Master’s in Data Science Program Guide
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In 2012, Harvard Business Review called data scientist the "sexiest job of the 21st century." They defined "sexy" as being in demand and possessing rare qualities.
Data scientists typically have a background in analytical skills, computer science, and science. It's common to find a candidate with one of these qualities, but it can be much harder to find one with all three. A master's degree in data science program can help provide those skills.
Data scientists combine their knowledge of statistics, science, and math with industry knowledge to analyze and evaluate large sets of data. They look for patterns and come up with meaningful insights that their employers can use to improve products and services. Data scientists work in many different industries.
Graduates with a master's degree in data science may qualify to work in positions such as data analyst, data scientist, senior data scientist, data management and biostatistics scientist, and machine learning engineer.
Should I Get a Master's in Data Science?
Many data science positions require a graduate degree. Earning a master's in data science can give you access to more opportunities and help advance your career.
The U.S. Bureau of Labor Statistics (BLS) projects 22% job growth for computer and information research scientists from 2020-2030. This career umbrella includes data scientists. According to the BLS, the typical entry-level education for a computer and information research scientist is a master's degree.
It takes 1.5-2 years, on average, to earn a master's degree. However, depending on the program and whether you attend school full time or part time, it could take anywhere from seven months to seven years. Attending school part time can give you the option of continuing to work while you earn your master's degree. You might want to consider an online master's degree in data science program if you are going to be working and attending school at the same time.
One challenge data scientists often face is a lack of specific industry knowledge. Employees in many roles need more than a master's degree in data science. To understand the specific data they'll encounter on the job, they may also need ample industry knowledge. This can take time to develop.
Data scientists may improve their chances of landing a better job by obtaining one or more data science certifications. Certifications include certified analytics professional, Microsoft certified: Azure data scientist associate, and Cloudera certified associate data analyst, among many other options.
What Will I Learn in a Data Science Master's Program?
Students enrolled in a master's program in data science can learn about research design, data mining and exploring, data cleansing, data engineering, and information ethics and privacy. They also may study statistical analysis, data visualization, and machine learning.
Typical courses include:
- Applied Machine Learning: This course teaches students to solve problems using machine learning techniques.
- Behind the Data: This course looks at policy, legal, and ethical issues involved in data science.
- Natural Language Processing With Deep Learning: This course teaches students how to analyze linguistics using machine learning.
Concentrations
Data Acquisition and Management
This concentration focuses on how companies acquire and manage data. Students learn how to apply data science techniques to data acquisition and management.
Data Security
In this concentration, students focus on how data science applies to data security. Students learn how to use data science to keep their organization's data secure.
Business Applications
Students learn how to use data science to inform business decisions. They learn how to act as a liaison between the data and the executives who must make decisions based on the data.
Computational Intelligence
This concentration covers the application, design, theory, and development of linguistically and biologically motivated computational systems as they relate to data science.
Big Data Informatics
In this concentration, students focus on big data in healthcare. They learn how to apply data science techniques to analyze and manage this data.
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How to Get Into a Data Science Master's Program
The requirements for admission vary from one program to another. However, to get into a master's program, you should first earn a bachelor's degree. There may or may not be a requirement to have that degree in a certain field.
Due to the nature of data science programs, most schools have prerequisites in math and computer science. These typically must be completed before beginning master's-level coursework. These prerequisites may include courses such as calculus, intro to computer science, and statistics.
Some schools may require standardized tests, such as the GRE, as well as a minimum undergraduate GPA. You also may need work experience, a statement of academic purpose, and letters of recommendation.
Read Our Guide to Graduate Admissions
What Can I Do With a Master's in Data Science?
Professionals with a master's degree in data science work in many different fields. These include sectors like computer systems design, healthcare, and government work. Job titles include data analyst, data scientist, data management and biostatistics scientist, machine learning engineer, and clinical data scientist.
Popular Career Paths
- Data Science
- Data Analyst: A data analyst gathers and organizes data for an organization. They then use this data to reach meaningful conclusions and help inform decisions within an organization.
- Machine Learning Engineer: A machine learning engineer designs and builds artificial intelligence systems for various applications.
Popular Continuing Education Paths
- Ph.D. in Data Science: A Ph.D. in data science provides rigorous training to help students become data science researchers. Students can learn to gather fundamental research insights and translate them into data science best practices.
- Doctorate in Data Analytics: A doctorate in data analytics can cover qualitative and quantitative research as used in data analytics. Students also can learn about applying different research techniques in management and marketing.
- Ph.D. in Big Data Analytics: A Ph.D. program in big data analytics can train students to become researchers. Students learn how to analyze structured and unstructured data to discover patterns and other useful information.
How Much Money Can I Make With a Master's in Data Science?
PayScale reports the average annual salary for a junior data scientist is around $67,030, as of September 2021. As you move up the career ladder, your salary will likely increase. For example, the average yearly salary for a data scientist is around $96,480, while a lead data scientist earns an average wage of around $134,100 per year.
Frequently Asked Questions About Master's in Data Science Programs
What is data science?
Data science is an in-demand career path for skilled professionals who know how to analyze data to find meaningful patterns. These professionals can reach conclusions about what course of action a business or organization should take based on data. Data scientists may capture, maintain, process, analyze, and share their findings about data. Data scientists must be skilled at analyzing data, data mining, and programming. They collect data from many different sources, identify relevant questions, organize the data, and inform business executives of their findings.
Is a master’s in data science worth it?
Since many jobs in data science require a minimum of a master's, it can be worthwhile to get this degree. Without a master's degree, you could end up working as a market research analyst, data engineer, or other entry-level position. Also, according to PayScale, the average salary for a junior data scientist is about half that of a lead data scientist. Earning a graduate degree can help you climb the career ladder and boost your earning potential.
How much does it cost to get a master's in data science?
As with any degree, the cost of a master's in data science varies depending on the school. A public university typically charges much less than a private university. According to the National Center for Education Statistics, the average annual cost of graduate tuition and fees at a public college or university was $12,171 during the 2018-2019 academic year. At a private nonprofit institution, the average cost was $27,776. By shopping around, you may find that it's cheaper to get an online master's degree in data science from a school farther away from your location. Don't just consider online programs from schools close by. However, make sure to ask about in-person or on-campus requirements. Some online programs may feature occasional hands-on requirements.
How long does it take to get a master’s in data science?
Although the average length of time to complete a master's degree is 1.5-2 years, it can take more or less time. For example, if you are in an accelerated program, you may be able to finish within a year. On the other hand, if you attend school part time while earning your master's degree, it could take significantly longer. If you're enrolling part time, check with your school to find out if there is a time limit. You may have to complete all of your courses within 4-6 years in order to graduate.
What are the highest-paying jobs in data science?
Lead data scientist is one of the highest-paying jobs in data science, with an average annual salary of around $134,100 as of September 2021, according to PayScale. Your specific skills can also affect your salary — professionals who pursue industry certifications can set themselves apart from their peers and increase their income. Additionally, the BLS reports that computer and information research scientists earned a median salary of $126,830 per year, as of May 2020. Those who worked for software publishers typically earned the most, with a median annual salary of $145,920. People who worked in research and development in the sciences earned a median salary of around $140,000, while those in computer systems design earned a median salary of around $136,950.
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