Data Science Master's Vs. Machine Learning Master's

Jun 13, 2019

Students interested in working with large data sets in various fields may pursue a master's degree in data science or machine learning. Learn about some of the similarities and differences between the degrees to find the right match for you.

Machine learning is a specific specialization or minor area of data science, and therefore, master's degree programs in data science tend to be broader and provide a greater overview of the field of data science. Both master's degree programs are available in flexible formats and provide hands-on learning opportunities, but vary slightly in coursework and other graduation requirements. Compare and contrast the 2 programs here.

Comparing a Master's in Data Science to a Master's in Machine Learning

Master's in Data Science

Master's degree programs in data science are typically offered as Master of Science (MS) degrees and can be taken full- or part-time. Some of these programs may be available in online formats and/or can be completed in as little as 1.5 years, but most programs take about 2 years to complete. A few programs may offer additional areas of concentration within the field, such as data engineering, artificial intelligence, analytics and modeling, or analytics management, and most of these programs require students to complete a capstone project or thesis. Students may be required to complete around 36 to 45 credits and may take courses that discuss topics in applied statistics, decision analytics, project management, machine learning, data systems, and probability. Graduates with their master's in data science may pursue careers as data scientists or other data science professionals in research organizations, industry, nonprofit organizations, or the government visualizing and managing large data.

Master's in Machine Learning

Master's degree programs in machine learning are also usually available as MS degrees, but students can also pursue MS in Computer Science degree programs with a track or specialization in machine learning. These degree programs are available in full- and part-time formats, can be taken online, and may require around 30 credits for completion. Students in these programs may gain hands-on experience through practicum or internship experiences, and some programs may offer an optional thesis experience. Students typically complete these programs in 2 years, and coursework may discuss topics like machine learning, data analysis, probability, deep learning, statistical machine learning, artificial intelligence, and optimization. Graduates with their master's in machine learning can also work in research, industry, or the government with job titles like data scientist, machine learning engineer, data engineer, research scientist, or business intelligence developer.

Common Entrance Requirements

Students applying to either a master's in data science or master's in machine learning program typically need to hold a bachelor's degree. Many of these degree programs are competitive and may require students to meet a minimum GPA of a 3.0 or higher. Typically these programs also require the GRE, but some programs may only recommend that students take the GRE or may not require the exam for part-time students. Some data science programs may also prefer to see relevant research work or research experience in the field and/or require applicants to have prior coursework in areas like linear algebra, calculus, and computer programming. Common application materials for both degree programs include transcripts, letters of recommendation, a personal statement or series of essay responses, and/or a resume.

Master's degree programs in data science and machine learning are typically offered as MS degrees and usually take around 2 years to complete. Both degree programs can be completed in online formats, but data science programs are larger in scope and may provide additional areas of concentration.

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