A master's degree in data science is great for students who want to continue pursuing expanding technological fields. By combining an understanding of data systems and analysis, the graduate student will be taught how to interpret new information from large data sets, and thus, be more desirable to many prospective employers.
Data Science Master's Degree: Salary & Jobs
|Job Title||Average Salary (2019)*||Job Growth (2018-2028)**|
|Computer Scientist||$122,840||15% (forr Computer and Information Research Scientists)|
Sources: *Payscale.com; **Bureau of Labor Statistics
Related Careers for a Master in Data Science
A computer scientist studies both practical and theoretical information that can be transmitted from computer databases. Individuals are expected to have a master's degree in statistics, engineering or computer science. Computer scientists need to be efficient problem solvers who can develop algorithms and use software engineering codes. Specifically, they work to improve software systems in a team environment.
Research analysts solve perplexing technological issues via analytical and mathematical applications. Most analysts have a master's degree in computer science, mathematics, engineering or another technical field. In order to succeed in this career, the individual needs to be highly proficient in problem-solving by using data mining and statistical methods. These analysts work alongside businesses and organizations to manage cost-effective and efficient solutions.
Statisticians can work in market research companies, federal government agencies or in research studies to collect and analyze quantitative data (surveys, polls, experiments, etc). They create new models and recognize trends within data to submit statistical reports. A mathematical background, usually a master's degree in statistics or finance, is required to obtain a career in entry-level positions. This career path is unique in that it allows the individual to work with varying audiences to complete a common goal.
Marketing analysts aide companies in the supply and demand of various products by analyzing the competitor, consumer and market conditions. A marketing analyst is expected to have obtained a graduate degree in business, statistics or computer science. Specific tasks may include studying price values of both the competitor and the distributor, create methods to advertise or communicate products and work with other developers to find a common goal. A marketing analyst will use skills in SQL databases, programming, and analytical problem-solving.
Survey researchers analyze data that have been obtained through surveys, whether it be from nonprofits or researching organizations. Survey topics may include health, economics or politics, and work with the general public through various online platforms. Survey methods include polls, questionnaires, and file reviews. Most companies require experience in SQL and excel, as well as graduate-level education in order to pursue this career path.
In order to pursue a master's degree in data science, the student must have a background in statistics or computer science (i.e. bachelors) with a minimum 3.0 GPA. Most programs require the student to have taken the GRE. Average scores on the GRE place in the 150s, while scoring in the 160s is preferential for higher-ranked schools. In addition to taking the GRE, most programs require three letters of recommendation, as well as all college transcripts. Completing a master's degree in data science can take up to two years, depending on if you're going full time.
Master in Data Science Program Information
As technological advances increase, so does the rising demand for data science students. Schools nationwide are offering master programs for students who want to pursue careers in tech companies as a business analyst or computer scientist. Listed below are common courses you might encounter in a master's of data science program.
Introduction to Data Science
An introduction to data science course will provide a general overview of data science by reflecting on case studies through specific organizations. Specifically, students will learn about data mining, matrix factorization, and Python programming. This course is the first step in getting graduate students accustomed to the technological language that will be used throughout the program.
Classes focused on algorithms will prepare students to learn how to interpret algorithms in order to analyze data. Some topics may include algorithm assortment, programming, and binary search trees. In order to take this course, the student is expected to have experience in using computer languages (i.e. Java).
During a machine learning course, students will use statistical modeling to understand the basics of machine learning. Students will become well versed in real-world problems in the realm of data science. Some topics may include probability models, properties of Lasso and convex optimization.
An applied statistics class will explore programming skills in SQL and Python, both of which emphasize deciphering code languages. Specific data analysis techniques will be taught in order to create statistical models and estimations. In addition, other statistical methods will be studied, such as analyzing variance and covariants, as well as regression and nonparametric methods.
Database Management Systems
This course aims to teach students the fundamentals of database management systems, which involves manipulating data structure through query processing and optimization. The student will learn about index structures, resource allocation, and transaction management. Learning about this system software will provide the necessary skills to create and manage databases.
If you're an undergraduate student looking to pursue a master's in data science, you have chosen a high growing technological field. A master's in data science allows the student to further understand data systems and acquisition, which in turn will prepare them for rewarding careers.