M.S. Vs. PhD in Data Science

Mar 22, 2019

Students interested in studying data science can pursue an M.S. or PhD to work in fields like education, business, science, politics, and more. Here we discuss some of the differences between the degree levels and their common admission standards.

Data science is a growing field that can be utilized in a wide range of industries and is available for study at the master's and doctoral levels. Due to the broad nature of the field, most of these graduate programs allow students to specialize in a particular area and usually conclude with a culminating experience of some kind. Explore the details of the Master of Science and Doctor of Philosophy degree programs for data science.

M.S. vs. PhD in Data Science

Master of Science in Data Science

Master of Science (M.S.) in Data Science programs can be taken full- or part-time and usually take 2 to 3 years to complete. Some of these programs may offer online courses for flexibility and programs may require around 36 credits for completion. Students in these programs typically choose from a range of tracks or specializations in areas such as environmental science, data engineering, physics, artificial intelligence, business analytics, or microeconomic analysis. These programs usually require a final capstone experience, but some programs may also offer a thesis option, and students may take courses in subjects like machine learning, statistical programming, data science, probability, and big data. Graduates of these degree programs can work as data scientists in a variety of fields, including business, politics, science, media, and manufacturing.

Doctor of Philosophy in Data Science

The length and credit requirements for Doctor of Philosophy (PhD) in Data Science programs vary based on institution and/or a student's educational background, as some programs allow students with only a bachelor's degree to earn their M.S. on their way to earning their PhD. Coursework can usually be completed in the first 3 years and may require around 60 to 72 credits. Students in these programs may be able to choose from concentrations in areas such as life sciences or financial services, or take a range of elective courses that may include subjects like natural language processing, optimization, and more. These programs usually require comprehensive exams and a dissertation and may include core coursework in areas such as research methods, data science, data analytics, statistical modeling, computational systems, and machine learning. Graduates of these programs are also qualified to work as data scientists in diverse industries, but may also pursue careers in academia.

Common Entrance Requirements

Students applying to M.S. or PhD programs in data science must have at least a bachelor's degree and may need to have prior coursework in areas like algebra, calculus, computer science, statistics, or probability. Some programs may also require or like to see programming skills and/or GRE or GMAT test scores. Work experience is not required, but several programs will factor experience into their admission decision. Typical applications for these programs require students to include their transcripts, letters of recommendation, and statement of purpose. Some programs may also require a resume or CV to examine students' backgrounds and experiences.

Students can usually earn their M.S. in Data science in 2 to 3 years and must complete a capstone project or thesis, while PhD students usually take more than 3 years to earn their degree and must complete a dissertation. Both graduate programs allow students to focus their studies in a particular area of interest and prepare students for careers as data scientists.

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