Data Science PhD Programs

Students interested in data analytics and statistics may consider earning a PhD in Data Science. Explore this degree program and get information about some of the standard admissions requirements.

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Doctoral degree programs in data science advance students' skills in collecting, analyzing and managing large data sets. These degree programs are typically offered on-campus and can usually be completed in four to five years. Learn more about these degree programs and some of their typical requirements.

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Information for Doctoral Degree Programs in Data Science

Students pursuing a PhD in Data Science typically need to complete a dissertation in addition to their coursework. These programs often offer a variety of elective courses to allow students to individualize their studies, but here we examine a few of the common course topics for these programs.

Introduction to Data Science

Many degree programs in data science offer an introductory course to provide students with a broad overview of the subject. These courses may be offered online and discuss basic information processing, evaluation and analysis, as well as ethical issues in the field. Students often get hands-on experience with various software programs and programming.

Big Data

Students are usually required to take a course in big data early in the program that covers various methods for working with large data sets. Some of these methods may include map-reduce framework, class embedding, feature hashing and more. Students in advanced courses may discuss cloud computing, supercomputing, grid computing and other advanced algorithms for big data.

Machine Learning

Machine learning is another course that is typically taken early in the program as a foundation to build upon. Some of these courses may be set up as a research seminar to discuss current issues in the field, while others explore topics and methods in machine learning. Specific topics may include pattern recognition, neural computation and theoretical and statistical modeling.

Research Design

Students in these degree programs usually take some kind of course that discusses research design in the field of data science. Some of these courses take a broad approach and cover a wide range of methodologies in qualitative and quantitative data to help students prepare for their own research projects. Other courses may examine specific topics in experimental and quasi-experimental design, such as Latin square, randomized complete block, split-plot, incomplete block and repeated measures designs, among others.

Modeling/Statistics for Data Science

These courses may go by many names, including data analytics, modeling in data science or probability and statistics in data science, depending on their emphasis, but they're usually taken early in the program. No matter the name of these courses, they usually cover topics in statistical methods and modeling techniques for analyzing data. Specific topics vary by course, but may include linear regression, boosting, visualization techniques, segmentation models, production level modeling, probability and more.

Common Entrance Requirements

Students interested in applying to doctoral degree programs in data science usually need to include their official transcripts, GRE scores, letters of recommendation, a personal statement and a resume or CV with their application. Some of these degree programs may have a minimum GPA requirement and/or require students to go through an interview process. Students must hold a bachelor's degree, and typically a master's degree is preferred; some programs may ask that these degrees be in a scientific discipline, such as statistics, mathematics, engineering or computer science. However, most programs require students to have prior coursework in calculus, computer programming, statistics and probability.

Doctoral degree programs in data science are usually heavy in statistical and mathematical coursework that equips students with advanced analysis skills. These degree programs require a dissertation and provide hands-on learning with data sets.

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