Best Graduate Degree for Data Science

As the field of data science grows increasingly complex, a number of programs at the master's and doctoral level have been developed to train students to enter this career area.

With the growth in amount of data produced by technological advancement, data science has become a burgeoning career field. As a result, graduate degrees in the field of data science are becoming increasingly popular. Data scientists conduct research on business questions, develop algorithms to solve complex problems, and analyze trends and opportunities in large data sets.

Degree Options for Data Science

While there are a few jobs available in data science with only a bachelor's degree, data scientists typically enter the field with at least a master's degree. Degree programs in data science are also available at the doctoral level. These programs generally take longer to complete but could provide stipends and other forms of financial support to doctoral candidates.

Master of Science in Data Science

One option for those interested in this field is to earn the Master of Science (M.S.) in Data Science. The M.S. in Data Science can usually be completed in one to two years of full-time study. Students learn various programming languages and statistics, as well as develop the skills needed to communicate findings. In addition to coursework, students may have the opportunity to complete a research project demonstrating their skills with data. Applicants should plan to submit transcripts, a resume, recommendations, and GRE or GMAT scores. Undergraduate prerequisite courses in algebra, programming, and statistics may be required.

Master of Science in Business Analytics

Another option is to pursue an M.S. in Business Analytics. This degree can typically be completed in one year of full-time study. Students learn data management, applied statistics, and application of analytics to business strategies. Opportunities may be available to work on live projects to gain real-world experience. To apply, prospective students should hold a bachelor's degree, preferably in a quantitative field. They should submit transcripts, letters of recommendation, a resume or CV, and GRE or GMAT scores. Applicants should be able to demonstrate knowledge of programming languages and statistics. An interview or video essay may be required.

Doctor of Philosophy in Data Science

Those who wish to study data science at the highest level might consider earning a Doctor of Philosophy (Ph.D.) in the field. Students engage in coursework, gain experience in the industry, and complete a research dissertation. Comprehensive exams may be required. To apply, applicants should hold a bachelor's degree in a computational field and demonstrate experience and knowledge of calculus, modeling, and programming. Some programs require a master's degree as well. Transcripts, recommendations, a resume, and GRE scores should be submitted. Graduates of data science Ph.D. programs are qualified for positions in business, government, or academia.

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Program Requirements

Within any of these graduate degree programs, students will develop expertise in collecting, organizing, analyzing and storing data to help businesses and other organizations drive their decision-making processes. In order to best hone these skills, there are some common core courses that are encountered across different programs.

Data Visualization

Within this course, students learn techniques to develop visual representations for different types of data sets, including geospatial data and time series, in order to clearly communicate information. Fundamentals of graphic design are considered, and software tools such as SQL or SAP may be utilized. A case study or project may provide students the opportunity to create their own visualization.

Data Mining

This course gives students the opportunity to interact with large data sets in order to discover patterns and facts within the data. Students can learn the processes of selecting data, cleaning data, and coding data. They may also apply what they have mined to specific business problems. Query design may be reviewed.

Machine Learning

Students in this course learn how to develop and apply machine learning algorithms and other machine learning techniques. Various models for machine learning, such as kNN and random forests, are reviewed. Students focus on how to evaluate accuracy of machine learning and apply results to data problems in the business world.

Computational/Applied Statistics

This course covers advanced statistical methods. Regression model applications, such as multiple regression, model diagnostics, and analysis of variance, are covered. Other topics may include graphical models and Bayesian inference. Various software, including R, may be utilized.

Cyber Security

Within this course, students learn how to identify and address threats to the information technology of a company. Types of risks and threats are considered. Students also learn standards for cyber safety.

Social Media Analytics

Within this course, students learn how to utilize social media to enhance the performance of a business. They look at various types of social media initiatives and how metrics are used to measure the performances of these campaigns. The various fields in which these analytics are utilized, including social computing, marketing, and informatics, are studied.

Earning a graduate degree in data science provides career opportunities in a quickly-growing field. Students with a strong quantitative background can find success in these programs.

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