Individuals who are interested in pursuing a master's degree program in data science have a number of different options to choose from. These programs will expose students to the major concepts in data science and prepare them for a variety of different careers.
Master's Degree Programs in Data Science
Master's degree programs in data science are generally found as Master of Science programs and can typically be completed in about two years or less, though some programs can be completed on a part-time basis over a longer period of time. These programs generally include a number of required core courses and then give students the option of selecting a more focused track or taking elective courses that align with their specific interests. Below, we will look at some of the courses that are commonly included in these master's degree programs.
Algorithms in Data Science
In this course, students will learn how algorithms are used in the field of data science. They may begin by focusing on how to design and analyze algorithms and how to use them in order to solve different types of problems that occur in the field of computer science. This course may focus on topics like data structures, computational complexity, graph algorithms, and string and pattern matching.
Master's degree programs in data science generally include a course on machine learning in which students will gain a foundational understanding of machine learning, including both the theories of machine learning and the mathematical methods that are used to solve real problems. Topics likely to be discussed in this course include linear regression, Bayesian networks and interference, graphical models, and support vector machines.
Statistical Methods in Data Science
These programs are likely to include multiple courses that focus on statistical methods in data science, as well as related topics like probability, modeling, and statistical inference. Students will learn how to use various types of statistical methods in order to properly analyze data. Topics that could be discussed in statistical methods courses include probability theory, convergence of random processes, descriptive statistics, and regression.
Introduction to Optimization
In this course, students will be exposed to various types of optimization problems, including nonlinear optimization, network optimization, and linear optimization. Students will learn how to use different types of optimization models and methods to solve real-world problems in data analysis and data science. Topics covered in this course could include compressed sensing, matrix completion, and clustering.
Programs in data science will also likely include at least one course that is specifically focused on data science in general, or a specific aspect of data science. This course will provide students with a foundational understanding of big data, as well as the different types of statistical analysis methods that can be used to handle and process big data. Students will learn how they can use their knowledge in other areas, like algorithms and machine learning, to handle complex big data problems.
General Admission Requirements for Master's Degree Programs in Data Science
To be admitted into a master's degree program in data science, students should be sure to check with the program they are interested in attending to verify the specific admission requirements. However, in general these programs require that students have a bachelor's degree, which can usually be in any subject as long as students have taken all of the necessary prerequisite courses. This includes having taken calculus, computer programming, linear algebra, and statistics. Many of these programs may also have minimum GPA requirements. In addition to submitting undergraduate transcripts, students will also need to submit an application form, letters of recommendation, resumé, personal statement, and results from the GRE.
Master's degree programs in data science are designed for students who have a strong background in math and computer science and who are interested in gaining a more advanced level of understanding through focused coursework.