Do Online Master's in Data Analytics and Master's in Data Science Programs Require Computer Programming Experience?

Answer: Yes and no, as it depends on the program.

Some online Master’s in Data Analytics and Master’s in Data Science programs do require prior computer programming experience, while others merely prefer that applicants have some programming knowledge. There are also programs that do not specify prior computer programming experience as a requirement for admissions.

Online Master’s in Data Analytics and Master’s in Data Science programs typically consider a number of factors when evaluating applicants for admission, including standardized test scores, undergraduate grade point average, relevant work experience, and proficiencies in statistics, mathematics, and computer science. Some programs require or recommend that applicants demonstrate some knowledge of computer programming, either through prior professional experience (usually 2-5 years), or the completion of undergraduate coursework in computer science. These requirements vary by school, so potential applicants should carefully research the specific admissions criteria for each program they are considering.

There are also online Master’s in Data Analytics and Master’s in Data Science programs that do not require computer programming experience for admission. However, the fields of data analytics and data science are programming-intensive. Therefore, while it may not be required for admission, prior computer programming experience, along with a background in mathematics and statistics, is generally an advantage for students entering online Master’s in Data Analytics and Master’s in Data Science programs. For students who lack programming experience, online resources offered by organizations like Udacity and Codecademy can be a good way to prepare for an online master’s in analytics program.

Programming languages and software environments for Online Master’s in Data Analytics and Data Science programs

There are a number of different ways to prepare for online Master’s in Data Analytics and Data Science programs. In terms of computer programming proficiencies, this can be broken down into three distinct areas: data management tools; general-purpose programming languages; and statistical computing software environments.

For data management tools, most experts agree that it is helpful to be familiar with the various functionalities of Microsoft Excel, which is the current industry standard spreadsheet application for organizing and analyzing data, as well with basic SQL (structured query language). SQL is the industry standard language for navigating databases. The general-purpose programming language most often associated with data analytics and data science is Python, although familiarity with another common, high-level programming language, such as Java or C++, can be just as helpful in preparing for online Master’s in Data Analytics and Master’s in Data Science programs.

The core work in data analytics and data science programs involves mining, modeling, and interpreting large datasets. This is done using software that is designed to handle these complex operations. The two statistical computing languages/software environments most commonly used in this facet of data analytics and data science programs are SAS (Statistical Analysis System) and R. Learning to work with SAS, R, and other statistical computing languages is a core part of the curriculum in online Data Analytics and Data Science programs. However, curricula do vary, so prospective students should explore the details of each program as part of the application and preparation process.

There are several other programming platforms that are used by data scientists engaged in advanced analytics work. For example, Apache Hadoop is an open-source software framework that can handle very large datasets from multiple sources. Advanced programming languages like Hadoop are typically taught after students have learned to code in one or more of the primary programming languages and platforms used by data analysts.

For additional information on programming languages and platforms used by data analysts and data sciences, see our Guide to Careers in Analytics.

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