Master of Applied Data Science

Oct 02, 2019

Data science is a fast-growing field, and you can get a job in it through a master's degree in applied data science. Keep reading to find what these programs require and how to get accepted.

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A master's degree in applied data science can give you the necessary skills to become a data scientist, which was ranked as the number one job in the United States for 2019, according to Glassdoor. Many Master of Applied Data Science programs are available online, making it easier for working professionals to gain new skills in this growing field. Most courses in an applied data science program will require a foundation of math and computer science--read more about the coursework and the admission requirements below.

General Master of Applied Data Science Degree Information

Many of the courses mentioned below are also offered at more advanced levels as students progress through their programs. However, as students advance they can also choose courses to learn more specific techniques or to apply what they've learned to other fields, like marketing, finance or business.

Introduction to Data Science

Many programs will have an introductory class that gives an overview of the field. Courses like this may cover the history of data science and its uses today. These courses might serve as a review of some of the computer programming and math skills needed as well. Students may also learn about ethical concerns related to data science and topics like quality control and data publication.

Data and Database Management

Courses in database management teach students how to develop, design and manage modern databases. This can include search specifications and query language, file organization, data modeling and more. Some courses may also include cloud data storage.

Data Visualization

Classes in data visualization cover how to visually represent data in ways that make sense and are understandable. These courses will likely cover cognition, perception and design, and how visualization affects how data is understood. Students may learn about graphs, maps and trees, and how to use programming languages like Python as part of data visualization.

Data Analytics

Data analytics courses can include basic techniques for using statistics to analyze data. They also may cover how to organize, display and summarize data, and discuss sampling and testing hypotheses. Courses may include theory, like the challenges of data analysis and where the field is headed, but also the practical application of data analysis in the real world.

Machine Learning

Machine learning is a field of study of programming machines to learn on their own without being explicitly instructed to do so; it's a subset of artificial intelligence. Courses in machine learning may include the theory and application to real-world situations. They also might cover topics like data preprocessing, data reduction, principal component analysis and more.

Data Mining

Data mining is all about finding patterns in large amounts of data to gain information from them. An introductory class might cover basic data mining techniques and their applications. Students may also learn about item sets, matrices, and sequences and the map-reduce programming model, and may use case studies to help further their knowledge.

Big Data Analytics

Big data analytics courses cover methods for analyzing sets of data that are too large or complex to address with normal methods. These classes may involve using real-world case studies. Courses in big data analytics may include concepts about data mining and machine learning. Topic covered may include the data lifecycle and the storage and processing of big data.

Program Admission Requirements

Most Master of Applied Data Science programs generally require a bachelor's degree, with a preferred or required GPA of at least 3.0 or higher. Other documents that schools ask for include letters of recommendation, a statement of purpose and a resume. GRE scores are not always required but can help strengthen applicants, especially for applicants who have low GPAs.

As far as prerequisite coursework, applicants don't necessarily need to have bachelor's degrees in data science or related fields, but they do generally need to have knowledge of calculus and proficiency in statistics. Some programs ask for proficiency in computer programming languages, like Python, but others don't.

In general, master's degree programs in applied data science require foundations in math and programming, whether through college-level mathematics and statistics, or professional experience working with data. Students can also expect the hands-on application of data science topics and not just theory.

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