Commonly offered as a Master of Science, master's programs in computational data science are available from institutions around the country. In these programs, students will gain familiarity with writing programs, designing algorithms, and mining data.
Information for Master's in Computational Data Science Programs
Master's degree programs in computational data science typically begin with introductory courses on general programming or data analysis concepts and then allow students to further specialize through electives or concentrations. Below are some examples of common courses for this degree.
This course could be general and discuss theoretical programming techniques, or it may focus on building skills in a particular programming language. Students may be asked to design their own programs, and they may learn about data structures that are foundational to programming. Students could also be introduced to user-defined classes.
In a machine learning course, students could explore how to design programs that learn and improve through artificial intelligence. This course may discuss a variety of machine learning models and ask students to create their own machine learning programs. Additionally, this course will typically cover statistical concepts that are imperative for machine learning, such as kernel machines, conjugate distributions, stochastic inference, and posterior distributions.
In a course on data mining, students might learn techniques for extracting large sets of data and gain an understanding of distributed file systems. Generally, approaches like clustering and classification will be covered, and degree candidates could be asked to mine data from sample databases. Some courses may also discuss popular applications that are designed for data mining. Some courses might also cover web data mining and focus on using data mining for e-metrics, web analytics, and web personalization.
Algorithm Design and Analysis
This class might teach students the foundational principles required for designing algorithms and analyzing their results. Common techniques, such as divide-and-conquer or dynamic programming, may be discussed. Students may also explore polynomial complexity classes and problems associated with them.
Students with experience in a high-level programming language may take a class on designing and implementing operating systems. This course might cover foundational concepts like file systems, security, and memory management and consistency. In addition, degree candidates may be asked to complete assignments in which they analyze portions of existing operating systems to illustrate their understanding.
Common Entrance Requirements
For admission to a master's in computational data science program, many schools require a bachelor's degree in computer science or a related field, or proof that you have completed relevant math and computer science courses. Typically, students will not be accepted with a GPA below a 3.0 on a 4.0 scale. Many programs ask for official transcripts from past attended institutions, GRE scores, and TOEFL scores for non-native English speakers. Some programs might also ask applicants to submit a resume, letters of recommendation, and a statement of purpose.
Students interested in a master's degree in computational data science can expect to expand their programming skills and learn how to mine and analyze large sets of data. These programs typically have academic prerequisites and require test scores for admittance.