Hailing from backgrounds in areas like math, statistics, programming, and even physics, data scientists are a diverse group. Still, there are certain traits that seem to be universal among all of them: they’re curious, innovative, analytical, creative thinkers, and nearly all hold advanced degrees. In fact, according to a 2015 study conducted by executive recruiting firm Burtch Works, 88 percent of data scientists have graduate degrees.
- 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
- Villanova Business - Master's in Analytics and Study Data Mining, Predictive Analytics Online
A graduate degree in a quantitative field like computer science, applied mathematics or statistics has historically served as the foundation for the profession.
More recently, field-specific graduate programs in data science have been developed, offering a more targeted approach to developing the multidisciplinary skillset necessary to isolate, organize, visualize and find purpose in large amounts of scattered and unorganized data.
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Expected Outcomesof Data Science Graduate Programs
Data scientists are among a number of big data professionals who apply their sophisticated quantitative skills to derive useful information from massive datasets. However, unlike other big data professionals, data scientists possess the unique ability to store, retrieve, and exploit big data. In other words, their expertise lies with their ability to make sense of and derive insights from data before it has been organized, structured and given purpose.
Masters programs in data science prepare students to:
- Work with enormous, unstructured datasets
- Extract data from multiple, disparate sources
- Transform data to meet operational requirements
- Load data for analysis retrieval
- Derive useful information using sophisticated analytical methods
Masters degree programs in data science also arm students with the skills required to:
- Retrieve and summarize data using tools like Hadoop and MapReduce
- Write programs using languages such as Python and Java to retrieve and summarize data from Hadoop clusters
- Use statistical methods and tools like R and SAS to derive insight from data
- Use methods pattern recognition, signal processing, and visualization to derive useful information from data
Graduates of data science masters programs often go on to work for:
- Consulting agencies
- Private commercial corporations
- Financial services firms
- Gaming industry
- Government agencies
- Healthcare/pharmaceutical companies
- Marketing services
- Technology companies
The Master of Science (MS) in Data Science prepares tomorrows data science leaders to be capable of transforming big data into actionable information that can then be used in strategic decision making.
Masters degrees in data science may also be designed as:
- Master of Science in Statistics: Data Science
- Master of Computational Data Science
- Master of Information and Data Science (MIDS)
Data science masters degree programs provide students with a framework in computation, programming languages, and linear modeling and then build upon skills related to high-level mathematics, statistics, and computer science. Students learn how to analyze data, make visual representations of their results, and articulate their discoveries.
Data science masters degree programs prepare graduates to become competent and versatile data scientists capable of skillfully and confidently:
- Constructing and testing hypotheses
- Creating and evaluating models
- Drawing conclusions and determining if the results make sense in the real world
- Exploring and improving the structure of available data
- Questioning underlying premises and reformulating issues
Data science masters degree programs provide students with a project-based, interdisciplinary curriculum designed to prepare graduates to solve real-world problems by gaining insights from complex and unstructured data.
The curriculum consists of about 30-35 credits of core coursework in areas such as:
- Storing and retrieving data
- Exploring and analyzing data
- Applied machine learning
- Data visualization and communication
- Experimental statistics
- File organization and database management
- Data and network security
- Statistical sampling
Most data science masters degree programs include the study of data science ethics, which allows students to develop the ethical and critical thinking skills needed to work with large data assets while ensuring the privacy and property rights of individuals and organizations. Some of the issues covered through a data science ethics course include integrity in the data science practice, honoring intellectual property rights, and respecting data privacy.
While many colleges and universities offer their data science programs as on-campus, full-time programs, which take between 18 and 24 months to complete, perhaps just as many have begun offering their programs in alternate formats that include:
- Part-time programsfeature a more relaxed curriculum load for busy working professionals. This type of format takes about 32 months to complete.
- Accelerated programs(often referred to as professional masters degree programs) consist of a more demanding, compressed curriculum completed in about 11-12 months.
- Online programsallow students to complete the majority of their curriculum requirements through online study. Institutions offering these programs may require students to visit the campus to complete an immersion experience.
Capstone Projects and Immersion Experiences
Well-rounded masters programs include capstone projects or immersion experiences that give students the opportunity to address the kind of challenges they would encounter as professional data scientists. These final projects allow students to demonstrate their ability to use the fundamental concepts learned throughout their program all while being mentored, guided, and eventually evaluated by faculty members.
While capstone projects are typically part of the curriculum found in campus-based programs, immersion experiences can be incorporated into online programs and often represent the only on-campus requirement students will be expected to fulfill. Immersion experiences are campus-based experiences that provide students with the opportunity to network with faculty and peers, meet data science professionals, and engage in hands-on workshops and community-building opportunities.
Admission into a data science masters degree program requires an undergraduate degree from an accredited college or university. Some institutions require candidates to possess an undergraduate degree in a quantitative field, such as computer science, statistics, or physics. Other institutions dont require the completion of a specific undergraduate degree but instead the completion of specific undergraduate courses, which would typically include:
- Linear algebra
- Discrete mathematics
- Data mining/database development
- Computer programming (e.g., Matlab, C++, Java, Python, Ruby, etc.)
Data science masters degree programs require competitive undergraduate GPAs (3.0 or above, in most cases), as well as:
- Strong scores (85th percentile) in the quantitative section of the GRE or GMAT
- A resume showing professional experience in data management, data mining, data science, or a similar field
- Letters of recommendation
Data science masters degree programs often accept applicants lacking all prerequisite coursework but who are otherwise strong candidates, provided they complete the required undergraduate coursework through a bridge program upon admission. Some candidates for these programs choose to complete the required coursework through massive open online courses (MOOCs) prior to admission into the program.
Data science graduate certificates are designed for individuals who have already completed graduate study in a quantitative discipline yet want to make the move to a data science specialization. Grad certificate programs allow students to explore data science in-depth and learn more about the numerous industry-based applications for this discipline.
Just a few of the titles of these programs include:
- Big Data Science and Business Intelligence Certificate
- Certificate in Data Science
- Certificate of Advanced Study in Data Science
- Graduate Certificate in Data Science and Innovation
- Graduate Certificate of Applied Data Science
Usually consisting of about 15 credits, data science grad certificate programs may serve as stand-alone certificates or may be taken as part of a graduate degree program. Most grad certificate programs in data science are offered fully online.
Admission into these programs usually requires a masters degree in a quantitative discipline, as well as a strong academic record. Many also require work or research experience in data science, as these programs draw on students professional experiences.
Doctoral programs in data science provide students with an interdisciplinary and personalized course of study that emphasizes innovative research and the practical application of research. Program titles often include:
- PhD in Data Science and Management
- PhD in Analytics and Data Science
- PhD in Computational and Data Sciences
Students of these programs tend to have their sights set on data science careers in academia, scientific research laboratories, private industry, and governmental agencies.
These doctoral programs teach basic methodologies and techniques of computational science through approximately 48 hours of core courses and culminate in a final dissertation, internship, and/or examination. Most programs require about four years of full-time study.
Most doctoral programs in data science stipulate that applicants must complete graduate-level mathematics courses and have significant programming experience prior to applying.