PhD Programs in Data Science

Those interested in the field of data science can work to earn a PhD in the field. Learn more about how to pursue a PhD in Data Science, including information regarding program admission and common classes in the program.

How to Earn a PhD in Data Science

Before entering into a PhD program for data science, students will need to meet set admission requirements such as taking certain tests and submitting their scores to the schools they are applying to. A typical PhD program in data science will require between 60 and 72 credit hours of coursework, and some of the classes that students may take include introduction to data science, probability and statistics for data science, and big data.

Introduction to Data Science

A class like this will introduce students to the concept of working with large-scale data, and also give students the chance to gain hands-on experience working with different possible areas of focus in the field. Some of the topics that could be covered in an introduction to data science course include data collection, data security, management, modeling analysis, statistical modeling, and informed decision making. This course will touch base on the challenges of working with different types of data and big data, and areas where big-data can be seen such as social media and in the telecommunications industry.

Probability and Statistics for Data Science

This course will teach students how to use different techniques in probability and statistics from a data science perspective, and students will also learn how to use different types of statistical software. Before entering into this course, certain schools may require prerequisite courses like calculus, linear algebra, or basic programming. Other topics that may be covered in this class include time series, data dimension reduction, clustering methods, multiple linear regression, and/or committee methods.

Big Data

The main focus of a course in big data is to teach students about the different obstacles to working with large pools of data, along with the different methods for doing so. Some of the different techniques that students will touch base on could include mining, machine learning, and computational techniques. Students will learn about real world applications, like the use of data mining in regards to social media accounts, and also gain key hands-on experience.

Deep Learning

A deep learning course is designed to focus on the concepts of machine learning, more specifically the practical and mathematical perspectives as they relate to the topic of artificial neural networks. Some of the most prominent network architectures that will be discussed include CNNs, GANs, and RNNs. By the conclusion of a course like this, students should be able to understand the math behind deep learning, and they will have had the chance to work on projects that will provide them with essential hands-on experience.

Fundamental Algorithms

Fundamental algorithms is a course set up to teach students about different important algorithms with an emphasis on both correctness and efficiency of the algorithms. A few of the topics that can be covered in this course are synthesizing algorithms, average run times for algorithms, elementary data structures, and randomized algorithms. Students will cover practical applications and techniques that are used for both writing and analyzing programs.

Machine Learning

Students in a machine learning course will learn about topics in the field that will help them to be able to model real-work problems using models of probability. A course like this usually focuses on practical uses of machine learning and algorithmic uses. Additional topics that can be covered include concept learning, neural networks, active learning, linear learning, and hypothesis testing.

Common Admission Requirements

Applicants that want to gain admission into a PhD program for data science will need to take the GRE, GMAT, or TOEFL, depending on the institution to which they plan to apply. Some universities or colleges may also require students to already have a minimum of a master's degree. Students will need to fill out a college or university specific application and pay any application fees stated on the forms. Additional documentation applicants may need to submit include previous college transcripts, letters of recommendation, or a statement of purpose.

Once students meet set admission requirements, such as taking either the GRE exam, GMAT exam, or TOEFL exam and submitting their scores, they can enter into a PhD program in the field of data science. These programs can require students to accumulate anywhere from 60 to 72 credits from a variety of different courses that could include deep learning, big data, fundamental algorithms, and probability and statistics for data science.

To continue researching, browse degree options below for course curriculum, prerequisites and financial aid information. Or, learn more about the subject by reading the related articles below:

The schools in the listing below are not free and may include sponsored content but are popular choices among our users. Tuition and costs will vary across programs and locations. Be sure to always request tuition information before starting a program.

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