however. Some schools will just look at the specific undergraduate courses you took and aren’t as concerned with the degree title as long as your combined transcripts show:

  • Programming languages (C++, Python, JAVA, R)
  • Linear algebra
  • Data structures
  • Algorithms and algorithm analysis

Other requirements for entering a master’s degree in data science program often include:

  • Minimum undergraduate GPA level
  • Admissions interview
  • Letters of recommendation
  • Minimum GRE/GMAT scores in quantitative reasoning

Data Science Boot Camps in New York City or Online Can Meet Masters Degree Prerequisites While Qualifying You for Entry-Level Work

If the requirements seem daunting, or you have already earned your bachelor’s and failed to hit the minimums, it’s reassuring to know that you have other options for meeting the stringent qualifications.

One of those is a relatively new one, the data science boot camp.

As the name implies, boot camps are strict, accelerated, no-nonsense courses in the fundamentals of data science. They skip the theoretical underpinnings of the field and focus strictly on the cutting-edge skills employers look for, running you through practical exercises using the latest tools and methods.

They may be aimed at a variety of skill levels, from entry-level pre-degree options for those new to the field to advanced post-degree programs for experienced data scientists.

Most have either extensive career counseling or a direct-to-work pipeline to connect graduates with industry partners, so they are a good choice if you are looking to move straight to a position in the field without passing through a master’s program first.

Boot camps have become so popular that now even major universities are offering them. The Columbia Engineering Data Analytics Boot Camp draws from the deep expertise at the Fu Foundation School of Engineering and Applied Science. This entry-level, part-time boot camp takes 24-weeks of evening and weekend sessions to cover:

  • Python and Javascript programming
  • Statistical modeling and forecasting
  • Data visualization with Leaflet.js and D3.js Javascript libraries
  • Big Data Analytics with Hadoop
  • Machine learning

It all happens either on-campus or online, in a collaborative atmosphere and using real-world data, like bike traffic information throughout the city, or USGS earthquake data. The school includes a dedicated career services team to help you prep your resume and professional portfolio, getting you ready for either employment or graduate program placement interviews.

Bridge Programs and Massive Open Online Course (MOOC) Options

Although admission into a master’s degree in data science requires the completion of specific undergraduate courses, many institutions allow students that do not possess all of the necessary coursework requirements to complete them as bridge courses upon admission into the program, thereby closing any educational gaps left by your undergraduate program. These are generally conventional college courses offered at the undergraduate level specifically designed to satisfy grad program entry requirements.

Another option for students that do not meet the educational requirements for admission into a master’s degree in data science is massive open online courses (MOOCs). MOOCs are offered by both universities and private providers as an entirely online version of traditional college coursework. Students can take all necessary prerequisite courses through MOOCs before even applying to a master’s program in data science, skipping the bridge courses and accelerating the entry process. MOOCs offer readily available courses and flexible scheduling options.

Relevant Personal and Work Experience

Although the completion of specific undergraduate coursework remains an important requirement for admission into a master’s degree in data science, relevant work and/or personal experience also play an important role. For example, colleges and universities may require candidates to have experience in areas like:

  • Data mining
  • Data preparation
  • Text analytics
  • Data visualization
  • Machine learning
  • Pattern recognition

Many firms in New York employ entry-level/junior data scientists, data analysts, and similar professionals, opening up plenty of opportunities for gaining the required experience for admission.

For example, the Acara Solutions in Buffalo employs bachelor’s-prepared data scientists to work under the lead data scientist and engage in numerical, stochastic, and optimization model design, implementation, and testing. New York City’s Memorial Sloan Kettering Cancer Center employs associate data scientists as part of their Strategy Analytics Team to work on projects crucial to understanding patient dynamics, effects of treatment interventions, and impacts of new care models. This job title requires candidates to possess a bachelor’s degree in a quantitative field, such as statistics, operations research, mathematics, etc.

Preparing for Success on the GRE/GMAT

Most colleges and universities offering master’s degrees in data science place a heavy emphasis on your GRE/GMAT scores, particularly for candidates who lack professional experience. Specifically, institutions often require you to score in the 85th percentile in the quantitative section of either graduate exam to have any hope of admission. You can improve your chances of success on these exams by ensuring you have a solid understanding of what to expect. Fortunately, there are a host of exam prep materials available to students:

GRE Revised General Test

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 quantitative section of the GRE assesses the skills, concepts, and abilities of the candidate in four content areas:

  • Arithmetic
  • Algebra
  • Geometry
  • Data analysis

The Math Review document provides test takers with detailed information about the GRE’s quantitative reasoning measure.

GMAT Examination

The GMAT examination’s quantitative reasoning section measures a candidate’s ability to analyze data and draw conclusions using reasoning skills.

Test takers may find a number of study tools, including videos, sample questions, and official guidebooks here.

Earning a Master’s Degree in Data Science in New York

Master’s degrees in data science may go by any number of different titles, including:

  • 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

You’ll find plenty of New York universities offering campus-based options, although many of today’s master’s degrees in data science are offered online. This allows you to choose from a bigger pool of programs and complete a data science program that best fits your professional goals.

Most online programs allow students to complete all coursework remotely, with the exception of an immersion experience, which might require a week or two on campus. Online programs are designed to be equivalent to their campus-based counterparts, offering students one-on-one time with professors and opportunities for peer-to-peer discussions.

Other program options available for master’s degree in data science students include:

  • Accelerated format: Designed to take about 12 months to complete (versus 18 to 24 months for a typical program)
  • Part-time format: Designed to take about 32 months to complete

Curriculum and Core Coursework

Master’s degrees in data science consist of about 30 credits of core coursework, along with a few elective courses and an immersion experience, which provides students with an opportunity to meet faculty and peers and engage in networking, community-building, and learning opportunities.

The curriculum focuses on topics such as mining and exploring, data visualization, and statistical analysis. Typical courses include:

  • Data mining
  • Experimental statistics
  • Applied machine learning
  • Statistical sampling
  • File organization and data management

Program Competencies and Objectives

A master’s degree in data science prepares students to become tomorrow’s data science leaders, capable of solving real-world problems using the latest tools and analytical methods. The multidisciplinary curriculum, which draws from computer science, statistics, management, and the social sciences, prepares graduates to:

  • Apply statistical analysis and machine learning techniques to make predictions and identify patterns
  • Imagine innovative uses for large datasets
  • Appreciate the ethical and legal considerations associated with data privacy and security
  • Retrieve, organize, clean, and store data from a wide array of sources

Career Opportunities for Data Scientists in New York with Advanced Degrees

Master’s-prepared data scientists in New York use scientific methods to draw meaning from raw data. Their solid foundation in machine learning, modeling statistics, and analytics, coupled with strong communication skills and business acumen, make them a valued presence in nearly every industry imaginable.

The following job descriptions show some of the professional opportunities available to data scientists in New York. Although these job postings may not be active by the time you read this and do not offer an assurance of employment, they do provide a sense of the kind of requirements you’ll need to meet to get your foot in the door:

Data Scientist, Comcast: New York, NY

Responsibilities:

  • Support optimization of Comcast’s Visible World video advertising solution by utilizing knowledge of predictive modeling within large data sets
  • Create data sets for analysis using big data tools and methodologies
  • Create prediction models for viewership numbers, inventory availability, and pricing
  • Design and implement production level optimization algorithms

Requirements:

  • Master’s degree in any quantitative or scientific field
  • Knowledge of the following:
    • Creating and analysis of large data sets using predictive modeling and big data techniques
    • Hadoop
    • Hive
    • ETL

Senior Data Scientist, Nielsen (Data Integration Group): New York, NY

Responsibilities:

  • Conduct analyses by writing SAS code and interpreting findings
  • Identify an approach to a problem using a variety of quantitative methods
  • Use queries to pull large datasets from various company databases

Requirements:

  • Advanced degree in statistics or a related field
  • At least 3 years of experience (preferred)
  • Problem-solving skills and analytical approach to problems
  • Strong quantitative aptitude
  • Strong technical proficiency in SAS, SQL, SPSS, and/or R

Senior Data Scientist, Unspecified Client through Harnham Recruiting: New York, NY

Responsibilities:

  • Research develop, and implement new methods of measuring and analyzing large and highly complicated data sets
  • Work with a variety of cutting-edge innovative software tools and open sources to gather, cleanse, and analyze complex financial data
  • Educate, mentor, and manage other data scientists to maximize impact to additional products and various business units

Requirements:

  • MS or higher in statistics, mathematics, computer engineering, computer science, or another quantitative discipline
  • At least three years of professional experience in the use of advanced statistical analysis/machine learning methods in data analysis within financial services and decision making
  • Fluent in Python, R, or Matlab and SQL

Back to Top