Companies, organizations, and government agencies have one thing in common: they all possess an unwieldy collection of unorganized and unstructured information. But this data is not the mess it seems. In fact, it is a virtual gold mine that could boost revenue, inform decisions, improve public health, and save money (just to name a few). Enter data scientists: experts of technology who unearth, organize, and make sense of big data, thereby gifting organizations with innovative insights that rewrite the rules of business.
- Syracuse University - M.S. in Applied Data Science: GRE Waivers available
- SMU - Master of Science in Data Science - Bachelor's Degree Required.
- UC Berkeley - Master of Information and Data Science Online - Bachelor's Degree Required.
- Syracuse University - Master of Information Management Online
- Maryville University - Master of Science in Business Data Analytics
- Villanova Business - Master's in Analytics and Study Data Mining, Predictive Analytics Online
New York City’s Mayor Bloomberg knew that data is of little value if we can’t interpret it and achieve insights from it. Taking his background in data-driven analytics in the financial sector, Bloomberg wanted to prove that the same techniques could work for cities, too. So, with Mayor Bloomberg’s backing, the Office of Data Analytics (MODA) was created.
The purpose of MODA was succinct: to use data to improve critical services, empower frontline workers, save money and, most importantly, save lives. Nearly six years later and the program is still going strong; in fact, it has become a central part of NYC’s City Hall approach to government, which includes enhancing:
- Service delivery
- Emergency response times
- Economic development
- Tax enforcement
The data scientists of MODA combine and interrogate data from a variety of sources as to increase the efficiency and effectiveness of government operations and services. Their work has allowed MODA to serve as a model for data science—the process of making sense of massive amounts of unorganized data. It comes as no surprise, then, that others are following suit and quickly realizing just how much can be achieved by giving purpose to large amounts of data.
Data Driven NYC, for example, is a community endeavor that brings together enthusiasts with a passion for big data, data-driven products, and data technologies. A number of big data conferences also make their way to New York every year, including Big Data Everywhere (aimed at helping financial services firms leverage big data in order to maintain a competitive edge) and Big Data for Media Conference (features the world’s foremost experts on Big Data analytics and technologies).
Preparing for a Master’s Degree in Data Science in New York
Bachelor’s prepared individuals with their sights set on pursing a master’s degree in data science must be prepared to prove their ability to succeed in this highly technical discipline—through their undergraduate work, their previous experience or, more often than not, a combination of the two.
Undergraduate Degree and Master’s Prerequisite Courses
One of the most important qualifications students interested in graduate work in data science must possess is an undergraduate degree in a related discipline, such as computer science, operations research, engineering, applied mathematics, statistics, and physics.
While some colleges and universities require candidates to possess a bachelor’s degree in a related discipline, others do not have this requirement; instead requiring students to complete specific undergraduate courses, including:
- 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 include:
- Minimum undergraduate GPA
- Admissions interview
- Letters of recommendation
- Minimum GRE/GMAT scores in quantitative reasoning
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 not possess all of the necessary coursework requirements to complete them as bridge courses upon admission into the program, thereby satisfying any educational gaps in their undergraduate study.
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). Students can take all necessary prerequisite courses through MOOCs before applying to a master’s degree program in data science, thereby accelerating the process of entering the graduate program. 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 possess experience in areas such as:
- 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, thus providing candidates interested in a master’s degree in data science with opportunities for gaining the required experience for admission.
For example, the Superior Group 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 candidates’ GRE/GMAT scores, particularly for candidates who lack professional experience. Specifically, institutions often require candidates to score in the 85th percentile in the quantitative section of either graduate exam. Students can improve their chances of success on these examinations by ensuring they have a solid of understanding of what to expect. Fortunately, there is a host of exam prep materials available to students:
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:
- Data analysis
The Math Review document provides test takers with detailed information about the GRE’s quantitative reasoning measure.
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 a number of 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
New York colleges and universities offer a number of campus-based data science master’s degree programs, although many of today’s master’s degrees in data science are offered as online programs. Online master’s degrees in data science allow candidates to choose from a greater pool of programs and complete a data science program that best fits their professional and personal goals.
Most programs allow students to complete all program requirements through web-based study, with the exception of an immersion experience, which requires in-person participation. Online programs are designed to be equivalent to their campus-based counterparts, offering students such features as one-on-one professor attention and opportunities for peer discussion.
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. Therefore, 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 who solve 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 utilize scientific methods to create 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 in the state.
The following job descriptions, sourced in March 2016, reveal the many professional opportunities available to data scientists in New York. Although not a guarantee or assurance of employment, they do provide data scientists with advanced degrees with a clear picture of the types of jobs available in New York:
Data Scientist, Comcast: New York, NY
- 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
- 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
Senior Data Scientist, Nielsen (Data Integration Group): New York, NY
- 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
- 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, Harnam: New York, NY
- 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
- 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